Large multilingual models have inspired a new class of word alignment methods, which work well for the model's pretraining languages. However, the languages most in need of automatic alignment are low-resource and, thus, not typically included in the pretraining data. In this work, we ask: How do modern aligners perform on unseen languages, and are they better than traditional methods? We contribute gold-standard alignments for Bribri--Spanish, Guarani--Spanish, Quechua--Spanish, and Shipibo-Konibo--Spanish. With these, we evaluate state-of-the-art aligners with and without model adaptation to the target language. Finally, we also evaluate the resulting alignments extrinsically through two downstream tasks: named entity recognition and part-of-speech tagging. We find that although transformer-based methods generally outperform traditional models, the two classes of approach remain competitive with each other.

Text summarization has been a crucial problem in natural language processing (NLP) for several decades. It aims to condense lengthy documents into shorter versions while retaining the most critical information. Various methods have been proposed for text summarization, including extractive and abstractive summarization. The emergence of large language models (LLMs) like GPT3 and ChatGPT has recently created significant interest in using these models for text summarization tasks. Recent studies \cite{goyal2022news, zhang2023benchmarking} have shown that LLMs-generated news summaries are already on par with humans. However, the performance of LLMs for more practical applications like aspect or query-based summaries is underexplored. To fill this gap, we conducted an evaluation of ChatGPT's performance on four widely used benchmark datasets, encompassing diverse summaries from Reddit posts, news articles, dialogue meetings, and stories. Our experiments reveal that ChatGPT's performance is comparable to traditional fine-tuning methods in terms of Rouge scores. Moreover, we highlight some unique differences between ChatGPT-generated summaries and human references, providing valuable insights into the superpower of ChatGPT for diverse text summarization tasks. Our findings call for new directions in this area, and we plan to conduct further research to systematically examine the characteristics of ChatGPT-generated summaries through extensive human evaluation.

This paper studies the role of over-parametrization in solving non-convex optimization problems. The focus is on the important class of low-rank matrix sensing, where we propose an infinite hierarchy of non-convex problems via the lifting technique and the Burer-Monteiro factorization. This contrasts with the existing over-parametrization technique where the search rank is limited by the dimension of the matrix and it does not allow a rich over-parametrization of an arbitrary degree. We show that although the spurious solutions of the problem remain stationary points through the hierarchy, they will be transformed into strict saddle points (under some technical conditions) and can be escaped via local search methods. This is the first result in the literature showing that over-parametrization creates a negative curvature for escaping spurious solutions. We also derive a bound on how much over-parametrization is requited to enable the elimination of spurious solutions.

Alzheimer's disease is a degenerative brain disease. Being the primary cause of Dementia in adults and progressively destroys brain memory. Though Alzheimer's disease does not have a cure currently, diagnosing it at an earlier stage will help reduce the severity of the disease. Thus, early diagnosis of Alzheimer's could help to reduce or stop the disease from progressing. In this paper, we proposed a deep convolutional neural network-based model for learning model using to determine the stage of Dementia in adults based on the Magnetic Resonance Imaging (MRI) images to detect the early onset of Alzheimer's.

American defense officials on Tuesday sought to dispel any doubt that Iran is supplying drones for Russia's war in Ukraine, releasing photos and analysis of unmanned aircraft deployed in the conflict to demonstrate Tehran's involvement. During a briefing in London, analysts from the Defense Intelligence Agency displayed photos of drones that attacked Ukraine alongside images of those previously traced to Iran. A comparison of design details such as tail fins, nose cones and landing gear shows that the weapons used in Ukraine are "indistinguishable" from Shahed-131 and -136 attack drones and Mohajer 6 unmanned aerial vehicles used in the Middle East. The effort to "show the homework'' is intended to help persuade governments or international agencies of Tehran's involvement. Iran has said it supplied a "small number" of drones to Russia before the invasion of Ukraine but has denied providing any more since troops crossed the border last February. The evidence proves otherwise, an official from the Defense Intelligence Agency said while speaking on condition of anonymity because of the sensitivity of the information. "Iran is a partner in the conflict with Russia,'' the official said.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Chinese leader Xi Jinping expressed support for Iran during a visit by its president on Tuesday as Tehran tries to expand relations with Beijing and Moscow to offset Western sanctions over its nuclear development. The official Chinese account of Xi's meeting with Ebrahim Raisi gave no indication whether they discussed Russia's attack on Ukraine. Tehran supplied military drones to Russian President Vladimir Putin's government but says they were delivered before the war began.

