Plot.

American CIA agent Ethan Renner (Kevin Costner) works with a team to capture the Albino, the lieutenant to an arms trafficker (called the Wolf) who is selling a dirty bomb to terrorists in a hotel in Belgrade. The Albino deduces the trap when he recognizes one of Renner's fellow agents (disguised as a chambermaid), whom he kills. Renner, suddenly dizzy as he pursues The Albino, only manages to cripple him by shooting him in the leg, then blacks out, allowing the Albino to escape. Meanwhile, elite CIA assassin Vivi Delay (Amber Heard), a "Top Shelf agent", has been personally assigned by the Director to kill the Wolf. Vivi monitors the operation and suspects Renner has unknowingly seen the Wolf.  Renner is nearly disabled by an extreme cough, which is diagnosed as terminal brain cancer that has spread to his lungs. He is given only a few months to live, and will not see the next Christmas. For decades he has kept his dangerous career a carefully guarded secret from his wife Christine (Connie Nielsen) and daughter Zooey (Hailee Steinfeld), at the cost of losing them. He decides to spend his remaining time trying to fix his relationship with his estranged daughter, and if possible, his ex-wife. He returns to Paris, where he and his family live separately, to find the Réunion family of Jules is squatting in his apartment. He is told by the police that he is not permitted to evict indigent squatters until after the winter. He makes an awkward reconnection with Christine, and tells her of his terminal illness. She allows him to reconnect with Zooey, and when she has to go out of the country on business, she agrees to let him look after Zooey.  Vivi recruits him to find and kill the Wolf, in exchange for an experimental drug that could extend his life significantly. Renner reluctantly accepts, to get more time with his family. Vivi tells him the way to trap the Wolf is by getting the Albino, in turn by getting his accountant, in turn by kidnapping the gang's limousine driver. All the while Renner is fighting the hallucinogenic effect of the medicine, which occurs whenever his heart rate goes too high, and which he can only control by consuming alcohol. He must also deal with Zooey's school problems, including her habit of lying so she can sneak out partying. He manages to keep her out of trouble, and slowly reestablishes a father relationship with her, which impresses his wife.  He tracks the Wolf and the Albino into the subway, but they gain the upper hand when he is disabled by the hallucinations. The Albino attempts to kill him by pushing him in front of an oncoming train, but Renner manages to push the Albino on the track instead. The Wolf escapes, then contacts a business partner who can help him to flee the country.  The family is invited to a party thrown by Zooey's boyfriend's father, who happens to be the Wolf's business partner. Renner manages to protect Christine and Zooey, kill all the Wolf's men, and trap the Wolf in an elevator before breaking the cables, causing the cabin to free-fall to the ground. The Wolf survives, severely injured, but Renner is again disabled and, also feeling guilty for all the damage his work has done to his family, he's suddenly unable to pull the trigger, and drops his gun where the Wolf can get it. Vivi intervenes and kicks the gun back to Renner, telling him to finish the job and kill the Wolf, but he decides not to, because "I promised my wife I'd quit." Vivi then kills the Wolf.  At last retired, Renner survives to Christmas, which he is spending at a beach house with Zooey and Christine. He discovers a small, red wrapped gift package, which contains another vial of the cancer medicine. Vivi is seen on a hill behind the house smiling as Renner opens the package.

Production.

On 7 August 2012 "Deadline" reported that Kevin Costner had been offered the role of Ethan Renner, a government assassin in the McG-directed film. The film, set in France, was scripted by Luc Besson and Adi Hasak, with EuropaCorp having produced while Relativity Media has North American rights. On 2 October 2012, it was confirmed that actor Costner had closed the deal to star as lead in the film. On 29 November 2012, Hailee Steinfeld joined the cast of the film as the female lead, and the film began production in early 2013. On 13 December, Amber Heard also joined the cast. Later, on 7 January 2013, Connie Nielsen was added to the cast.

Filming.

On 7 January 2013, crews were filming scenes in Paris and Belgrade, and shooting wrapped in April. Some scenes in Paris were filmed in the studios and in the central nave of the Cité du Cinéma, founded by Luc Besson in Saint-Denis. Scenes in Belgrade were filmed in front of Hotel Jugoslavija.

Release.

On 31 January 2013, photos from the set of the film were released. In November 2013, stills from the film were released. On 17 December 2013, the studio released the first trailer and the poster for the film. On 30 January 2014, Relativity released a new Super Bowl 2014 spot.  On 28 May 2013, Relativity set a release date of 14 February 2014 for the film. Later, on 30 October, the film's date was shifted back a week, to 21 February 2014.

Critical response.

, the film holds a 28% approval rating on Rotten Tomatoes, based on 118 reviews with an average rating of 4.39/10. The site's consensus states: ""3 Days to Kill" uneasily mixes technically accomplished action sequences with an underdeveloped family conflict." On Metacritic, the film has a score of 40 out of 100, based on 30 critics, indicating "mixed or average reviews" from critics. Audiences surveyed by CinemaScore gave the film a grade B on scale of A to F.  Geoff Berkshire of "Variety magazine" wrote: "The lukewarm family dynamics sit awkwardly alongside equally underwhelming action sequences."  The Guardian's Mark Kermode, rated the film 1/5 stars describing it as "pitiful".

Box office.

"3 Days to Kill" grossed $12,242,218 in its opening weekend, finishing in second place behind "The Lego Movie" ($31.3 million). The film grossed a domestic total of $30,697,999 and an international total of $21,900,000, bringing its total gross to $52,597,999.

3 Days to Kill is a 2014 action thriller film directed by McG and written by Luc Besson and Adi Hasak. The film stars Kevin Costner, Amber Heard, Hailee Steinfeld, Connie Nielsen, Richard Sammel, and Eriq Ebouaney. The film was released on 21 February 2014. The film received mixed reviews and grossed $52.6 million against its $28 million budget.

是一部2014年美国和法国合拍的动作惊悚片,McG执导,卢·贝松与Adi Hasak编剧。凯文·科斯特纳、安柏·赫德、海莉·史坦菲德、Connie Nielsen、Richard Sammel和Eriq Ebouaney主演。美国于2014年2月21日上映。


Early life.

Tergesen was born in Ivoryton, Connecticut and graduated from Valley Regional High School in nearby Deep River. He moved to New York to try to become an actor. He graduated from the American Musical and Dramatic Academy (AMDA)'s two-year program in Manhattan, where, from 1986 to 1989, he worked at the Empire Diner. "I wasn't a great waiter. I was funny, but I gave a lot of attitude", he told "Rosie Magazine" in March 2002. He added: "The place is like a vortex for me". He met Tom Fontana, the future creator of "Oz", at the diner.  He did some stage work during this time. "I was doing plays all the time, but there's no money in it", Tergesen said in a 1995 "Los Angeles Times" article. "After graduation, I thought I'd be making a living at it." He went to Los Angeles to help Fontana move into his house. While dining at a restaurant on the day after arriving in Los Angeles, California, a casting director and friend of Fontana's asked Lee if he was an actor. "He told me there was a part in this movie and at that time, I couldn't imagine what it could be," Tergesen told the "L.A. Times". The movie ended up being "Point Break", starring Keanu Reeves and Patrick Swayze. "It was the beginning of me never having to do anything else but act", he told LT.com in 1990.

Career.

Tergesen began his career with a bit role in the 1991 prime-time aired pilot, "Acting Sheriff".  Tergesen then appeared in "Wayne's World" (1992) and "Wayne's World 2" (1993), in the 1994 series "Weird Science" as Chester "Chett" Donnelly, and in a recurring role in the first season of "".  He portrayed Tobias Beecher on HBO's "Oz" (1997), a critically acclaimed role he played until the series ended in 2003. His film credits include "Point Break", "Shaft", "Monster", "The Forgotten", "", "Cast A Deadly Spell" and "Wild Iris". He has made several appearances in TV shows such as "ER", "Rescue Me", "" episode 517, "Criminal Minds" episode 511, "The 4400", "House", "Law & Order" and two of its spin-offs, "" and "". He played a murderous Navy SEAL, Larrick, in Season 2 of The Americans on the FX television network.  He played an Alcoholics Anonymous sponsor in the second season of "Desperate Housewives", becoming romantically involved with Bree Van de Kamp. He appeared in the USA Network series "Royal Pains" and in Seasons 4–5 of the Lifetime Network series "Army Wives". He played Evan Wright in the HBO mini-series "Generation Kill".  He has appeared on stage in the Off-Broadway productions of "Long Lost" (2019) by Donald Margulies and "Rapture, Blister, Burn" in 2012 at Playwrights Horizons, among others.

Personal life.

He married Yuko Otomo in 2011; their daughter Lily was born December 5, 2012. He is the brother-in-law of actress Toni Lewis by her marriage to Lee's brother, Chris (who served as the music supervisor on "Oz").

Filmography.

Film.

Sources: tcm.com allmovie.com

Television.

Source: allmovie.com

Lee Allen Tergesen (; born July 8, 1965) is an American actor. He is known for his roles in "Weird Science", as Tobias Beecher in HBO's prison drama "Oz" and as Evan Wright in "Generation Kill," as well as guest starring in many other series.


Early life and education.

Kern was raised in Simmering, a working-class district of Vienna, as the son of an electrician and a secretary. He studied journalism and communication at the University of Vienna followed by postgraduate education at the Management Zentrum St. Gallen.

Career.

