Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA

Retrieving evidences from tabular and textual resources is essential for open-domain question answering (OpenQA), which provides more comprehensive information. However, training an effective dense table-text retriever is difficult due to the challenges of table-text discrepancy and data sparsity problem. To address the above challenges, we introduce an optimized OpenQA Table-Text Retriever (OTTeR) to jointly retrieve tabular and textual evidences. Firstly, we propose to enhance mixed-modality representation learning via two mechanisms: modality-enhanced representation and mixed-modality negative sampling strategy. Secondly, to alleviate data sparsity problem and enhance the general retrieval ability, we conduct retrieval-centric mixed-modality synthetic pre-training. Experimental results demonstrate that OTTeR substantially improves the performance of table-and-text retrieval on the OTT-QA dataset. Comprehensive analyses examine the effectiveness of all the proposed mechanisms. Besides, equipped with OTTeR, our OpenQA system achieves the state-of-the-art result on the downstream QA task, with 10.1% absolute improvement in terms of the exact match over the previous best system. All the code and data are available at https://github.com/Jun-jie-Huang/OTTeR.
Capturing Global Structural Information in Long Document Question Answering with Compressive Graph Selector Network

Long document question answering is a challenging task due to its demands for complex reasoning over long text. Previous works usually take long documents as non-structured flat texts or only consider the local structure in long documents. However, these methods usually ignore the global structure of the long document, which is essential for long-range understanding. To tackle this problem, we propose Compressive Graph Selector Network (CGSN) to capture the global structure in a compressive and iterative manner. The proposed model mainly focuses on the evidence selection phase of long document question answering. Specifically, it consists of three modules: local graph network, global graph network and evidence memory network. Firstly, the local graph network builds the graph structure of the chunked segment in token, sentence, paragraph and segment levels to capture the short-term dependency of the text. Secondly, the global graph network selectively receives the information of each level from the local graph, compresses them into the global graph nodes and applies graph attention to the global graph nodes to build the long-range reasoning over the entire text in an iterative way. Thirdly, the evidence memory network is designed to alleviate the redundancy problem in the evidence selection by saving the selected result in the previous steps. Extensive experiments show that the proposed model outperforms previous methods on two datasets.
What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries

Deep neural network classifiers partition input space into high confidence regions for each class. The geometry of these class manifolds (CMs) is widely studied and intimately related to model performance; for example, the margin depends on CM boundaries. We exploit the notions of Gaussian width and Gordon's escape theorem to tractably estimate the effective dimension of CMs and their boundaries through tomographic intersections with random affine subspaces of varying dimension. We show several connections between the dimension of CMs, generalization, and robustness. In particular we investigate how CM dimension depends on 1) the dataset, 2) architecture (including ResNet, WideResNet \&Vision Transformer), 3) initialization, 4) stage of training, 5) class, 6) network width, 7) ensemble size, 8) label randomization, 9) training set size, and 10) robustness to data corruption. Together a picture emerges that higher performing and more robust models have higher dimensional CMs. Moreover, we offer a new perspective on ensembling via intersections of CMs. Our code is at https://github.com/stanislavfort/slice-dice-optimize/
Multiple-time-scale Landau-Zener transitions in many-body systems

Motivated by recent cold-atom experiments in optical lattices, we consider a lattice version of the Landau-Zener problem. Every single site is described by a Landau-Zener problem, but due to particle tunneling between neighboring lattice sites this on-site single-particle Landau-Zener dynamics couples to the particle motion within the lattice. The lattice, apart from having a dephasing effect on single-site Landau-Zener transitions, also implies, in the presence of a confining trap, an intersite particle flow induced by the Landau-Zener sweeping. This gives rise to an interplay between intra-and intersite dynamics. The adiabaticity constraint is therefore not simply given by the standard one, the Hamiltonian rate of change relative to the gap of the on-site problem. In experimentally realistic situations, the full system evolution is well described by Franck-Condon physics; e.g., nonadiabatic excitations are predominantly external ones characterized by large phononic vibrations in the atomic cloud, while internal excitations are very weak as close-to-perfect on-site transitions take place.
Commitments to Quantum States

