Recent advances in citation recommendation have improved accuracy by leveraging multi-view representation learning to integrate the various modalities present in scholarly documents. However, effectively combining multiple data views requires fusion techniques that can capture complementary information while preserving the unique characteristics of each modality. We propose a novel citation recommendation algorithm that improves upon linear Canonical Correlation Analysis (CCA) methods by applying Deep CCA (DCCA), a neural network extension capable of capturing complex, non-linear relationships between distributed textual and graph-based representations of scientific articles. Experiments on the large-scale DBLP (Digital Bibliography & Library Project) citation network dataset demonstrate that our approach outperforms state-of-the-art CCA-based methods, achieving relative improvements of over 11% in Mean Average Precision@10, 5% in Precision@10, and 7% in Recall@10. These gains reflect more relevant citation recommendations and enhanced ranking quality, suggesting that DCCA's non-linear transformations yield more expressive latent representations than CCA's linear projections.

Chain-of-Thought (CoT) reasoning has significantly advanced state-of-the-art AI capabilities. However, recent studies have shown that CoT reasoning is not always faithful, i.e. CoT reasoning does not always reflect how models arrive at conclusions. So far, most of these studies have focused on unfaithfulness in unnatural contexts where an explicit bias has been introduced. In contrast, we show that unfaithful CoT can occur on realistic prompts with no artificial bias. Our results reveal non-negligible rates of several forms of unfaithful reasoning in frontier models: Sonnet 3.7 (16.3%), DeepSeek R1 (5.3%) and ChatGPT-4o (7.0%) all answer a notable proportion of question pairs unfaithfully. Specifically, we find that models rationalize their implicit biases in answers to binary questions ("implicit post-hoc rationalization"). For example, when separately presented with the questions "Is X bigger than Y?" and "Is Y bigger than X?", models sometimes produce superficially coherent arguments to justify answering Yes to both questions or No to both questions, despite such responses being logically contradictory. We also investigate restoration errors (Dziri et al., 2023), where models make and then silently correct errors in their reasoning, and unfaithful shortcuts, where models use clearly illogical reasoning to simplify solving problems in Putnam questions (a hard benchmark). Our findings raise challenges for AI safety work that relies on monitoring CoT to detect undesired behavior.

Ireland senator weighs in on bill to'restrict' speech for the'common good.' (Credit: Houses of the Oireachtas, June 13, 2023) Ireland's government condemned the recent burning of a hotel meant to house 70 migrants outside of Galway in the west of the country as a suspected arson attack. "I am deeply concerned about recent reports of suspected criminal damage at a number of properties around the country which have been earmarked for accommodating those seeking international protection here, including in County Galway last night," Prime Minister Leo Varadkar said in a statement Sunday. "There is no justification for violence, arson or vandalism in our Republic. Garda [police] investigations are underway." The statement came in response to a fire that erupted Saturday night at the Ross Lake House Hotel in Rosscahill, County Galway, in the west of Ireland, destroying the building.

Cameras are the primary sensor in automated driving systems. They provide high information density and are optimal for detecting road infrastructure cues laid out for human vision. Surround-view camera systems typically comprise of four fisheye cameras with 190{\deg}+ field of view covering the entire 360{\deg} around the vehicle focused on near-field sensing. They are the principal sensors for low-speed, high accuracy, and close-range sensing applications, such as automated parking, traffic jam assistance, and low-speed emergency braking. In this work, we provide a detailed survey of such vision systems, setting up the survey in the context of an architecture that can be decomposed into four modular components namely Recognition, Reconstruction, Relocalization, and Reorganization. We jointly call this the 4R Architecture. We discuss how each component accomplishes a specific aspect and provide a positional argument that they can be synergized to form a complete perception system for low-speed automation. We support this argument by presenting results from previous works and by presenting architecture proposals for such a system. Qualitative results are presented in the video at https://youtu.be/ae8bCOF77uY.