Kern started his career in 1989 as a business journalist writing for the "Wirtschaftspressedienst" and Austrian business magazine "Option". In 1991, he became an assistant of the Federal Chancellery's undersecretary of state for civil service,. When Kostelka became chairman of the Social Democratic Party (SPÖ) parliamentary group in 1994, Kern remained his chief of office and spokesman.  In 1997, Kern moved to the largest Austrian electricity supplier, the Verbund AG, where from 1999 he oversaw marketing and sales. In 2007 he was appointed a senior manager overseeing foreign mergers & acquisitions, investments, and the Austrian high-voltage transmission grid

CEO of the Austrian Federal Railways.

In 2010, Kern was selected to take over the post as CEO of the Austrian Federal Railways (ÖBB). He was appointed chairman of the Community of European Railway and Infrastructure Companies (CER) in 2014. Kern has been a board member of FK Austria Wien since 2009.  In 2012, ÖBB celebrated the 175th anniversary of the "Nordbahn", the earliest predecessor company marking the start of rail transport in Austria. Kern inaugurated an exhibition on the company's complicity with the Third Reich, named "The Suppressed Years – Railway and National Socialism in Austria 1938–1945". He referred to that period as "the darkest part of our company's history," adding that "We are obliged to commemorate and with this documentation we would like to further contribute to coming to terms with the past. No matter how incredible these events may seem to us today, we need to clearly accept these times as part of our ÖBB history." The exhibition later went on tour and was presented at the European Parliament's parliamentary building in Brussels. For his extraordinary engagement accounting for the company's past, in June 2013 the Vienna Israelite Community awarded Kern the Marietta and Friedrich Torberg Medal.  In the course of the 2015 migrant crisis, Kern organized the transport of hundred thousands of migrants coming from the "Balkan route" across the country. He is considered a supporter of German Chancellor Angela Merkel's migration policy. Leading Austrian trade unionist, who is also chairman of the ÖBB's works council described Kern in 2016 as "the first ÖBB boss to really stand by his workers."

Chancellor of Austria.

Since 2014, Kern was repeatedly named as one of the possible successors for Werner Faymann's chancellor post. In 2015, Austrian news magazine "profil" referred to him as the "Chancellor of hearts" and the Federal Railways he led as "the only state institution that flawlessly worked amidst the refugee crisis."  Half a year later, when on 9 May 2016 Chancellor Faymann resigned from all his posts, Kern was again named one of the candidates alongside Time Warner manager Gerhard Zeiler and former Siemens manager. On a 12 May party session, the Social Democrats agreed on nominating Kern for the country's highest executive office. He was announced to be appointed the new chancellor by 17 May, and to be nominated as party chairman at the upcoming party congress on 25 June. Kern was sworn into office on 17 May by outgoing President Heinz Fischer.  At his first press conference, Kern called for a change in the style of cooperation within the coalition government, warning the two parties risked otherwise "disappearing from the screen". He reaffirmed his position that in the refugee crisis, Austria was right not to "leave women and children standing in the rain," while ensuring order and security.  In spite of his credentials as a manager, Kern's nomination of members of the party's left wing, and Jörg Leichtfried as new ministers was interpreted as a turn towards the party's left. The appointment of Wehsely, who is known for her staunch pro-asylum course during the European migrant crisis, was however considered all too controversial, with political analyst Thomas Hofer referring to it as a declaration of war (""kleine Kampfansage"") against conservative coalition partner ÖVP. Wehsely ultimately declined and decided she would stay city councillor in Vienna. At the time, observers expected Kern to follow the centrist examples of German chancellor Gerhard Schröder or Britain's Tony Blair, combining pro-business policies with a social conscience.  Kern appointed Muna Duzdar, a lawyer and chairwoman of the Palestininian-Austrian Society, as state secretary in the Chancellery, where she will be the first Muslim to hold a government post. The fact that Duzdar, who has previously come out as a sharp critic of Israel, will now be in charge of Jewish community affairs, irritated the Jewish community. According to "Jerusalem Post" author Samuel Laster, Duzdar's appointment may however be considered a "signal of openness" for Kern who is "widely regarded as a friend of Israel."  Soon after taking office, Kern's government implemented several law-and-order measures, including a ban on Muslim face-covering veils and a tightening of immigration rules.  In June 2017, Kern criticized the draft of new U.S. sanctions against Russia that target EU–Russia energy projects, including Nord Stream 2 gas pipeline. In a joint statement Kern and (Germany)'s foreign minister Sigmar Gabriel said that "Europe's energy supply is a matter for Europe, and not for the United States of America."  In October 2017's snap general election, Kern's SPÖ got defeated by Sebastian Kurz' ÖVP. Kurz decided to form a coalition with the FPÖ. From December 2017 Kern was leader of the opposition.

Resignation from politics.

In September 2018, Kern announced that he would resign as national party leader and instead run as top candidate for the Austrian Social Democrats in the European Parliament elections. However, he announced to resign from politics completely in October 2018. His successor as party leader is Pamela Rendi-Wagner and Andreas Schieder was presented as top candidate in the EU Parliament elections.

Personal life.

In 1985 Kern married Karin Wessely, with whom he has three sons. In 2001, his marriage with Wessely, who is a local SPÖ politician in Mödling, a district capital south of Vienna, ended in divorce. Wessely however supported his nomination as successor to Faymann, and highly praised him as a charismatic personality, who is able to unite the more left-aligned and the more right-aligned factions of their party. With his second wife, Eveline Steinberger, he has a daughter.

Christian Kern ( ; born 4 January 1966) is an Austrian businessman and former politician who served as Chancellor of Austria from 17 May 2016 to 18 December 2017 and chairman of the Social Democratic Party from 25 June 2016 to 25 September 2018.

克里斯蒂安·科安(;1966年-1月4日),是一名奥地利政治人物及商人、社会民主党党员,曾担任奥地利联邦铁路总裁。2016年5月17日,他接替代总理莱因霍尔特·米特雷纳成为奥地利总理,至2017年提前大选后,因胜选的人民党决定不再与其筹组大联合政府而卸任。


Definition.

The name “convolutional neural network” indicates that the network employs a mathematical operation called convolution. Convolution is a

Architecture.

A convolutional neural network consists of an input and an output layer, as well as multiple hidden layers. The hidden layers of a CNN typically consist of a series of convolutional layers that "convolve" with a multiplication or other dot product. The activation function is commonly a RELU layer, and is subsequently followed by additional convolutions such as pooling layers, fully connected layers and normalization layers, referred to as hidden layers because their inputs and outputs are masked by the activation function and final convolution.  Though the layers are colloquially referred to as convolutions, this is only by convention. Mathematically, it is technically a "sliding dot product" or cross-correlation. This has significance for the indices in the matrix, in that it affects how weight is determined at a specific index point.

Convolutional.

When programming a CNN, the input is a tensor with shape (number of images) x (image height) x (image width) x (image depth). Then after passing through a convolutional layer, the image becomes abstracted to a feature map, with shape (number of images) x (feature map height) x (feature map width) x (feature map channels). A convolutional layer within a neural network should have the following attributes:  Convolutional layers convolve the input and pass its result to the next layer. This is similar to the response of a neuron in the visual cortex to a specific stimulus. Each convolutional neuron processes data only for its receptive

Pooling.

Convolutional networks may include local or global pooling layers to streamline the underlying computation. Pooling layers reduce the dimensions of the data by combining the outputs of neuron clusters at one layer into a single neuron in the next layer. Local pooling

Fully connected.

Fully connected layers connect every neuron in one layer to every neuron in another layer. It is

Receptive field.

In neural networks, each neuron receives input from some number of locations in the previous layer. In a fully connected layer, each neuron receives input from "every" element of the previous layer. In a convolutional layer, neurons receive input from only a restricted subarea of the previous layer. Typically the subarea is of a square shape (e.g., size 5

Weights.

Each neuron in a neural network computes an output value by applying a specific function to the input values coming from the receptive field in the previous layer. The function that is applied to the input values is determined by a vector of weights and a bias (typically real numbers). Learning, in a neural network,

History.

CNN design follows vision processing in living organisms.

Receptive fields in the visual cortex.

Work by Hubel and Wiesel in the 1950s and 1960s showed that cat and monkey visual cortexes contain neurons that individually respond to small regions of the visual field. Provided the eyes are not moving, the region of visual space within which visual stimuli affect the firing of a single neuron

Neocognitron, origin of the CNN architecture.

The "neocognitron" was introduced by Kunihiko Fukushima in 1980. It was inspired by the above-mentioned work of Hubel and Wiesel. The neocognitron introduced the two basic types of layers in CNNs: convolutional layers, and downsampling layers. A convolutional layer contains units whose receptive fields cover a patch of the previous layer. The weight vector (the set of adaptive parameters) of such a unit is often called a filter. Units can share filters. Downsampling layers contain units whose receptive fields cover patches of previous convolutional layers. Such a unit typically

Time delay neural networks.

The time delay neural network (TDNN) was introduced in 1987 by Alex Waibel et al. and was the first convolutional network, as it achieved shift invariance. It did so by utilizing weight sharing in combination with Backpropagation training. Thus, while also using a pyramidal structure as in the neocognitron, it performed a global optimization of the weights instead of a local one.  TDNNs are convolutional networks that share weights along the temporal dimension. They allow speech signals to be processed time-invariantly. In 1990 Hampshire and Waibel introduced a variant which performs a two dimensional convolution. Since these TDNNs operated on spectrograms, the resulting phoneme recognition system was invariant to both shifts in time and in frequency. This inspired translation invariance in image processing with CNNs. The tiling of neuron outputs can cover timed stages.  TDNNs now achieve the best performance in far distance speech recognition.

