Legal case retrieval, which aims to retrieve relevant cases given a query case, plays an essential role in the legal system. While recent research efforts improve the performance of traditional ad-hoc retrieval models, legal case retrieval is still challenging since queries are legal cases, which contain hundreds of tokens. Legal cases are much longer and more complicated than keywords queries. Apart from that, the definition of legal relevance is beyond the general definition. In addition to general topical relevance, the relevant cases also involve similar situations and legal elements, which can support the judgment of the current case. In this paper, we propose an interaction-focused network for legal case retrieval with a multi-view contrastive learning objective. The contrastive learning views, including case-view and element-view, aim to overcome the above challenges. The case-view contrastive learning minimizes the hidden space distance between relevant legal case representations produced by a pre-trained language model (PLM) encoder. The element-view builds positive and negative instances by changing legal elements of cases to help the network better compute legal relevance. To achieve this, we employ a legal element knowledge-aware indicator to detect legal elements of cases. We conduct extensive experiments on the benchmark of relevant case retrieval. Evaluation results indicate our proposed method obtains significant improvement over the existing methods.

Airports and industries should be required to publicly disclose their use of facial recognition, while the National Security and Intelligence Committee of Parliamentarians should review any military or intelligence use of the technology, they said. Tamir Israel, a lawyer with the Samuelson-Glushko Canadian Internet Policy and Public Interest Clinic, testified at the committee that travelers might not be aware they're subject to the technology, such as at the customs screening mechanism at the Pearson Airport in Toronto. The government should disclose its own acquisitions of the technology, and "create a public AI registry in which all algorithmic tools used by any entity operating in Canada are listed," MPs said. Privacy lawyer Carole Piovesan told the committee that while discussions on FRT "tend to focus on security and surveillance," the technology is also used by other sectors, including health care, retail and e-commerce, and telecom and IT. The technology is also more accurate in identifying white individuals, and less accurate in identifying people of colour.

Reliable Robotics, a leader in autonomous aircraft systems, announced the addition of Scott O'Brien to drive legislative and advocacy efforts. O'Brien brings 18 years of wide-ranging experience in grassroots initiatives, legislative strategy and public policy analysis to his new role as Vice President of Legislative Affairs. He will focus on creating an ecosystem to advance aviation safety through airborne autonomy and remotely piloted aircraft, key enablers of Advanced Air Mobility (AAM). Near-term, O'Brien will represent Reliable before Capitol Hill and the Administration, along with industry associations to prioritize safety technologies that will prevent common aviation accidents. TigerLRM, the next-generation sales enablement and CRM platform, announced the launch of its new mobile apps for iOS and Android devices.

"Milton's voice was not stifled or choked for making Satan a heroic figure… in fact, in "Areopagitica", the blind poet champions free speech: Give me the liberty to know, to utter and to argue freely according to conscience above all liberties…. Artificial Intelligence may, in the foreseeable future, be an entity that thinks independently enough to enjoy the pleasures of expression the way humanity does. Whether it may have the potential evils of a Satan is to be seen, but its ontology and a legal framework to accommodate it may be in order. Morality aside, it may be reasonably seen that it is a power that can possibly "make a heaven of hell, a hell of heaven" [2] of the world. It may be pointed out that the possibility of "strong artificial intelligence" -- artificial intelligence constructs that are comparable to a human brain, with features like consciousness that are identified with being human,[3] are entirely hypothetical and may remain so.

Federated learning (FL) has been proposed to protect data privacy and virtually assemble the isolated data silos by cooperatively training models among organizations without breaching privacy and security. However, FL faces heterogeneity from various aspects, including data space, statistical, and system heterogeneity. For example, collaborative organizations without conflict of interest often come from different areas and have heterogeneous data from different feature spaces. Participants may also want to train heterogeneous personalized local models due to non-IID and imbalanced data distribution and various resource-constrained devices. Therefore, heterogeneous FL is proposed to address the problem of heterogeneity in FL. In this survey, we comprehensively investigate the domain of heterogeneous FL in terms of data space, statistical, system, and model heterogeneity. We first give an overview of FL, including its definition and categorization. Then, We propose a precise taxonomy of heterogeneous FL settings for each type of heterogeneity according to the problem setting and learning objective. We also investigate the transfer learning methodologies to tackle the heterogeneity in FL. We further present the applications of heterogeneous FL. Finally, we highlight the challenges and opportunities and envision promising future research directions toward new framework design and trustworthy approaches.