Language models (LMs) often struggle to pay enough attention to the input context, and generate texts that are unfaithful or contain hallucinations. To mitigate this issue, we present context-aware decoding (CAD), which follows a contrastive output distribution that amplifies the difference between the output probabilities when a model is used with and without context. Our experiments show that CAD, without additional training, significantly improves the faithfulness of different LM families, including OPT, GPT, LLaMA and FLAN-T5 for summarization tasks (e.g., 14.3% gain for LLaMA in factuality metrics). Furthermore, CAD is particularly effective in overriding a model's prior knowledge when it contradicts the provided context, leading to substantial improvements in tasks where resolving the knowledge conflict is essential.

Current abstractive summarization systems present important weaknesses which prevent their deployment in real-world applications, such as the omission of relevant information and the generation of factual inconsistencies (also known as hallucinations). At the same time, automatic evaluation metrics such as CTC scores have been recently proposed that exhibit a higher correlation with human judgments than traditional lexical-overlap metrics such as ROUGE. In this work, we intend to close the loop by leveraging the recent advances in summarization metrics to create quality-aware abstractive summarizers. Namely, we propose an energy-based model that learns to re-rank summaries according to one or a combination of these metrics. We experiment using several metrics to train our energy-based re-ranker and show that it consistently improves the scores achieved by the predicted summaries. Nonetheless, human evaluation results show that the re-ranking approach should be used with care for highly abstractive summaries, as the available metrics are not yet sufficiently reliable for this purpose.

The series, which is produced by BBC StoryWorks Commercial Productions and presented by the International Science Council (ISC), includes films, articles and podcasts which will be hosted on a dedicated BBC.com StoryWorks webpage. The series explores how scientific culture is changing for the better, towards a future of more effective and inclusive citizen engagement, interdisciplinary and international cooperation, and open knowledge-sharing. This five-minute film highlights the innovative use of shipwrecks to map the seabed to inform the siting of offshore windfarms as the seas around Ireland provide an abundance of wind resources. Shipwrecks disturb near-seabed currents, causing certain types of sediments to be washed away or eroded. By studying these changes, we can better predict how man-made structures including wind turbines, will behave on the seabed over time.

We introduce a visual motion segmentation method employing spherical geometry for fisheye cameras and automoated driving. Three commonly used geometric constraints in pin-hole imagery (the positive height, positive depth and epipolar constraints) are reformulated to spherical coordinates, making them invariant to specific camera configurations as long as the camera calibration is known. A fourth constraint, known as the anti-parallel constraint, is added to resolve motion-parallax ambiguity, to support the detection of moving objects undergoing parallel or near-parallel motion with respect to the host vehicle. A final constraint constraint is described, known as the spherical three-view constraint, is described though not employed in our proposed algorithm. Results are presented and analyzed that demonstrate that the proposal is an effective motion segmentation approach for direct employment on fisheye imagery.

It's karaoke-rehearsal time at Knollwood Military Retirement Community, a 300-bed facility tucked away in a leafy corner of northwest Washington, D.C. Knollwood resident and retired U.S. Army Colonel Phil Soriano, 86, has hosted the facility's semi-monthly singalongs since their debut during a boozy snowstorm happy hour in 2016. For the late August 2019 show, he'll share emcee duties with a special guest: Stevie, a petite and personable figure who's been living at Knollwood for the last six weeks. Soriano wants to sing the crowd-pleasing hit "YMCA" while Stevie leads the crowd through the song's signature dance moves. But Stevie is a robot, and this is harder than it sounds. "We could try to make him dance," says Niamh Donnelly, the robot's lead AI engineer, though she sounds dubious. She enters commands on a laptop.

It's karaoke-rehearsal time at Knollwood Military Retirement Community, a 300-bed facility tucked away in a leafy corner of northwest Washington, D.C. Knollwood resident and retired U.S. Army Colonel Phil Soriano, 86, has hosted the facility's semi-monthly singalongs since their debut during a boozy snowstorm happy hour in 2016. For the late August 2019 show, he'll share emcee duties with a special guest: Stevie, a petite and personable figure who's been living at Knollwood for the last six weeks. Soriano wants to sing the crowd-pleasing hit "YMCA" while Stevie leads the crowd through the song's signature dance moves. But Stevie is a robot, and this is harder than it sounds. "We could try to make him dance," says Niamh Donnelly, the robot's lead AI engineer, though she sounds dubious. She enters commands on a laptop.