Max pooling.

In 1990 Yamaguchi et al. introduced the concept of max pooling. They did so by combining TDNNs with max pooling in order to realize a speaker independent isolated word recognition system. In their system they used several TDNNs per word, one for each syllable. The results of each TDNN over the input signal were combined using max pooling and the outputs of the pooling layers were then passed on to networks performing the actual word classification.

Image recognition with CNNs trained by gradient descent.

A system to recognize hand-written ZIP Code numbers involved convolutions in which the kernel coefficients had been laboriously hand designed.  Yann LeCun et al. (1989) used back-propagation to learn the convolution kernel coefficients directly from images of hand-written numbers. Learning was thus fully automatic, performed better than manual coefficient design, and was suited to a broader range of image recognition problems and image types.  This approach became a foundation of modern computer vision.

LeNet-5.

LeNet-5, a pioneering 7-level convolutional network by LeCun et al. in 1998, that classifies digits, was applied by several banks to recognize hand-written numbers on checks () digitized in 32x32 pixel images. The ability to process higher resolution images requires larger and more layers of convolutional neural networks, so this technique is constrained by the availability of computing resources.

Shift-invariant neural network.

Similarly, a shift invariant neural network was proposed by W. Zhang et al. for image character recognition in 1988. The architecture and training algorithm were modified in 1991 and

Neural abstraction pyramid.

The feed-forward architecture of convolutional neural networks was extended in the neural abstraction pyramid by lateral and feedback connections. The resulting recurrent convolutional network

GPU implementations.

Although CNNs were invented in the 1980s, their breakthrough in the 2000s required fast implementations on graphics processing units (GPUs).  In 2004, it was shown by K. S. Oh and K. Jung that standard neural networks can be greatly accelerated on GPUs. Their implementation was 20 times faster than an equivalent implementation on CPU. In 2005, another paper also emphasised the value of GPGPU for machine learning.  The first GPU-implementation of a CNN was described in 2006 by K. Chellapilla et al. Their implementation was 4 times faster than an equivalent implementation on CPU. Subsequent work also used GPUs, initially for other types of neural networks (different from CNNs), especially unsupervised neural networks.  In 2010,

Intel Xeon Phi implementations.

Compared to the training of CNNs using GPUs, not much attention was given to the Intel Xeon Phi coprocessor. A notable development is a

Distinguishing features.

In the past, traditional multilayer perceptron (MLP) models have been used for image recognition. However, due to the full connectivity between nodes, they suffered from the curse of dimensionality, and did not scale well with higher resolution images. A 1000×1000-pixel image with RGB color channels has 3 million weights, which is too high to feasibly process efficiently at scale with full connectivity.  For example, in CIFAR-10, images are only of size 32×32×3 (32 wide, 32 high, 3 color channels), so a single fully connected neuron in a first hidden layer of a regular neural network would have 32*32*3 = 3,072 weights. A 200×200 image, however, would lead to neurons

Building blocks.

A CNN architecture is formed by a stack of distinct layers that transform the input volume into an output volume (e.g. holding the class scores) through a differentiable function. A few distinct types of layers are commonly used. These are further discussed below.

Convolutional layer.

The convolutional layer is the core building block of a CNN. The layer's parameters consist of a set of learnable filters (or kernels), which have a small receptive field, but extend through the full depth of the input volume. During the forward pass, each filter is convolved across the width and height of the input volume, computing the dot product between the entries of the filter and the input and producing a 2-dimensional activation map of that filter. As a result, the network learns filters that activate when it detects some specific type of feature at some spatial position in the input.  Stacking the activation maps for all filters along the depth dimension forms the full output volume of the convolution layer. Every entry in the output volume can thus also be interpreted as an output of a neuron that looks at a small region in the input and shares parameters with neurons in the same activation map.

Local connectivity.

When dealing with high-dimensional inputs such as images, it is impractical to connect neurons to all neurons in the previous volume because such a network architecture does not take the spatial structure of the data into account. Convolutional networks exploit spatially local correlation by enforcing a sparse local connectivity

Spatial arrangement.

Three hyperparameters control the size of the output volume of the convolutional layer: the depth, stride and zero-padding.  The spatial size of the output volume can be computed as a function of the input volume size formula_3, the kernel field size of the convolutional layer neurons formula_4, the stride with which they are applied formula_5, and the amount of zero padding formula_6 used on the border. The

Parameter sharing.

A parameter sharing scheme is used in convolutional layers to control the number of free parameters. It relies on the assumption that if a patch feature is useful to compute at some spatial position, then it should also be useful to compute at other positions. Denoting a single 2-dimensional slice of depth as a "depth slice", the neurons in each depth slice are constrained to use the same weights and bias.  Since all neurons in a single depth slice share the same parameters, the forward pass in each depth slice of the convolutional layer can be computed as a convolution of the neuron's weights with the input

Pooling layer.

Another important concept of CNNs is pooling, which is a form of non-linear down-sampling. There are several non-linear functions to implement pooling among which "max pooling" is the most common. It partitions the input image into a set of non-overlapping rectangles and, for each such sub-region, outputs the maximum.  Intuitively, the exact location of a feature is less important than its rough location relative to other features. This is the idea behind the use of pooling in convolutional neural networks. The pooling layer serves to progressively reduce the spatial size of the representation, to reduce the number of parameters, memory footprint and amount of computation in the network, and hence to also control overfitting. It is common to periodically insert a pooling layer between successive convolutional layers in a

ReLU layer.

ReLU is the abbreviation of rectified linear unit, which applies the non-saturating activation function formula_11. It effectively removes negative values from an activation map by setting them to zero. It increases the nonlinear properties of the decision function and

Fully connected layer.

Finally, after several convolutional and max pooling layers, the high-level reasoning in the neural network is done via fully connected layers. Neurons in a fully connected layer have

Loss layer.

The "loss layer" specifies how training penalizes the deviation between the predicted (output) and true labels and is normally the final layer of a neural network. Various loss functions appropriate

Choosing hyperparameters.

CNNs use more hyperparameters than a standard multilayer perceptron (MLP). While the usual rules for learning rates and regularization constants still apply, the following should be kept in mind when optimizing.

Number of filters.

Since feature map size decreases with depth, layers near the input layer will tend to have fewer filters while higher layers can have more. To equalize computation at each layer, the product of feature values "v" with pixel

Filter shape.

Common filter shapes found in the literature vary greatly, and are usually chosen based on the dataset.

Max pooling shape.

Typical values are 2×2. Very large input volumes may warrant 4×4 pooling in the lower layers.

Regularization methods.

Regularization is a process of introducing additional information to solve an ill-posed problem or to prevent overfitting. CNNs use various types of regularization.

Empirical.

Dropout.

Because a fully connected layer occupies most of the parameters, it is prone to overfitting. One method to reduce overfitting is dropout. At each training stage, individual nodes are either "dropped out" of the net with probability formula_17 or kept with probability formula_18, so that a reduced network is left; incoming and outgoing edges to a dropped-out node are also removed. Only the reduced network is trained on the data in that stage. The removed nodes are then reinserted into the network with their original weights.  In the training stages, the probability that a hidden node will be dropped is usually 0.5; for input nodes, however, this probability is typically much

DropConnect.

DropConnect is the generalization of dropout in which each connection, rather than each output unit, can be dropped with probability formula_17. Each unit thus receives input from a random subset of units in the previous layer.  DropConnect

Stochastic pooling.

A major drawback to Dropout is that it does not have the same benefits for convolutional layers, where the neurons are not fully connected.  In stochastic pooling, the conventional deterministic pooling operations are replaced with a stochastic procedure, where the activation within each pooling region is picked randomly according to a multinomial distribution, given by the activities within the pooling region.

Artificial data.

Since the degree of model overfitting is determined by both its power and the amount of training it receives, providing a convolutional network with more training examples can reduce overfitting. Since these networks are usually trained with all available data, one approach is to either generate new data from scratch (if possible) or perturb existing data to create new ones. For example, input images could be asymmetrically cropped by a few percent to create new examples with the same label as the original.

Explicit.

Early stopping.

One of the simplest methods to prevent overfitting of a network is to simply

Number of parameters.

Another simple way to prevent overfitting is to limit the number of parameters, typically by limiting the number of hidden units in each layer or limiting network depth. For convolutional networks, the

Weight decay.

A simple form of added regularizer is weight decay, which simply adds an additional error, proportional to the sum of weights (L1 norm) or squared magnitude (L2 norm) of the weight vector, to the error at each node. The level of acceptable model complexity can be reduced by increasing the proportionality constant, thus increasing the penalty for large weight vectors.  L2 regularization is the most common form of regularization. It can be implemented by penalizing the squared magnitude

Max norm constraints.

Another form of regularization is to enforce an absolute upper bound on the magnitude of the weight vector for every neuron and use projected gradient descent to enforce the constraint. In practice, this corresponds to performing the parameter update as normal, and then enforcing the constraint by clamping the weight vector formula_24 of every neuron to satisfy formula_25. Typical values of formula_26 are order of 3–4. Some papers report improvements when using this form of regularization.

Hierarchical coordinate frames.