Microsoft-backed OpenAI has kept its hit ChatGPT app off-limits to users in China, but the app is attracting huge interest in the country, with firms rushing to integrate the technology into their products and launch rival solutions. While residents in the country are unable to create OpenAI accounts to access the artificial intelligence-powered (AI) chatbot, virtual private networks and foreign phone numbers are helping some bypass those restrictions. At the same time, the OpenAI models behind the ChatGPT programme, which can write essays, recipes and complex computer code, are relatively accessible in China and increasingly being incorporated into Chinese consumer technology applications from social networks to online shopping. The tool's surging popularity is rapidly raising awareness in China about how advanced U.S. AI is and, according to analysts, just how far behind tech firms in the world's second-largest economy are as they scramble to catch up. "There is huge excitement around ChatGPT. Unlike the metaverse which faces huge difficulty in finding real-life application, ChatGPT has suddenly helped us achieve human-computer interaction," said Ding Daoshi, director of Beijing-based internet consultancy Sootoo.

We propose to explore the potential of physics-informed neural networks (PINNs) in solving a class of partial differential equations (PDEs) used to model the propagation of chronic inflammatory bowel diseases, such as Crohn's disease and ulcerative colitis. An unsupervised approach was privileged during the deep neural network training. Given the complexity of the underlying biological system, characterized by intricate feedback loops and limited availability of high-quality data, the aim of this study is to explore the potential of PINNs in solving PDEs. In addition to providing this exploratory assessment, we also aim to emphasize the principles of reproducibility and transparency in our approach, with a specific focus on ensuring the robustness and generalizability through the use of artificial intelligence. We will quantify the relevance of the PINN method with several linear and non-linear PDEs in relation to biology. However, it is important to note that the final solution is dependent on the initial conditions, chosen boundary conditions, and neural network architectures.

Satellite imagery analysis using deep learning methods, specifically convolutional neural networks (CNNs), has grown in popularity since 2012, with uses extending into the estimation of population [1], wealth [2], poverty [3], conflict [4], migration [5], education [6], and infrastructure [7], among other applications [8, 9, 10, 11]. These techniques have broadly illustrated that harnessing satellites to remotely track development over time in otherwise data sparse regions is a potentially effective strategy [12]. One currently untested application of deep learning with satellite imagery is the identification and monitoring of industrial sites (e.g., factories, power plants, ports). The development of industrial sites is of broad interest, as it can serve as a proxy for everything from economic development [13] to the projection of soft power [14]. Because of its interrelationship with national security or proprietary corporate interests, information on such large-scale development is often undocumented or difficult to obtain openly by interested parties. This article focuses on testing our capability to automatically detect and monitor industrial sites within China using high-resolution panchromatic satellite imagery. Largely unrecorded in structured open source text information, the size and extent of industrial sites in China can be observed through routine or targeted satellite collection. From select sources, many locations appear, on average, at least yearly in cloud-free high-resolution imagery from satellite-based sensors over the past 15 years; some locations of interest have temporal granularity of as high as one day. To-date, no work has explored the use of machine learning methods trained on satellite imagery to estimate, and monitor over time, the development of particular economic industries at the scale of individual sites.

Robust tensor completion (RTC) aims to recover a low-rank tensor from its incomplete observation with outlier corruption. The recently proposed tensor ring (TR) model has demonstrated superiority in solving the RTC problem. However, the existing methods either require a pre-assigned TR rank or aggressively pursue the minimum TR rank, thereby often leading to biased solutions in the presence of noise. In this paper, a Bayesian robust tensor ring decomposition (BRTR) method is proposed to give more accurate solutions to the RTC problem, which can avoid exquisite selection of the TR rank and penalty parameters. A variational Bayesian (VB) algorithm is developed to infer the probability distribution of posteriors. During the learning process, BRTR can prune off slices of core tensor with marginal components, resulting in automatic TR rank detection. Extensive experiments show that BRTR can achieve significantly improved performance than other state-of-the-art methods.