What does it mean to commit to a quantum state? In this work, we propose a simple answer: a commitment to quantum messages is binding if, after the commit phase, the committed state is hidden from the sender's view. We accompany this new definition with several instantiations. We build the first non-interactive succinct quantum state commitments, which can be seen as an analogue of collision-resistant hashing for quantum messages. We also show that hiding quantum state commitments (QSCs) are implied by any commitment scheme for classical messages. All of our constructions can be based on quantum-cryptographic assumptions that are implied by but are potentially weaker than one-way functions. Commitments to quantum states open the door to many new cryptographic possibilities. Our flagship application of a succinct QSC is a quantum-communication version of Kilian's succinct arguments for any language that has quantum PCPs with constant error and polylogarithmic locality. Plugging in the PCP theorem, this yields succinct arguments for NP under significantly weaker assumptions than required classically; moreover, if the quantum PCP conjecture holds, this extends to QMA. At the heart of our security proof is a new rewinding technique for extracting quantum information.
QED on the lattice and numerical perturbative computation of $g-2$

We compute the electron $g$ factor to the $\mathcal{O}(\alpha^5)$ order on the lattice in quenched QED. We first study finite volume corrections in various IR regularization methods to discuss which regularization is optimal for our purpose. We find that in QED$_L$ the finite volume correction to the effective mass can have different parametric dependences depending on the size of Euclidean time $t$ and match the `naive on-shell result' only at very large $t$ region, $t \gg L$. We adopt finite photon mass regularization to suppress finite volume effects exponentially and also discuss our strategy for selecting simulation parameters and the order of extrapolations to efficiently obtain the $g$ factor. We perform lattice simulation using small lattices to test feasibility of our calculation strategy. This study can be regarded as an intermediate step toward giving the five-loop coefficient independently of the preceding studies.
Pre-Training for Robots: Offline RL Enables Learning New Tasks in a Handful of Trials

Progress in deep learning highlights the tremendous potential of utilizing diverse robotic datasets for attaining effective generalization and makes it enticing to consider leveraging broad datasets for attaining robust generalization in robotic learning as well. However, in practice, we often want to learn a new skill in a new environment that is unlikely to be contained in the prior data. Therefore we ask: how can we leverage existing diverse offline datasets in combination with small amounts of task-specific data to solve new tasks, while still enjoying the generalization benefits of training on large amounts of data? In this paper, we demonstrate that end-to-end offline RL can be an effective approach for doing this, without the need for any representation learning or vision-based pre-training. We present pre-training for robots (PTR), a framework based on offline RL that attempts to effectively learn new tasks by combining pre-training on existing robotic datasets with rapid fine-tuning on a new task, with as few as 10 demonstrations. PTR utilizes an existing offline RL method, conservative Q-learning (CQL), but extends it to include several crucial design decisions that enable PTR to actually work and outperform a variety of prior methods. To our knowledge, PTR is the first RL method that succeeds at learning new tasks in a new domain on a real WidowX robot with as few as 10 task demonstrations, by effectively leveraging an existing dataset of diverse multi-task robot data collected in a variety of toy kitchens. We also demonstrate that PTR can enable effective autonomous fine-tuning and improvement in a handful of trials, without needing any demonstrations. An accompanying overview video can be found in the supplementary material and at thi URL: https://sites.google. com/view/ptr-final/
Multi-Agent Distributed and Decentralized Geometric Task Allocation

We consider the general problem of geometric task allocation, wherein a large, decentralized swarm of simple mobile agents must detect the locations of tasks in the plane and position themselves nearby. The tasks are represented by an a priori unknown demand profile Φ(x,y) that determines how many agents are needed in each location. The agents are autonomous, oblivious, indistinguishable, and have a finite sensing range. They must configure themselves according to Φ using only local information about Φ and about the positions of nearby agents. All agents act according to the same local sensing-based rule of motion, and cannot explicitly communicate nor share information. We propose an approach based on gradient descent over a simple squared error function. We formally show that this approach results in attraction-repulsion dynamics. Repulsion encourages agents to spread out and explore the region to find the tasks, and attraction causes them to accumulate at task locations. The figures in this work are snapshots of simulations that can be viewed at https://youtu.be/1_5f0MnUJag.
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity

$\ell_0$ constrained optimization is prevalent in machine learning, particularly for high-dimensional problems, because it is a fundamental approach to achieve sparse learning. Hard-thresholding gradient descent is a dominant technique to solve this problem. However, first-order gradients of the objective function may be either unavailable or expensive to calculate in a lot of real-world problems, where zeroth-order (ZO) gradients could be a good surrogate. Unfortunately, whether ZO gradients can work with the hard-thresholding operator is still an unsolved problem. To solve this puzzle, in this paper, we focus on the $\ell_0$ constrained black-box stochastic optimization problems, and propose a new stochastic zeroth-order gradient hard-thresholding (SZOHT) algorithm with a general ZO gradient estimator powered by a novel random support sampling. We provide the convergence analysis of SZOHT under standard assumptions. Importantly, we reveal a conflict between the deviation of ZO estimators and the expansivity of the hard-thresholding operator, and provide a theoretical minimal value of the number of random directions in ZO gradients. In addition, we find that the query complexity of SZOHT is independent or weakly dependent on the dimensionality under different settings. Finally, we illustrate the utility of our method on a portfolio optimization problem as well as black-box adversarial attacks.
Formalizing May's Theorem

This report presents a formalization of May's theorem in the proof assistant Coq. It describes how the theorem statement is first translated into Coq definitions, and how it is subsequently proved. Various aspects of the proof and related work are discussed. To the best of the author's knowledge, this project is the first documented attempt in mechanizing May's Theorem.
Unifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition

—Combining skeleton structure with graph convolutional networks has achieved remarkable performance in human action recognition. Since current research focuses on designing basic graph for representing skeleton data, these embedding fea- tures contain basic topological information, which cannot learn more systematic perspectives from skeleton data. In this paper, we overcome this limitation by proposing a novel framework, which uniﬁes 15 graph embedding features into the graph convolutional network for human action recognition, aiming to best take advantage of graph information to distinguish key joints, bones, and body parts in human action, instead of being exclusive to a single feature or domain. Additionally, we fully investigate how to ﬁnd the best graph features of skeleton structure for improving human action recognition. Besides, the topological information of the skeleton sequence is explored to further enhance the performance in a multi-stream framework. Moreover, the uniﬁed graph features are extracted by the adaptive methods on the training process, which further yields improvements. Our model is validated by three large-scale datasets, namely NTU-RGB+D, Kinetics and SYSU-3D, and outperforms the state-of-the-art methods. Overall, our work uniﬁed graph embedding features to promotes systematic research on human action recognition.
Functional Constrained Optimization for Risk Aversion and Sparsity Control

Risk and sparsity requirements often need to be enforced simultaneously in many applications, e.g., in portfolio optimization, assortment planning, and treatment planning. Properly balancing these potentially conflicting requirements entails the formulation of functional constrained optimization with either convex or nonconvex objectives. In this paper, we focus on projection-free methods that can generate a sparse trajectory for solving these challenging functional constrained optimization problems. Specifically, for the convex setting, we propose a Level Conditional Gradient (LCG) method, which leverages a level-set framework to update the approximation of the optimal value and an inner conditional gradient oracle (CGO) for solving mini-max subproblems. We show that the method achieves O( 1 ǫ2 log 1 ǫ ) iteration complexity for solving both smooth and nonsmooth cases without dependency on a possibly large size of optimal dual Lagrange multiplier. For the nonconvex setting, we introduce the Level Inexact Proximal Point (IPP-LCG) method and the Direct Nonconvex Conditional Gradient (DNCG) method. The first approach taps into the advantage of LCG by transforming the problem into a series of convex subproblems and exhibits an O( 1 ǫ3 log 1 ǫ ) iteration complexity for finding an (ǫ, ǫ)-KKT point. The DNCG is the first single-loop projection-free method, with iteration complexity bounded by O(1/ǫ) for computing a so-called ǫ-Wolfe point. We demonstrate the effectiveness of LCG, IPP-LCG and DNCG by devising formulations and conducting numerical experiments on two risk averse sparse optimization applications: a portfolio selection problem with and without cardinality requirement, and a radiation therapy planning problem in the healthcare area.
Bond diluted Levy spin-glass model and a new finite-size scaling method to determine a phase transition

A spin-glass transition occurs both within and outside of the limits of validity of mean-field theory for a diluted one-dimensional chain of Ising spins where exchange bonds occur with a probability decaying as the inverse power of the distance. Varying the power in this long-range model corresponds, in a one-to-one relationship, to changing the dimension in spin-glass short-range models. Using different finite-size scaling methods, evidence for a spin-glass transition is also found for systems whose equivalent dimension is below the upper critical dimension at zero magnetic field. The application of a new method is discussed, which can be exported to systems in a magnetic field.
Shotgun-2: A Bayesian phase I/II basket trial design to identify indication-specific optimal biological doses

