From bare interactions, low--energy constants and unitary gas to nuclear density functionals without free parameters: application to neutron matter

We further progress along the line of Ref. [Phys. Rev. {\bf A 94}, 043614 (2016)] where a functional for Fermi systems with anomalously large $s$-wave scattering length $a_s$ was proposed that has no free parameters. The functional is designed to correctly reproduce the unitary limit in Fermi gases together with the leading-order contributions in the s- and p-wave channels at low density. The functional is shown to be predictive up to densities $\sim0.01$ fm$^{-3}$ that is much higher densities compared to the Lee-Yang functional, valid for $\rho < 10^{-6}$ fm$^{-3}$. The form of the functional retained in this work is further motivated. It is shown that the new functional corresponds to an expansion of the energy in $(a_s k_F)$ and $(r_e k_F)$ to all orders, where $r_e$ is the effective range and $k_F$ is the Fermi momentum. One conclusion from the present work is that, except in the extremely low--density regime, nuclear systems can be treated perturbatively in $-(a_s k_F)^{-1}$ with respect to the unitary limit. Starting from the functional, we introduce density--dependent scales and show that scales associated to the bare interaction are strongly renormalized by medium effects. As a consequence, some of the scales at play around saturation are dominated by the unitary gas properties and not directly to low-energy constants. For instance, we show that the scale in the s-wave channel around saturation is proportional to the so-called Bertsch parameter $\xi_0$ and becomes independent of $a_s$. We also point out that these scales are of the same order of magnitude than those empirically obtained in the Skyrme energy density functional. We finally propose a slight modification of the functional such that it becomes accurate up to the saturation density $\rho\simeq 0.16$ fm$^{-3}$

Zero-Shot Video Question Answering via Frozen Bidirectional Language Models

Video question answering (VideoQA) is a complex task that requires diverse multi-modal data for training. Manual annotation of question and answers for videos, however, is tedious and prohibits scalability. To tackle this problem, recent methods consider zero-shot settings with no manual annotation of visual question-answer. In particular, a promising approach adapts frozen autoregressive language models pretrained on Web-scale text-only data to multi-modal inputs. In contrast, we here build on frozen bidirectional language models (BiLM) and show that such an approach provides a stronger and cheaper alternative for zero-shot VideoQA. In particular, (i) we combine visual inputs with the frozen BiLM using light trainable modules, (ii) we train such modules using Web-scraped multi-modal data, and finally (iii) we perform zero-shot VideoQA inference through masked language modeling, where the masked text is the answer to a given question. Our proposed approach, FrozenBiLM, outperforms the state of the art in zero-shot VideoQA by a significant margin on a variety of datasets, including LSMDC-FiB, iVQA, MSRVTT-QA, MSVD-QA, ActivityNet-QA, TGIF-FrameQA, How2QA and TVQA. It also demonstrates competitive performance in the few-shot and fully-supervised setting. Our code and models are publicly available at https://github.com/antoyang/FrozenBiLM.Comment: NeurIPS 2022 Camera-Ready; Project Webpage: https://antoyang.github.io/frozenbilm.html; 25 pages; 5 figure

Interaction Embeddings for Prediction and Explanation in Knowledge Graphs

Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions --- bi-directional effects between entities and relations --- help select related information when predicting a new triple, but haven't been formally discussed before. In this paper, we propose CrossE, a novel knowledge graph embedding which explicitly simulates crossover interactions. It not only learns one general embedding for each entity and relation as most previous methods do, but also generates multiple triple specific embeddings for both of them, named interaction embeddings. We evaluate embeddings on typical link prediction tasks and find that CrossE achieves state-of-the-art results on complex and more challenging datasets. Furthermore, we evaluate embeddings from a new perspective --- giving explanations for predicted triples, which is important for real applications. In this work, an explanation for a triple is regarded as a reliable closed-path between the head and the tail entity. Compared to other baselines, we show experimentally that CrossE, benefiting from interaction embeddings, is more capable of generating reliable explanations to support its predictions.Comment: This paper is accepted by WSDM201

A novel high mobility group box 1 neutralizing chimeric antibody attenuates drug-induced liver injury and postinjury inflammation in mice