Pooling loses the precise spatial relationships between high-level parts (such as nose and mouth in a face image). These relationships are needed for identity recognition. Overlapping the pools so that each feature occurs in multiple pools, helps retain the information. Translation alone cannot extrapolate the understanding of geometric relationships to a radically new viewpoint, such as a different orientation or scale. On the other hand, people are very good at extrapolating; after seeing a new shape once they can recognize it from a different viewpoint.  Currently, the common way to deal with this problem is to train the network on transformed data in different orientations, scales, lighting, etc. so that the network can cope with these variations. This is computationally intensive for large

Applications.

Image recognition.

CNNs are often used in image recognition systems. In 2012 an error rate of 0.23 percent on the MNIST database was reported. Another paper on using CNN for image classification reported that the learning process was "surprisingly fast"; in the same paper, the best published results as of 2011 were achieved in the MNIST database and the NORB database. Subsequently, a similar CNN called AlexNet won the ImageNet Large Scale Visual Recognition Challenge 2012.  When applied to facial recognition, CNNs achieved a large decrease in error rate. Another paper reported a 97.6 percent recognition rate on "5,600 still images of more than 10 subjects". CNNs were used to assess video quality in an objective way after manual training; the resulting system had a very low root mean square error.  The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object classification and detection, with millions of images and hundreds of object classes.

Video analysis.

Compared to image data domains, there is relatively little work on applying CNNs to video classification. Video is more complex than images since it has another (temporal) dimension. However, some extensions of CNNs into the video domain have been explored. One approach is to treat space and time as equivalent

Natural language processing.

CNNs have also been explored for natural language processing. CNN models are effective for

Anomaly Detection.

A CNN with 1-D convolutions was used on time series in

Drug discovery.

CNNs have been used in drug discovery. Predicting the interaction between molecules and biological proteins can identify potential treatments. In 2015, Atomwise introduced AtomNet, the first deep learning neural network for structure-based rational drug design. The system trains directly

Health risk assessment and biomarkers of aging discovery.

CNNs can be naturally tailored to analyze a sufficiently large collection of time series data representing one-week-long human physical activity streams augmented by the rich clinical

Checkers game.

CNNs have been used in the game of checkers. From 1999 to 2001, Fogel and Chellapilla published papers showing how a convolutional neural network could learn to play checker using co-evolution. The learning process did not use prior human professional games,

Go.

CNNs have been used in computer Go. In December 2014, Clark and Storkey published a paper showing that a CNN trained by supervised learning from a database of human professional games could outperform GNU Go and win some games against Monte Carlo tree search Fuego 1.1 in a fraction of the time it took Fuego to play. Later it was announced that a large 12-layer convolutional neural network

Time series forecasting.

Recurrent neural networks are generally considered the best neural network architectures for time series forecasting (and sequence modeling in general), but recent studies show that convolutional networks can perform comparably or even better. Dilated convolutions might enable one-dimensional convolutional neural networks

Fine-tuning.

For many applications, the training data is less available. Convolutional neural networks usually require a large amount of training data in order to avoid overfitting. A common technique is to train

Human interpretable explanations.

End-to-end training and prediction are common practice in computer vision. However, human interpretable explanations are required for critical systems

Related architectures.

Deep Q-networks.

A deep Q-network (DQN) is a type of deep learning model that combines a deep neural network with Q-learning, a form of reinforcement learning. Unlike earlier reinforcement learning

Deep belief networks.

Convolutional deep belief networks (CDBN) have structure very similar to convolutional neural networks and are trained similarly to deep belief networks. Therefore, they exploit the 2D structure of

In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics. They have applications in image and video recognition, recommender systems, image classification, medical image analysis, natural language processing, and financial time series.

卷积神经网络(Convolutional Neural Network, CNN)是一种前馈神经网络,它的人工神经元可以响应一部分覆盖范围内的周围单元,对于大型图像处理有出色表现。


Gameplay.

"Assassin's Creed Rogue" is an action-adventure, stealth game set in an open world environment and played from a third-person perspective. Naval aspects from previous games return with the player controlling Shay's ship, the "Morrigan". The "Morrigan" has a shallower draft compared to Edward Kenway's "Jackdaw" from "", allowing for river travel. New features include new ship-based weapons such as releasing an oil slick which can then be ignited, Puckle guns, and the ability for enemies to board the "Morrigan" during ship-to-ship combat. The arctic environment also adds features to naval gameplay and exploration, as certain icebergs can be rammed with an icebreaker. However, the underwater diving missions featured in "Black Flag" do not exist as swimming in the North Atlantic causes the player's health to rapidly deplete due to the frigid water, though Shay is able to swim in the more southerly waters of the 'river valley' area.  For combat, the game introduces an air rifle, similar to the blowpipe from "Black Flag", which allows the player to silently take out enemies at a distance. The air rifle can be outfitted with a variety of different projectiles, such as firecrackers. The player can also use it as a grenade launcher, which fires off shrapnel grenades and other loads. Hand-to-hand combat has been slightly altered, and now enemy attacks can be countered with timing, similar to the "" series of games. Enemy Assassins feature archetypes similar to previous games, using skills that players have been using throughout the series; they can hide in bushes, blend in with crowds, and perform air assassinations against the player. Poison gas can now be used as an environmental weapon, and Shay has a mask that can mitigate its effects, and Shay's Eagle Vision also takes elements from the multiplayer feature of the previous games that allowed them to track an enemy's position via a radar-like system, and even without it, Shay is able to detect enemy presence with the screen edges going red and is also able to accurately foresee when and from where his enemies will strike.  Side missions and activities return, with a number of them based on those of the previous games. Reflecting Shay's role as a Templar, the game introduces a new side mission: Assassin Interception. These mirror the Assassination side missions in previous games, in that Shay, after intercepting a messenger pigeon carrying an assassination contract, must prevent an innocent being assassinated by finding and killing Assassins hidden nearby.  Legendary ship battles make a return as well. In order to overcome these challenges, the player may acquire possible upgrades for the "Morrigan" as the game progresses.

Plot.

Shay Patrick Cormac (Steven Piovesan) is a new recruit to the Colonial Brotherhood of Assassins whose potential is offset by his insubordination. While training with the North Atlantic chapter under the Assassin Mentor Achilles Davenport (Roger Aaron Brown), he encounters the Assassin Adéwalé (Tristan D. Lalla), who brings news that the Haitian city of Port-au-Prince has been devastated by an earthquake during the search for a Precursor temple containing a Piece of Eden. With his experience captaining ships—including the recently acquired "Morrigan"—Cormac is enlisted in an investigation into Templar interests in a Precursor artifact and manuscript that are linked to the temple.  Posing as a Templar courier, he delivers the artifact and manuscript to Benjamin Franklin (Rick Jones) whose experiments with electricity on the artifact generate a map showing the location of Precursor temples around the world. He identifies one in Lisbon, and Cormac is dispatched by Davenport to retrieve the Piece of Eden it contains. However, Cormac has begun questioning the Assassins' motives after seeing their refusal to engage in dialogue with the Templars, and being asked to kill an already-dying Templar commander, Lawrence Washington. His convictions are destroyed in Lisbon when his efforts to retrieve the Piece of Eden trigger a massive earthquake, destroying the city. Correctly deducing that a similar sequence of events destroyed Port-au-Prince, Cormac is horrified to learn that Davenport intends to continue the search for Pieces of Eden despite the danger. Shay then steals the manuscript and attempts to escape the Homestead, but is cornered by members of the Brotherhood. Intending to commit suicide by jumping off the Homestead's cliff, Shay is shot and left for dead before he can destroy the Manuscript.  Now that he is cast adrift, he finds himself in New York City, acting as a vigilante to clean out gangs that are extorting the citizenry. His efforts attract the attention of Colonel George Monro (Graham J. Cuthbertson), who convinces Cormac that he can improve the lives of others. Meeting with Christopher Gist, Shay retakes his ship the "Morrigan" from the Assassins, and agrees to assist his new allies, despite learning that they are Templars. He is disturbed to learn that the Assassins have not given up their search for the Pieces of Eden, and believing them to be a threat, commits himself to the Templar Order to hunt them down. After killing Colonial Brotherhood Assassins Le Chasseur (Chimwemwe Miller) and Kesegowaase (Danny Blanco-Hall), Shay becomes a fully fledged member of the Order when he is inducted by Grand Master Haytham Kenway (Adrian Hough). Shay then eliminates Adéwalé, Hope Jensen (Patricia Summersett) and Louis-Joseph Gaultier de La Vérendrye (Marcel Jeannin). Finally, only two senior members of the Assassins in America remain alive: Achilles himself, and his second-in-command Liam O'Brien (Julian Casey), once Shay's best friend. Shay pursues the two to the Arctic with Haytham, where the Assassins have located another Temple. Having dispatched most of the Assassin expedition, Shay and Haytham enter the Temple to find Liam and Achilles, who have realized that the artifact is as Shay said—a means to stabilize the world, not a weapon to control it. However, Achilles' attempt to stop Liam taking vengeance causes the artifact to be destroyed, and a third earthquake is triggered. As the four try to escape, Shay and Liam duel on the ice and Liam is fatally injured when the two fall. Shay then makes his way back to his ship and arrives as Haytham overpowers Achilles. Shay persuades Haytham to spare Achilles, out of mercy and to ensure knowledge of the Temples will not be lost, so the Assassins will not try to pursue them again. Haytham then cripples the last American Assassin with his pistol.  As the Templars set sail for home, Haytham tasks Shay with retrieving the Precursor artifact. Shay spends much of his later life hunting it down, even as Templar influence in the colonies was eventually destroyed by Connor during the American Revolution. The final memories gradually reveal a mission to escort Benjamin Franklin through Paris, in search of the Precursor box that is in the possession of Charles Dorian, a French Assassin and the father of Arno Dorian, the protagonist of "Assassin's Creed Unity". Cormac murders Dorian and takes possession of the box as he becomes a senior member of the Templar Order.  In the present day, the player is researching Cormac's memories for Abstergo Entertainment when they inadvertently trip a hidden memory file that infects the Animus servers. Abstergo is placed in lockdown, and the player must clean the Animus servers out by living Cormac's later memories. Juhani Otso Berg, a senior member of the Templars, later orders the player to upload Shay's memories to the Assassin servers in order to weaken their resolve by showing how close Achilles Davenport came to destroying the world and the Assassins betraying Shay. The Brotherhood responds by cutting off their communications. In a mid-credits scene, Berg thanks the player for their help, and gives them a choice: join the Templar order, or die. The scene fades to black before the player's decision is shown.