For novel molecularly targeted agents and immunotherapies, the objective of dose-finding is often to identify the optimal biological dose, rather than the maximum tolerated dose. However, optimal biological doses may not be the same for different indications, challenging the traditional dose-finding framework. Therefore, we proposed a Bayesian phase I/II basket trial design, named “shotgun-2,” to identify indication-specific optimal biological doses. A dose-escalation part is conducted in stage I to identify the maximum tolerated dose and admissible dose sets. In stage II, dose optimization is performed incorporating both toxicity and efficacy for each indication. Simulation studies under both fixed and random scenarios show that, compared with the traditional “phase I + cohort expansion” design, the shotgun-2 design is robust and can improve the probability of correctly selecting the optimal biological doses. Furthermore, this study provides a useful tool for identifying indication-specific optimal biological doses and accelerating drug development.
Feminist Social Vision: Seeing Through the Lens of Marginalized Perceivers

Social vision research, which examines, in part, how humans visually perceive social stimuli, is well-positioned to improve understandings of social inequality. However, social vision research has rarely prioritized the perspectives of marginalized group members. We offer a theoretical argument for diversifying understandings of social perceptual processes by centering marginalized perspectives. We examine (a) how social vision researchers frame their research questions and who these framings prioritize and (b) how perceptual processes (person perception; people perception; perception of social objects) are linked to group membership and thus comprehensively understanding these processes necessitates attention to marginalized perceivers. We discuss how social vision research translates into theoretical advances and to action for reducing negative intergroup consequences (e.g., prejudice). The purpose of this article is to delineate how prioritizing marginalized perspectives in social vision research could develop novel questions, bridge theoretical gaps, and elevate social vision’s translational impact to improve outcomes for marginalized groups. Public Abstract Social vision research is a subfield of psychology and vision science which examines how people visually perceive social stimuli and what the downstream consequences of these perceptions are. Social vision work includes, for example, examination of how White people visually perceive racial minorities and how these perceptions lead to social categorizations of racial minorities as outgroups, and therefore contribute to behaviors such as stereotyping and prejudice. Social vision research has rarely prioritized the perspectives of marginalized group members. It therefore cannot fully explain the contributions of perception to intergroup relations, which are necessarily bidirectional. We offer a theoretical argument for diversifying understandings of social perceptual processes by centering marginalized perspectives to understand how people with marginalized identities see their social worlds. We believe that prioritizing these marginalized perspectives has the potential to contribute to the development of a psychological science with heightened capacity to improve the well-being of people with marginalized identities.
Food insecurity and level of depression among patients with chronic diseases, and associated factors during the COVID-19 lockdown: a cross-sectional study in rural Rwanda

Objectives We aimed to describe access to food and symptoms of depression among patients with chronic diseases or their caregivers, and assess associated factors during the COVID-19 lockdown in rural Rwanda. Design A cross-sectional study. Setting and participants A stratified random sampling technique was used to recruit 220 patients enrolled in the HIV, non-communicable diseases, mental health, paediatric development clinic and oncology programmes in three rural districts of Rwanda. Outcome measures Telephone-based interviews were conducted to collect data on the number of daily meals before and during the COVID-19, and depression was assessed using the Patient Health Questionnaire-9. We used logistic regression analysis to investigate factors associated with households reporting a reduction in daily meals and with the survey respondent reporting symptoms of depression. Results Of the participants, 19.1% reported a reduction in daily number of meals for either adults or children in their households during lockdown and 24.6% had depression. Reporting a reduction in daily meals was associated with the district of residence and estimated household’s monthly income. Self-reported depression was significantly associated with negative experiences during lockdown, including reporting feeling depressed or fear (AOR 4.82; 95% CI 2.08 to 11.21), loneliness (AOR 4.33; 95% CI 1.32 to 14.13), reduction in daily meals (AOR 4.15; 95% CI 1.56 to 11.00) and lack of access to healthcare (OR 3.29; 95% CI 1.32 to 8.23). Conclusions Our findings suggest that significant reduction in access to food affected rural Rwandans with chronic diseases during COVID-19 lockdown, and the lockdown effect varied by household’s pre-pandemic level of vulnerability to food insecurity. Reduction in household meals, as well as other self-reported effects of the lockdown, were associated with worse psychological status of survey respondents. Economic and food support should be considered by governments and non-governmental organisations to protect those most vulnerable including patients with chronic diseases against the effects of pandemics and their associated containment measures.