Acetaminophen (APAP) overdoses are of major clinical concern. Growing evidence underlines a pathogenic contribution of sterile postinjury inflammation in APAP‐induced acute liver injury (APAP‐ALI) and justifies development of anti‐inflammatory therapies with therapeutic efficacy beyond the therapeutic window of the only current treatment option, N‐acetylcysteine (NAC). The inflammatory mediator, high mobility group box 1 (HMGB1), is a key regulator of a range of liver injury conditions and is elevated in clinical and preclinical APAP‐ALI. The anti‐HMGB1 antibody (m2G7) is therapeutically beneficial in multiple inflammatory conditions, and anti‐HMGB1 polyclonal antibody treatment improves survival in a model of APAP‐ALI. Herein, we developed and investigated the therapeutic efficacy of a partly humanized anti‐HMGB1 monoclonal antibody (mAb; h2G7) and identified its mechanism of action in preclinical APAP‐ALI. The mouse anti‐HMGB1 mAb (m2G7) was partly humanized (h2G7) by merging variable domains of m2G7 with human antibody‐Fc backbones. Effector function‐deficient variants of h2G7 were assessed in comparison with h2G7 in vitro and in preclinical APAP‐ALI. h2G7 retained identical antigen specificity and comparable affinity as m2G7. 2G7 treatments significantly attenuated APAP‐induced serum elevations of alanine aminotransferase and microRNA‐122 and completely abrogated markers of APAP‐induced inflammation (tumor necrosis factor, monocyte chemoattractant protein 1, and chemokine [C‐X‐C motif] ligand 1) with prolonged therapeutic efficacy as compared to NAC. Removal of complement and/or Fc receptor binding did not affect h2G7 efficacy. Conclusion: This is the first report describing the generation of a partly humanized HMGB1‐neutralizing antibody with validated therapeutic efficacy and with a prolonged therapeutic window, as compared to NAC, in APAP‐ALI. The therapeutic effect was mediated by HMGB1 neutralization and attenuation of postinjury inflammation. These results represent important progress toward clinical implementation of HMGB1‐specific therapy as a means to treat APAP‐ALI and other inflammatory conditions. (Hepatology 2016;64:1699‐1710)

Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs

We present a new type of acquisition functions for online decision making in multi-armed and contextual bandit problems with extreme payoffs. Specifically, we model the payoff function as a Gaussian process and formulate a novel type of upper confidence bound (UCB) acquisition function that guides exploration towards the bandits that are deemed most relevant according to the variability of the observed rewards. This is achieved by computing a tractable likelihood ratio that quantifies the importance of the output relative to the inputs and essentially acts as an \textit{attention mechanism} that promotes exploration of extreme rewards. We demonstrate the benefits of the proposed methodology across several synthetic benchmarks, as well as a realistic example involving noisy sensor network data. Finally, we provide a JAX library for efficient bandit optimization using Gaussian processes.Comment: 10 pages, 4 figures, 1 tabl

Stability and convergence analysis of artificial boundary conditions for the Schrödinger equation on a rectangular domain

Based on the discrete artificial boundary condition introduced in [16] for the two-dimensional free Schrödinger equation in a computational rectangular domain, we propose to analyze the stability and convergence rate of the resulting full scheme. We prove that the global scheme is L 2-stable and that the accuracy is second-order in time, confirming then the numerical results reported in [16]

Searching for Embeddings in a Haystack:Link Prediction on Knowledge Graphs with Subgraph Pruning

Embedding-based models of Knowledge Graphs (KGs) can be used to predict the existence of missing links by ranking the entities according to some likelihood scores. An exhaustive computation of all likelihood scores is very expensive if the KG is large. To counter this problem, we propose a technique to reduce the search space by identifying smaller subsets of promising entities. Our technique first creates embeddings of subgraphs using the embeddings from the model. Then, it ranks the subgraphs with some proposed ranking functions and considers only the entities in the top k subgraphs. Our experiments show that our technique is able to reduce the search space significantly while maintaining a good recall

VidChapters-7M: Video Chapters at Scale

Segmenting long videos into chapters enables users to quickly navigate to the information of their interest. This important topic has been understudied due to the lack of publicly released datasets. To address this issue, we present VidChapters-7M, a dataset of 817K user-chaptered videos including 7M chapters in total. VidChapters-7M is automatically created from videos online in a scalable manner by scraping user-annotated chapters and hence without any additional manual annotation. We introduce the following three tasks based on this data. First, the video chapter generation task consists of temporally segmenting the video and generating a chapter title for each segment. To further dissect the problem, we also define two variants of this task: video chapter generation given ground-truth boundaries, which requires generating a chapter title given an annotated video segment, and video chapter grounding, which requires temporally localizing a chapter given its annotated title. We benchmark both simple baselines and state-of-the-art video-language models for these three tasks. We also show that pretraining on VidChapters-7M transfers well to dense video captioning tasks in both zero-shot and finetuning settings, largely improving the state of the art on the YouCook2 and ViTT benchmarks. Finally, our experiments reveal that downstream performance scales well with the size of the pretraining dataset. Our dataset, code, and models are publicly available at https://antoyang.github.io/vidchapters.html.Comment: Accepted at NeurIPS 2023 Track on Datasets and Benchmarks; Project Webpage: https://antoyang.github.io/vidchapters.html ; 31 pages; 8 figure