The ubiquity of Machine Learning (ML) models, and more specifically deep neural network (NN) models, in all sorts of applications has become undeniable in recent years. From classifying images [1, 2, 3], detecting objects [4, 1] and performing semantic segmentation [5, 4] to translating from one human language to another [6] and doing sentiment analysis [7], the advances in different subfields of ML can be attributed mostly to the explosion of computing power and their ability to speed up the training process of artificial NNs. Most famously, AlexNet [8] allowed for an impressive jump in performance in the challenging ILSVRC2012 image classification dataset [1], also known as ImageNet, permanently cementing deep convolutional NN (CNN) architectures in the field of computer vision. Since then, architectures have gotten more refined [9, 10], training procedures have gotten increasingly more complex [11], and their performance and robustness have greatly improved as a consequence. Namely, the success of these deep CNN models is related to their ability to treat high-dimensional and complex data such as images or natural language. The impressive performance of NNs for machine learning tasks can be explained by the ability of their flexible architecture to capture meaningful information on various kinds of complex data and the fact that they are potentially composed of millions of parameters. However, this poses a major challenge: deciphering the reasoning behind the model's predictions. For instance, typical NN architectures for classification or regression problems incrementally transform the representation of the input data in the so-called latent space (or feature space) and then use this transformed representation to make their predictions, as summarized in Figure 1. Each step of this incremental data processing pipeline (or feature extraction chain) is carried out by a so-called layer, which is mathematically a non-linear function (blue rectangle in Figure 1).

In 2017, spurred by advocacy from civil society groups, the New York City Council created a task force to address the city's growing use of artificial intelligence. But the task force quickly ran aground attempting to come to a consensus on the scope of "automated decision systems." In one hearing, a city agency argued that the task force's definition was so expansive that it might include simple calculations such as formulas in spreadsheets. By the end of its eighteen-month term, the task force's ambitions had narrowed from addressing how the city uses automated decision systems to simply defining the types of systems that should be subject to oversight. As policymakers around the world have attempted to create guidance and regulation for AI's use in settings ranging from school admissions and home loan approvals to military weapon targeting systems, they all face the same problem: AI is really challenging to define.

This study attempts to investigate cross-strait variations on two typical synonymous loanwords in Chinese, i.e. xie2shang1 and tan2pan4, drawn on MARVS theory. Through a comparative analysis, the study found some distributional, eventual, and contextual similarities and differences across Taiwan and Mainland Mandarin. Compared with the underused tan2pan4, xie2shang1 is significantly overused in Taiwan Mandarin and vice versa in Mainland Mandarin. Additionally, though both words can refer to an inchoative process in Mainland and Taiwan Mandarin, the starting point for xie2shang1 in Mainland Mandarin is somewhat blurring compared with the usage in Taiwan Mandarin. Further on, in Taiwan Mandarin, tan2pan4 can be used in economic and diplomatic contexts, while xie2shang1 is used almost exclusively in political contexts. In Mainland Mandarin, however, the two words can be used in a hybrid manner within political contexts; moreover, tan2pan4 is prominently used in diplomatic contexts with less reference to economic activities, while xie2sahng1 can be found in both political and legal contexts, emphasizing a role of mediation.

While large-scale neural language models, such as GPT2 and BART, have achieved impressive results on various text generation tasks, they tend to get stuck in undesirable sentence-level loops with maximization-based decoding algorithms (\textit{e.g.}, greedy search). This phenomenon is counter-intuitive since there are few consecutive sentence-level repetitions in human corpora (e.g., 0.02\% in Wikitext-103). To investigate the underlying reasons for generating consecutive sentence-level repetitions, we study the relationship between the probabilities of the repetitive tokens and their previous repetitions in the context. Through our quantitative experiments, we find that 1) Language models have a preference to repeat the previous sentence; 2) The sentence-level repetitions have a \textit{self-reinforcement effect}: the more times a sentence is repeated in the context, the higher the probability of continuing to generate that sentence; 3) The sentences with higher initial probabilities usually have a stronger self-reinforcement effect. Motivated by our findings, we propose a simple and effective training method \textbf{DITTO} (Pseu\underline{D}o-Repet\underline{IT}ion Penaliza\underline{T}i\underline{O}n), where the model learns to penalize probabilities of sentence-level repetitions from pseudo repetitive data. Although our method is motivated by mitigating repetitions, experiments show that DITTO not only mitigates the repetition issue without sacrificing perplexity, but also achieves better generation quality. Extensive experiments on open-ended text generation (Wikitext-103) and text summarization (CNN/DailyMail) demonstrate the generality and effectiveness of our method.

Way back in May 2014, the European court of justice issued a landmark ruling that European citizens had the right to petition search engines to remove search results that linked to material that had been posted lawfully on third-party websites. This was popularly but misleadingly described as the "right to be forgotten"; it was really a right to have certain published material about the complainant delisted by search engines, of which Google was by far the most dominant. Or, to put it crudely, a right not to be found by Google. On the morning the ruling was released, I had a phone call from a relatively senior Google employee whom I happened to know. It was clear from his call that the company had been ambushed by the ruling – its expensive legal team had plainly not expected it. But it was also clear that his US bosses were incensed by the effrontery of a mere European institution in issuing such a verdict.