Development.

By March 2014, an "Assassin's Creed" game code-named "Comet" was revealed to be in development, set for release on PlayStation 3 and Xbox 360. By the end of the month, additional reports indicated that "Comet" would be set around 1758 in New York, as well as feature sailing on the Atlantic Ocean. The game would be a direct sequel to "Assassin's Creed IV: Black Flag", and would feature a Templar named Shay as the main protagonist. Haytham Kenway from "Assassin's Creed III" and Adéwalé from "Black Flag" would also make appearances.  The game was officially announced on August 5, 2014, following a leak of the title. Game director Martin Capel described the game as finishing the series' "North American saga" and that the game was designed to accommodate specific fan requests, such as taking on the role of a Templar. The game is intended to "fill the gaps" of the story between "Assassin's Creed III" and "Assassin's Creed IV: Black Flag" and has "a crucial link" to the events of the previous games. In addition to Ubisoft Sofia's work on the game, contributions are also being made by Ubisoft studios in Singapore, Montreal, Quebec, Chengdu, Milan and Bucharest. Ubisoft also stated that the game was being envisioned without multiplayer components "at this stage", but did not rule out any modes being added after the game launched. On 20 March 2018, a remaster of the game was released for PlayStation 4 and Xbox One.

Reception.

"Assassin's Creed Rogue" received "mixed or average" reviews from critics, according to review aggregator Metacritic.  Ray Carsillo from Electronic Gaming Monthly gave the game an 8.5/10, praising its interesting lead character, enjoyable story, new weapons introduced, new mission design, which requires players to prevent assassinations instead of carrying out assassinations like in other "Assassin's Creed" titles, as well as advanced and improved combat mechanics. However, he criticized poor pacing of the story, frequent bugs, lack of replayability and the lack of inclusion of a multiplayer mode. He concluded the review by saying that ""Rogue" is a far more pleasurable experience than I anticipated. It does just enough to put its own stamp on the franchise while also giving us critical story details in order to tie up loose ends between "Assassin's Creed III" and "". It serves as a perfect conclusion to the series' time spent exploring Europe's North American colonies in the 18th century."  "Eurogamer" drew comparisons between "Rogue" and "Assassin's Creed Revelations"—a game which served to resolve storylines from Ezio Auditore's saga as a lead-in to "Assassin's Creed III", due to its focus on expanding on characters and storylines introduced in "III" and "Black Flag". Although noting that some settings, weapons, and mechanics had been reused from previous games in the series (such as an expansion of the New York City setting from "III", naval combat, renovating buildings to build income, and locating enemies with a radar similar to the former multiplayer mode), the use of Assassins as an enemy was considered to be a "much-needed new [idea] to the series' fighting mechanics" due to their use of tactics that were used by the player themselves in previous games (such as smoke bombs and hiding), and that "Rogue" felt the most "fresh" whilst exploring its new North Atlantic overworld. However, the story missions themselves and single player campaign overall were criticized for being noticeably shorter than in previous games.  Matt Miller from Game Informer gave the game an 8.25/10. He praised the huge variety of activities, varied environments, and mission types, new additions and well-performed gameplay, despite being too similar to its predecessors. He criticized the repetitive melee combat and the absence of multiplayer mode. He described the game by saying that ""Rogue" is vast with lots to explore, and while it lacks novelty, it offers a wealth of gameplay and lore to faithful fans." Daniel Bloodworth from GameTrailers gave the game a 7.2/10, praising the return of some old characters in the "Assassin's Creed" series, stunning scenery and environment, interesting interceptions missions, but criticizing the predictable and dull lead character, poorly-constructed missions in the beginning of the game, disappointing boss battles, as well as numerous bugs. He described the game by saying that ""Rogue" in many ways feels like an extension of last year's "Black Flag", even down to the menus, but there are some tweaks to the formula thanks to your new role as a former assassin, hunting down his old comrades."  Daniel Krupa from IGN gave the game a 6.8/10. He praised the engaging story, the nuanced lead character, atmospheric scenery, but criticized the lack of Templar abilities included, bland encounters with other main characters, uninspired side quests, empty world, as well as the frustrating combat and traversal system, which he stated has shown no improvements. He also criticized the game for not encouraging the player to explore the world. Mark Walton from GameSpot gave the game a 6/10, criticizing the predictable story, unlikeable lead character, lack of interesting missions, as well as being thin on core content. He stated that the game feels like a glorified "Black Flag" DLC pack and has done nothing to put the franchise forward. Xav de Matos from Joystiq gave the game a 6/10, criticizing the game for not adding anything new to the franchise. He stated that ""Assassin's Creed Rogue" is essentially a clone of "Black Flag"s setting and systems. If you can accept rampant copy-and-paste in another full priced entry, you'll more than likely enjoy what "Assassin's Creed Rogue" has to offer."  In December 2015, "Game Informer" ranked the game as the sixth best game in the "Assassin's Creed" series to date.

Sales.

As of December 31, 2014, Ubisoft had shipped a combined 10 million copies of "Assassin's Creed Unity" and "Assassin's Creed Rogue".

Assassin's Creed Rogue is an action-adventure video game developed by Ubisoft Sofia and published by Ubisoft. It is the seventh major installment in the "Assassin's Creed" series, and is set between 2013's "" and 2012's "Assassin's Creed III" with its final mission being the prologue to 2014's "Assassin's Creed Unity". It is the last of the "Assassin's Creed" games to be released for PlayStation 3 and Xbox 360. The game was first released for PlayStation 3 and Xbox 360 in November and December 2014 in North America, Oceania, Europe and Japan, and released for Microsoft Windows on March 10, 2015. A remastered version was released for PlayStation 4 and Xbox One on March 20, 2018. It was released on the Nintendo Switch as part of Assassin's Creed: The Rebel Collection alongside "Assassin's Creed IV: Black Flag" on December 6, 2019.

是一款由育碧索菲亚开发、育碧软件发行的虚构历史动作冒险游戏。本作是刺客教条系列的第七部作品,亦是2013年《刺客教条IV:黑旗》的续作,内容将与《刺客教条III》和《刺客教条:大革命》有所关联,并首次以圣殿骑士的角度描绘《刺客教条》的世界观。游戏于2014年11月11日在PlayStation 3和Xbox 360上发行,2015年3月10日发行Windows版。2018年3月20日在PlayStation 4和Xbox ONE上发行重制版。2019年12月6日在任天堂Switch上与《刺客信条IV:黑旗》以捆绑的形式《刺客信条:逆命合集》(英语:Assassin's Creed: Rebel Collection)发售。


Early life.

Spykman was born on 13 October 1893 in Amsterdam. He attended Delft University and the University of Cairo.

Career.

He worked as a journalist in various parts of the world during much of the 1910s and also served as a diplomatic assistant for the Netherlands in Egypt and the Dutch East Indies.  He then came to the United States around 1920 to enter a doctoral program at the University of California, where he received a bachelor's degree in 1921, a master's degree in 1922, and a Ph.D. in 1923. The subject of his dissertation, which he subsequently revised for publication, was Georg Simmel. He then was an instructor in political science and sociology there from 1923 to 1925. He became a naturalized citizen of the United States in 1928.

Professor.

In 1925, he came to Yale University, where he was an assistant professor of international relations. He became a full professor in 1928 and the chair of the university's department of international relations in 1935. Also in 1935, he was a co-founder of the Yale Institute of International Studies and was its first director. He held that position until 1940, when he became ill and relinquished it.

Author.

Spykman published two books on foreign policy.  "America's Strategy in World Politics" was published in 1942, soon after the entry of the United States into World War II. Concerned with the balance of power, he argued that isolationism, which relied on the oceans to protect the United States ("hemispheric" or "quarter defense"), was bound to fail. His object was to prevent another US retreat, as what occurred after World War I. Isaiah Bowman commented in 1942, "On grounds of merit and public value "America’s Strategy in World Politics" should be read in not less than a million American home. Every government official responsible for policy should read it once a year for the next twenty years—even if he may not agree with some of the remedies proposed."  "The Geography of the Peace" was published the year after Spykman's death. He explained his geostrategy and argued that the balance of power in Eurasia directly affected US security.  In his writings on geography and foreign policy, Spykman was somewhat of a geographical determinist. Since geography was "the most fundamentally conditioning factor because of its relative permanence," it is of primary relevance in analyzing a state's potential foreign policy.

