Canada's Carney has enjoyed a long political honeymoon. Mark Carney arrived on Canada's political scene last year as an Ivy League and Oxford educated economist and a former central banker for two countries. He had an impressive resume and ambitions to be prime minister but had never run for public office until replacing Justin Trudeau as Liberal leader. There was concern his lack of political experience would be a liability, but under his leadership, the Liberals won a minority government, which in a year had solidified into a narrow majority following the defection of five opposition members of parliament to his party. Carney tore up the rulebook, jumping from political neophyte to leading a G7 nation, and he is enjoying a lengthy honeymoon both in Canada and around the world as a globetrotting prime minister.

Understanding the neural basis of behavior is a fundamental goal in neuroscience. Current research in large-scale neuro-behavioral data analysis often relies on decoding models, which quantify behavioral information in neural data but lack details on behavior encoding. This raises an intriguing scientific question: how can we enable in-depth exploration of neural representations in behavioral tasks, revealing interpretable neural dynamics associated with behaviors. However, addressing this issue is challenging due to the varied behavioral encoding across different brain regions and mixed selectivity at the population level. To tackle this limitation, our approach, named (BeNeDiff), first identifies a fine-grained and disentangled neural subspace using a behavior-informed latent variable model. It then employs state-of-the-art generative diffusion models to synthesize behavior videos that interpret the neural dynamics of each latent factor.

Kevin Durant is expected to sit out Game 4 with a sprained left ankle and bone bruise, as the Rockets face elimination against the Lakers on Sunday night.

China's economy was just starting to open up in the late 1980s when a determined high school dropout made his way to Beijing with 600 pairs of shoes. Ding Shizhong had them made in a relative's factory and now he was going to sell them. The money he earned paid for his first workshop where he began making footwear for other companies. The 17-year-old was one of China's many newly minted entrepreneurs as capitalism took off under the watchful eye of its Communist Party rulers. But, as it turns out, Ding had much bigger plans.

We chose the Google Search corpus [Luo et al., 2021] for our question-answering system as it provides good coverage of the knowledge needed and is publicly available. However, as noted by the authors of RA-VQA, additional knowledge bases may be required to answer some questions correctly. Future work may address the issue by improving the quality and expanding the coverage of knowledge. We do not perceive any immediate ethical concerns associated with the misuse of our proposed system. There is a possibility that the trained KB-VQA system might generate inappropriate or biased content as a result of the training data biases during LLM and LMM pre-training and fine-tuning.

Lebanon's Ministry of Health has said Israeli strikes on the country on Sunday killed 14 people, including two children and two women, and injured 37. An Israel Defense Forces (IDF) spokesperson had earlier issued evacuation warnings for several villages in southern Lebanon, writing that residents must evacuate immediately, and that staying would be endangering their life. The IDF later said it had carried out artillery and aerial strikes targeting Hezbollah operatives and sites in southern Lebanon that it claims were used to advance attacks against IDF soldiers. It also said a 19-year-old IDF soldier had been killed and six others injured by a Hezbollah drone attack in Lebanon. Separately, Hezbollah launched three drones towards Israel, the IDF reported, which it said were intercepted by Israel's air force before they crossed the border.

Recent advances in the design of neural network architectures, in particular those specialized in modeling sequences, have provided significant improvements in speech separation performance. In this work, we propose to use a bio-inspired architecture called Fully Recurrent Convolutional Neural Network (FRCNN) to solve the separation task. This model contains bottom-up, top-down and lateral connections to fuse information processed at various time-scales represented by stages. In contrast to the traditional approach updating stages in parallel, we propose to first update the stages one by one in the bottom-up direction, then fuse information from adjacent stages simultaneously and finally fuse information from all stages to the bottom stage together. Experiments showed that this asynchronous updating scheme achieved significantly better results with much fewer parameters than the traditional synchronous updating scheme. In addition, the proposed model achieved good balance between speech separation accuracy and computational efficiency as compared to other state-of-the-art models on three benchmark datasets.

Episodic event memory is a key component of human cognition. Predicting event memorability, i.e., to what extent an event is recalled, is a tough challenge in memory research and has profound implications for artificial intelligence. In this study, we investigate factors that affect event memorability according to a cued recall process. Specifically, we explore whether event memorability is contingent on the event context, as well as the intrinsic visual attributes of image cues. We design a novel experiment protocol and conduct a large-scale experiment with 47 elder subjects over 3 months. Subjects' memory of life events is tested in a cued recall process. Using advanced visual analytics methods, we build a first-ofits-kind event memorability dataset (called R3) with rich information about event context and visual semantic features. Furthermore, we propose a contextual event memory network (CEMNet) that tackles multi-modal input to predict item-wise event memorability, which outperforms competitive benchmarks. The findings inform deeper understanding of episodic event memory, and open up a new avenue for prediction of human episodic memory.

As large language models (LLMs) develop increasingly sophisticated capabilities and find applications in medical settings, it becomes important to assess their medical safety due to their far-reaching implications for personal and public health, patient safety, and human rights. However, there is little to no understanding of the notion of medical safety in the context of LLMs, let alone how to evaluate and improve it. To address this gap, we first define the notion of medical safety in LLMs based on the Principles of Medical Ethics set forth by the American Medical Association. We then leverage this understanding to introduce MedSafetyBench, the first benchmark dataset designed to measure the medical safety of LLMs. We demonstrate the utility of MedSafetyBench by using it to evaluate and improve the medical safety of LLMs. Our results show that publicly-available medical LLMs do not meet standards of medical safety and that fine-tuning them using MedSafetyBench improves their medical safety while preserving their medical performance. By introducing this new benchmark dataset, our work enables a systematic study of the state of medical safety in LLMs and motivates future work in this area, paving the way to mitigate the safety risks of LLMs in medicine.