Death.

He died of cancer on 26 June 1943, at the age of 49, in New Haven, Connecticut.

Personal life.

He was married to the children's novelist E. C. Spykman.

Geostrategic ideas.

He could be considered as a disciple and critic of both geostrategists Alfred Mahan, of the United States Navy, and Halford Mackinder, the British geographer. Spykman's work is based on assumptions similar to Mackinder, the unity of world politics and the unity of the world sea, but extends it to include the unity of the air. The exploration of the entire world means that the foreign policy of any nation will affect more than its immediate neighbors; it will affect the alignment of nations throughout the world's regions. Maritime mobility opened up the possibility of a new geopolitical structure: the overseas empire.  Spykman adopts Mackinder's divisions of the world but renames some:

Heartland.

At the same time, even if he gives credit to the strategic importance of maritime space like Mackinder, he does not see it as a region that will be unified by powerful transportation or communication infrastructure in the near future. As such, it will not be in a position to compete with US sea power. Spykman agrees that the Heartland offers a uniquely-defensive position, but that is all Spykman grants its occupier.  While the USSR encompassed a great expanse of land, its arable land remained in a small portion of its territory, mostly in the West. Indeed, the Soviet's raw materials were largely located to the West of the Ural Mountains as well. Since the political and material center of gravity was in the Western part of the USSR, Spykman sees little possibility of the Soviets exerting much power in Central Asia.  Still, the Soviet Union was to remain the greatest land power in Asia and could be a peacekeeper or a problem.

Rimland.

The Rimland (Mackinder's "Inner or Marginal Crescent") sections:  While Spykman accepts the first two as defined, he rejects the simple grouping the Asian countries into one "monsoon land." India, the Indian Ocean littoral, and the culture of India had a geography and civilization separate from the Chinese lands.  The Rimland's defining characteristic is that it is an intermediate region, lying between the Heartland and the marginal sea powers. As the amphibious buffer zone between the land powers and sea powers, it must defend itself from both sides, the cause of its fundamental security problems. Spykman's conception of the Rimland bears greater resemblance to Alfred Thayer Mahan's "debated and debatable zone" than to Mackinder's Inner or Marginal Crescent.  The Rimland has great importance because of its demographic weight, natural resources, and industrial development. Spykman sees that its importance to be the reason that the Rimland will be crucial to containing the Heartland, but Mackinder had believed that the Outer or Insular Crescent would be the most important factor in containing the Heartland.

Offshore continents.

There are two offshore continents flanking Eurasia: Africa and Australia. Spykman sees both continents' geopolitical status as determined respectively by the state of control over the Mediterranean Sea and the "Asiatic Mediterranean." Neither has ever been the seat of significant power; chaos prevents Africa from harnessing the resources of its regions, and Australia has too little arable territory.  Other than the two continents, there are offshore islands of significance are Britain, Japan and the New World, buffered by the Atlantic Ocean and the Pacific Ocean.

Eurasian dynamics.

Again, Spykman differs from Mackinder, who sees Eurasian wars as historically pitting the Heartland against the sea powers for control of the rimland, establishing a land power-sea power opposition. Spykman states that historically, battles have pitted either Britain and Rimland allies against Russia and its Rimland allies or Britain and Russia together against a dominating Rimland power. In other words, the Eurasian struggle was not the sea powers containing the Heartland but the prevention of any power from ruling the Rimland.  Spykman disagrees with Mackinder's famous dictum:  He refashions it thus:  Therefore, British, Russian, and US power would play the key roles in controlling the European litoral and there the essential power relations of the world.

US strategic goals.

Spykman thought that it was in the American interests to leave Germany strong after World War II to be able to counter Russia's power. Strategically, there was no difference between Germany dominating all the way to the Ural or Russia controlling all the way to Germany, and as both scenarios were equally threatening to the US.  Spykman predicted that Japan would lose the Pacific War and that China and Russia would remain to struggle against each other over boundaries. He also forecast the rise of China, becoming the dominant power in Asia and that the US would thus take responsibility for Japan's defense.  Spykman was opposed to European integration and argued that US interests favored balanced power in Europe, rather than integrated power. The US was fighting a war against Germany to prevent Europe's conquest, and it would not make sense to federalize or to unify Europe after a war that had been fought to preserve balance.

Legacy.

John Foster Dulles and the founders of the containment strategy would borrow heavily from Spykman, as well as Mackinder, when forging the American Cold War strategy.

Nicholas John Spykman (pronounced "Speak-man", 13 October 1893 – 26 June 1943) was an American political scientist who was one of the founders of the classical realist school in American foreign policy, transmitting Eastern European political thought to the United States. A Sterling Professor of International Relations, teaching as part of the Institute for International Studies at Yale University, one of his prime concerns was making his students geographically literate, as geopolitics was impossible without geographic understanding.

尼古拉斯·斯皮克曼(Nicholas John Spykman,1893年-1943年),荷兰裔美国人,地缘战略学家,国际关系学者,被称为「围堵政策之教父」。他是美国外交政策的古典现实主义的发起者之一,将东欧政治思想带入美国。曾在耶鲁大学国际研究所任职。他教学重点放在地缘政治学。49岁死于癌症。


Club career.

Porto.

Born in Mozelos (Santa Maria da Feira), Aveiro District, Neves grew up supporting FC Porto and joined the club's youth system at the age of 8. During his time with the academy, he also spent a season on loan at Padroense which acted as the under-16 side; on his time in the youth structures, reserve team manager Luís Castro described him as a player with "extraordinary mental qualities, to go along with technical and tactical skills".  Ahead of the 2014–15 campaign, Neves was earmarked to bypass Porto's under-19s and play with the reserves instead. Following an injury to teammate Mikel Agu, however, he was called by Julen Lopetegui to be part of the main squad's pre-season preparations. On 15 August 2014, aged 17 years and five months, he made his Primeira Liga debut where he started and scored in a 2–0 home win against Marítimo – in doing so, he became the youngest player in the club's history to score a goal in the competition. He played his first match in the UEFA Champions League five days later in a 1–0 win over Lille, and broke another record (previously held by Cristiano Ronaldo) by being the youngest Portuguese to appear in the tournament.  On 10 December 2014, during a Champions League group stage fixture against Shakhtar Donetsk, Neves suffered an injury to his right knee following a collision in midfield with Alex Teixeira. At the end of the game, the club explained that he suffered a sprain, with apparent damage to the internal lateral ligament. After spending nearly a month on the sidelines, he returned to the pitch in a 3–1 home win over União da Madeira in the Taça da Liga; following his injury, his role in midfield was filled by Casemiro and he spent the majority of the remainder of the season behind the Brazilian in the pecking order.  On 20 October 2015, aged 18 years and 221 days, Neves became the youngest player to start as captain in the Champions League, helping Porto to a 2–0 win against Maccabi Tel Aviv in the group phase and surpassing previous record holder Rafael van der Vaart, who held it since 16 September 2003. On 5 December, in a 2–1 success over Paços de Ferreira also at the Estádio do Dragão, he broke another record by becoming the youngest player to achieve 50 games for the club, surpassing players like Fernando Gomes and Jaime Magalhães.  Neves scored his second competitive goal on 3 February 2016, opening the scoreline in a 3–0 triumph at Gil Vicente for the semi-finals of the Taça de Portugal.

Wolverhampton Wanderers.

On 8 July 2017, EFL Championship side Wolverhampton Wanderers announced that they had signed Neves for an undisclosed fee, thought to be in the region of £15.8 million – a club and league record fee. Upon joining, he was reunited with former Porto manager Nuno Espírito Santo. He scored his first goal on 15 August, in a 3–2 away win over Hull City.  In April 2018, Neves was nominated for the EFL Championship Player of the Season and Young Player of the Season awards, and was also included in the Team of the Season along with teammates Conor Coady and John Ruddy. His team was ultimately promoted as champions with the player scoring six goals in 42 appearances, all of which were scored from outside of the box; at the conclusion of the campaign, he claimed a hat-trick of club awards as he walked away with the Player of the Season, Player's Player of the Season and Goal of the Season accolades. On 7 April 2019 it was also announced that Neves had been awarded the EFL Goal of the Year award (for 2018) for his goal against Derby County at Molineux in April 2018.  Wolves announced that Neves had signed a new contract in July 2018, extending until 2023. In the first match of the new season, he scored his first Premier League goal and assisted Raúl Jiménez in the latter's competitive debut, as his team twice came from behind to draw 2–2 against Everton. His appearance in the match, alongside compatriots Rui Patrício, João Moutinho, Diogo Jota and Hélder Costa also saw the club break the league record for the most Portuguese players named in a starting line-up.  On 6 October 2018, Neves made his 50th appearance for Wolverhampton and marked the occasion by contributing in the build-up for Matt Doherty's winning goal in a 1–0 victory over Crystal Palace. The following January, his 55th-minute winning goal against Liverpool at Molineux in the FA Cup, "a venomous dipping shot from that beat Simon Mignolet at his near post", was voted the best of the third round by visitors to BBC Sport's website.  On 25 September 2019, Neves was made Wolves' club captain for the first time, when he led the team in the absence of regular skipper Conor Coady as Wolves secured progression to the 2019–20 EFL Cup Fourth Round with a penalty shoot-out victory over Reading at Molineux in the clubs' Third Round fixture.

International career.

Youth.

Neves represented Portugal at the 2014 UEFA European Under-17 Championship, helping the country reach the semi-finals as team captain. For his performances, he was named among the top ten talents of the tournament by a selection of UEFA reporters.  On 29 August 2014, still aged 17, Neves was called by coach Rui Jorge to be part of the under-21 side. He scored his first goal on 14 October, helping to a 5–4 home win against the Netherlands for the playoff round of the 2015 UEFA European Under-21 Championship.  When the finals began in the Czech Republic, Neves broke a new record by becoming the youngest player to debut for the Portuguese under-21s in the tournament, aged 18 years and three months, playing five minutes to help beat England 1–0 in the group stage opener. It was his only appearance, as the country reached the final and lost it to Sweden in a penalty shootout.

Senior.

Neves was first called up to the senior squad on 10 November 2015, ahead of friendlies against Russia and Luxembourg as an injury replacement for João Moutinho. He made his debut in the former match, featuring 17 minutes in the 0–1 loss in Krasnodar, then played the entire 2–0 win against the latter at the Stade Josy Barthel, sharing the midfield area with Porto teammates Danilo Pereira and André André.  Neves was selected by full side manager Fernando Santos to a preliminary 35-man squad for the 2018 FIFA World Cup, but he did not make the final cut.  At the 2019 UEFA Nations League Finals on home soil, Neves played in the 3–1 semi-final win over Switzerland, but was dropped from the starting eleven for the final against the Netherlands for the more defensive Danilo. In that match at his former club ground the "Dragão", he came on in added time for William Carvalho as the Portuguese won 1–0.

Style of play.

An article on UEFA.com described Neves as a defensive midfielder who is not afraid to go forward and prompt attacks, showing great maturity combined with an accurate passing skill, great vision and an astute sense of positioning. His playing attributes also granted him comparisons to Portuguese international teammate Moutinho for his fine first touch, similar desire to press his opponents and excellent distribution.  Neves also drew praise in the media for his technical, tactical and mental qualities, as well as his intelligence and long-range shooting ability.

Honours.

Wolverhampton Wanderers  Portugal  Portugal U21  Individual

Rúben Diogo da Silva Neves (; born 13 March 1997) is a Portuguese professional footballer who plays as a midfielder for Premier League club Wolverhampton Wanderers and the Portugal national team.

鲁本·内维斯(Rúben Diogo da Silva Neves)(出生于1997年3月13日)是葡萄牙的职业足球运动员,司职中场,现效力于英超的伍尔弗汉普顿流浪。


History of connected cars, 1996–present.

General Motors was the first automaker to bring the first connected car features to market with OnStar in 1996 in Cadillac DeVille, Seville and Eldorado. OnStar was created by GM working with Motorola Automotive (that was later bought by Continental). The primary purpose was safety and to get emergency help to a vehicle when there was an accident. The sooner medical helps arrives the more likely the drivers and passengers would survive. A cellular telephone call would be routed to a call center where the agent sent help.  At first, OnStar only worked with voice but when cellular systems added data the system was able to send the GPS location to the call center. After the success of OnStar, many automakers followed with similar safety programs that usually come with a free trial for a new car and then a paid subscription after the trial is over.  Remote diagnostics were introduced in 2001. By 2003 connected car services included vehicle health reports, turn-by-turn directions and a network access device. Data-only telematics were first offered in 2007.  In the summer of 2014, Audi was the first automaker to offer 4G LTE Wi-Fi Hotspots access and the first mass deployment of 4G LTE was by General Motors.  By 2015, OnStar had processed 1 billion requests from customers.  AA plc (formerly known as The Automobile Association) introduced Car Genie, the first piece of connected car technology in the UK that connects directly to a breakdown service, not only warning of issues with car health, but intervening directly with a phone call to customers to help them prevent a breakdown.  In 2017, European technology start-up Stratio Automotive provides over 10,000 vehicles predictive intelligence enabling fleet operators to better manage and maintain their vehicles.

Types of connectivity.

There are 5 ways a vehicle can be connected to its surroundings and communicate with them:

Categories of applications.

Applications can be separated into two categories:  Examples include, amongst others:  The connected car segment can be further classified into 8 categories.

Single-vehicle applications.

Current automobiles entail embedded navigation systems, smartphone integration and multimedia packages. Typically, a connected car made after 2010 has a head-unit, in car entertainment unit, in-dash system with a screen from which the operations of the connections can be seen or managed by the driver. Types of functions that can be made include music/audio playing, smartphone apps, navigation, roadside assistance, voice commands, contextual help/offers, parking apps, engine controls and car diagnosis.  On January 6, 2014, Google announced the formation of the Open Automotive Alliance (OAA) a global alliance of technology and auto industry leaders committed to bringing the Android platform to cars starting in 2014. The OAA includes Audi, GM, Google, Honda, Hyundai and Nvidia.  On March 3, 2014, Apple announced a new system to connect iPhone 5/5c/5S to car infotainment units using iOS 7 to cars via a Lightning connector, called CarPlay.  Android Auto was announced on June 25, 2014 to provide a way for Android smartphones to connect to car infotainment systems.  Increasingly, connected cars (and especially electric cars) are taking advantage of the rise of smartphones, and apps are available to interact with the car from any distance. Users can unlock their cars, check the status of batteries on electric cars, find the location of the car, or remotely activate the climate control system.  Innovations to be introduced until 2020 include the full integration of smartphone applications, such as the linkage of the smartphone calendar, displaying it on the car's windshield and automatic address searches in the navigation system for calendar entries. In the longer term, navigation systems will be integrated in the windshield and through augmented reality project digital information, like alerts and traffic information, onto real images from the driver's perspective.  Near-term innovations regarding Vehicle Relationship Management (VRM) entail advanced remote services, such as GPS tracking and personalized usage restrictions. Further, maintenance services like over-the-air tune-ups, requiring the collaboration of car dealers, OEMs and service centers, are under development.  Despite various market drivers there are also barriers that have prevented the ultimate breakthrough of the connected car in the past few years. One of these is the fact that customers are reluctant to pay the extra costs associated with embedded connectivity and instead use their smartphones as solution for their in-car connectivity needs. Because this barrier is likely to continue, at least in the short-term, car manufacturers are turning to smartphone integration in an effort to satisfy consumer demand for connectivity.

Cooperative safety-of-life and efficiency.

These services relate to Advanced Driver-Assistance Systems (ADAS), that depend on the sensory input of more than one vehicle and enable instant reaction through automatic monitoring, alerting, braking and steering activities. They depend on instant vehicle-to-vehicle communication, as well as infrastructure, functioning across brands and national borders and offering cross-brand and cross-border levels of privacy and security. The US National Highway Traffic Safety Administration (NHTSA) for that reason has argued for regulation in its Advance Notice of Proposed Rulemaking (ANPRM) on V2V Communication and argued the case in US Congress. NHTSA began the rule-making process on December 13, 2016, proposing to mandate dedicated short-range communications (DSRC) technology in new light vehicles. Under this proposed rule, vehicles would broadcast a defined data packet, the "basic safety message" (BSM) up to ten times per second, indicating vehicle location, heading, and speed. In March, 2017, GM became the first US automaker to provide DSRC as standard equipment on a production automobile, the Cadillac CTS. The US also has appropriate standards – IEEE 802.11p – and frequency rules in place. In Europe a frequency is harmonised for transport safety and a harmonised standard, called ETSI ITS-G5, are in place. In the EU there is no push to oblige vehicle manufacturers to introduce connect. Discussions about a regulatory framework for privacy and security are ongoing.  Technologically speaking cooperative applications can be implemented. Here the regulatory framework is the main obstacle to implementation, questions like privacy and security need to be addressed. British weekly "The Economist" even argues that the matter is regulatory driven.

Hardware.

The necessary hardware can be divided into built-in or brought-in connection systems. The built-in telematics boxes most commonly have a proprietary internet connection via a GSM module and are integrated in the car IT system. Although most connected cars in the United States use the GSM operator AT&T with a GSM SIM such as the case with Volvo, some cars such as the Hyundai Blue Link system utilizes Verizon Wireless Enterprise, a non-GSM CDMA operator.  Most brought-in devices are plugged in the OBD (on-board diagnostics) port for electrification and access to vehicle data and can further be divided into two types of connection:  All forms of hardware have typical use cases as drivers. The built-in solutions were mostly driven by safety regulations in Europe for an automated Emergency Call (abbr. eCall). The brought-in devices usually focus on one customer segment and one specific use case.

Insurance.

The data provided by greater vehicle connectivity is impacting the car insurance industry. Predictive-modeling and machine-learning technologies, as well as real-time data streaming, providing among others information on driving speed, routes and time, are changing insurers' doing-of-business. Early adopters have begun to adjust their offering to the developments in the automotive industry, leading them to transition from being pure insurance product provider to becoming insurance-service hybrids.  Progressive, for example, has introduced its usage-based-insurance program, Snapshot, in 2008, which takes into account driving times and ability. The data gathered through an onboard diagnostics device allows the company to perform further personal and regional risk assessments. Another innovation being tested in the insurance industry regards telematics devices, which transmit vehicle and driver data through wide-area networks and are subsequently used to influence driving behavior, for legal purposes and the identification of fraudulent insurance claims. Further applications are dynamic risk profiles and improved customer segmentation. Future services include coaching on driving skills for fuel efficiency and safety reasons, the prediction of maintenance needs and providing advise to car owners regarding the best time to sell their car.

Trends.

The following trends are strengthening the shift towards a fully developed connected cars industry, changing the concept of what is understood as a car and what are its functions.  Technological innovation in the field of connectivity is accelerating. High-speed computers help make the car aware of its surroundings, which can transform manoeuvring a self-driving vehicle an increasing reality.  There are initiatives to use ethernet technology to connect the sensors that allow for advanced driving assistance systems (ADAS). Through the ethernet, network speed inside the vehicle can increase from one megabit to gigabits. Further, ethernet uses switches that allow connections to any number of devices, reducing the amount of cabling required and thus the overall weight of the car. Moreover, it is more scalable, allowing devices and sensors to connect at different speeds and has the benefit of components being available off the shelf.  In fact, research also shows that customers are willing to switch manufacturers just to be able to use mobile devices and connectivity. In 2014 there were 21% who were willing to do so whereas in 2015 this number climbed up to 37%. On top of that 32% of those customers would also be ready to pay for a service related to connectivity on top on a base model. This figure has been at 21% in 2014, one year before. The increase of customers willing to switch manufacturers and to pay for such services shows the increase in importance for connected cars.  The Internet of Things will be used to provide mobile services in the car with high-speed internet. This feature will enable real time traffic control, interaction with the car manufacturer service for remote diagnostics and improved company logistics automation. Moreover, in the beginning of the self-driven car era, internet will be used for information exchange between the cars for better route selection and accident reports.

Criticism.

Drawbacks and Challenges.

Although the connected car offers lots of benefits and excitement to the drivers, it also faces drawbacks and challenges;

A connected car is a car that is one that can communicate bidirectionally with other systems outside of the car (LAN). This allows the car to share internet access, and hence data, with other devices both inside and outside the vehicle. For safety-critical applications, it is anticipated that cars will also be connected using dedicated short-range communications (DSRC) radios, operating in the FCC-granted 5.9 GHz band with very low latency.

互联汽车(connected car)也称为联网汽车,是可以连接到互联网的车辆,一般也可以存取无线局域网,因此车辆可以和其他车内或是车外的设备分享网络资源。多半这类车辆也会有一些配合互联网或是无线局域网的特别机能,可以让驾驶更加便利。在安全关键的应用中,车辆会利用专用短程通信(DSRC)广播互联,运作在FCC特许的5.9GHz频段,其延迟非常的小。


亚历山大·弗拉基米罗维奇·扎哈尔琴科

Early and personal life.

Zakharchenko was born in Ukrainian SSR, Donetsk on 26 June 1976. He graduated from technical college and then worked as a mine electrician before opening a business in the mining industry, also having studied with the law institute of the Interior Ministry. Zakharchenko had a wife, Natalia, and four sons. He was godfather to's children.

Political career.

In 2010, Zakharchenko became head of the Donetsk branch of Oplot (a non-governmental organisation established in Kharkiv).  On 16 April 2014, 20 members of Oplot (including Zakharchenko), armed with clubs, rifles and some automatic weapons, occupied the offices of Donetsk city council, demanding a referendum on the status of the region. Oplot were fairly well-behaved, and had helped free hostages and abductees.  By April 2014, he was the commander of a local militia in Donetsk (Oplot). The members of this militia were mainly from civic and martial arts group. Zakharchenko was appointed "military commandant of Donetsk" on 16 May 2014. Since May 2014, Zakharchenko played a leading role in the insurgency against Ukraine's central government. On 22 July 2014, he was wounded in the arm during a fight against Ukrainian government forces at Kozhevnia. In late August 2014, the DPR Ministry of Defence announced Zakharchenko's promotion to major general.  Zakharchenko succeeded Alexander Borodai as Prime Minister on 7 August 2014, Borodai then became the DPR Deputy Prime Minister. According to Borodai, Donbass native Zakharchenko succeeded him for a Russian government effort "to try to show the West that the uprising was a grassroots phenomenon". Borodai also claims that he personally recommended Zakharchenko as Prime Minister. In September 2014, Zakharchenko was the lead negotiator for the DPR at the Minsk Protocol, which agreed to a peace plan for the War in Donbass.  During the 2014 Donetsk parliamentary elections, Zakharchenko won the prime ministership with 78.93% of the vote. Next day after the elections, the head of Oplot organization Evgeniy Zhylin gave interview to the Russian television channel Dozhd where he told how Zakharchenko was appointed as a head of Donetsk branch of Oplot and how his candidacy as a leader of DPR was promoted from Moscow.  In February 2015, Zakharchenko, representing the DPR, agreed to the Minsk II peace treaty, calling it a "major victory for the Lugansk and Donetsk People's Republics". After signing the Minsk agreements, Zakharchenko stated that should the Ukrainian authorities violate the terms of the agreements, fail to withdraw from the border, or fail to release the Donetsk POWs, he would take Kharkiv and destroy the Ukrainian battalions in Debaltseve. Additionally, Zakharchenko stated that he had no intention on adhering to the ceasefire within the Debaltseve region. He was subsequently wounded in the leg on 17 February 2015 during the closing stages of the Battle of Debaltseve. In January 2016, he described the battle in July 2014 for the village of Kozhevnia as "a milestone for me", saying that it was "our first offensive. Unfortunately, in the course of fighting we practically destroyed this village. By burning down houses, we saved our lives and the lives of our people."

Political positions.

During the parliamentary election campaign, Zakharchenko told potential voters that he wanted pensions to be "higher than in Poland." Zakharchenko said this was feasible because Donetsk is very rich, "like the United Arab Emirates [...] [the Donetsk people] have coal, metallurgy, natural gas [...] [t]he difference between [them] and the Emirates is they don't have a war [in the Emirates] and [Donetsk does]." Zakharchenko promised to build "a normal state, a good one, a just one. [Donetsk] boys died for this, civilians are still being killed for this".  Zakharchenko made several homophobic remarks. For example, he stated: “...this generation is being raised on democracy, which implies that a family can have two fathers or two mothers. To me, this is categorically unacceptable.”  Zakharchenko was in favour of the death penalty.  In an interview with Zakhar Prilepin on Tsargrad TV in late 2016, he said that Britain must be conquered, which would usher in a "Golden Age for Russia". Prilepin, a Russian writer and political activist of the National Bolshevik Party, stated that Zakharchenko is among the top five most popular politicians in Ukraine and could be elected the President of Ukraine. In 2016, Prilepin published a book in which Alexander Zakharchenko is the protagonist.

Human rights abuses.

During the War in Donbass there were many cases of forced disappearances in the Donetsk People's Republic. Zakharchenko said that his forces detained up to five "Ukrainian subversives" every day. It was estimated that about 632 people were under illegal detention by separatist forces by 11 December 2014. Freelance journalist Stanislav Aseyev was abducted on 2 June 2017. At first, the "de facto" DNR government denied knowing his whereabouts, but on 16 July an agent of the DNR's Ministry of State Security confirmed that Aseyev was in their custody and that he is suspected of espionage. Independent media is not allowed to report from the DNR-controlled territory. Amnesty International has demanded that Zakharchenko release Aseyev, but he was still in custody, and had started a hunger strike.

Death.

Zakharchenko was killed by a bomb explosion in the café "Separ" on Pushkin Boulevard in Donetsk, on 31 August 2018. Preliminary reports say DNR's finance minister Alexander Timofeyev was also wounded in the blast.  The DNR and the Russian Federation blamed the Ukrainian government authorities; officials in Kiev reject these accusations, stating that Zakharchenko's death was the result of civil strife in the DNR. Initial reports say that Deputy Prime Minister Dmitry Trapeznikov was appointed acting head of the Donetsk People's Republic.  Funeral and memorial services were scheduled for 2 September, in the Donetsk Opera and Ballet Theatre. A three-day mourning period was declared on 1 September, with the start of the new academic year in the territory being postponed until 4 September. The ceremony was attended by South Ossetian President Anatoly Bibilov, head of the Republic of Crimea Sergei Aksyonov and Russian lawmaker Natalia Poklonskaya.

Reactions.

Russian President Vladimir Putin sent his condolences to Zakharchenko's family, calling his death a "contemptible murder". The Russian Foreign Ministry's official spokesperson Maria Zakharova blamed Ukraine for the death, claiming that it is "driving its country to the verge of an all-out disaster at increasingly faster speeds". Residents of the Republic of Crimea honoured the memory of Zakharchenko by laying flowers by his portraits in the central square of their capital, Simferopol.  The acting head of the Luhansk People's Republic, Leonid Pasechnik, paid tribute to Zakharchenko at a memorial service in the Republic, saying that the "banner of struggle, lifted by Alexander Zakharchenko, will never fall". He also said that the Donbass region "will not forgive Zakharchenko's murder".  According to Igor Girkin the assassination had financial motives and no involvement from Ukraine.

Alexander Vladimirovich Zakharchenko (, ; 26 June 1976 – 31 August 2018) was a separatist leader who was the head of state and Prime Minister of the Donetsk People's Republic, a self-proclaimed state and rebel group which declared independence from Ukraine on 11 May 2014. Zakharchenko was appointed Prime Minister in August 2014 after his predecessor, Alexander Borodai, resigned, and went on to win the early November 2014 election for the position.

亚历山大·弗拉基米罗维奇·扎哈尔琴科(;,1976年-6月26日-2018年-8月31日),顿涅茨克政治人物,曾任顿涅茨克总统兼总理。