Miracle-dz/UniFAR-FTUs huggingface dataset:
A Note on Zeroth-Order Optimization on the Simplex

We construct a zeroth-order gradient estimator for a smooth function defined on the probability simplex. The proposed estimator queries the simplex only. We prove that projected gradient descent and the exponential weights algorithm, when run with this estimator instead of exact gradients, converge at a $\mathcal O(T^{-1/4})$ rate.
Joint Learning-based Causal Relation Extraction from Biomedical Literature

Causal relation extraction of biomedical entities is one of the most complex tasks in biomedical text mining, which involves two kinds of information: entity relations and entity functions. One feasible approach is to take relation extraction and function detection as two independent sub-tasks. However, this separate learning method ignores the intrinsic correlation between them and leads to unsatisfactory performance. In this paper, we propose a joint learning model, which combines entity relation extraction and entity function detection to exploit their commonality and capture their inter-relationship, so as to improve the performance of biomedical causal relation extraction. Experimental results on the BioCreative-V Track 4 corpus show that our joint learning model outperforms the separate models in BEL statement extraction, achieving the F1 scores of 57.0% and 37.3% on the test set in Stage 2 and Stage 1 evaluations, respectively. This demonstrates that our joint learning system reaches the state-of-the-art performance in Stage 2 compared with other systems.
Fermi Level Depinning in Two-Dimensional Materials Using a Fluorinated Bilayer Graphene Barrier

: Strong Fermi level pinning (FLP) - often attributed to metal-induced gap states at the interfacial contacts - severely reduces the tunability of the Schottky barrier height of the junction and limits applications of the 2D materials in electronics and optoelectronics. Here, we show that fluorinated bilayer graphene (FBLG) can be used as a barrier to effectively prevent FLP at metal/2D materials interfaces. FLBG can be produced via short exposure (1-3 min) to SF 6 plasma that fluorinates only the top layer of a bilayer graphene with covalent C-F bonding, while the bottom layer remains intrinsic, resulting in a band gap opening of about 75 meV. Inserting FBLG between the metallic contacts and a layer of MoS 2 reduces the Schottky barrier height dramatically for the low-work function metals (313 and 260 meV for Ti and Cr, respectively) while it increases for the high-work function one ( 160 meV for Pd), corresponding to an improved pinning factor. Our results provide a straightforward method to generate atomically thin dielectrics with applications not only for depinning the Fermi level at metal/transition metal dichalcogenide (TMD) interfaces but also for solving many other problems in electronics and optoelectronics.
Hypothesis tests for multiple responses regression models in R: The htmcglm Package

This article describes the R package htmcglm implemented for performing hypothesis tests on regression and dispersion parameters of multivariate covariance generalized linear models (McGLMs). McGLMs provide a general statistical modeling framework for normal and non-normal multivariate data analysis along with a wide range of correlation struc-tures. The proposed package considers the Wald statistics to perform general hypothesis tests and build tailored ANOVAs, MANOVAs and multiple comparison tests. The goal of the package is to provide tools to improve the interpretation of regression and dispersion parameters. We assess the eﬀects of the covariates on the response variables by testing the regression coeﬃcients. Similarly, we perform tests on the dispersion coeﬃcients in order to assess the correlation between study units. It could be of interest in situations where the data observations are correlated with each other, such as in longitudinal, times series, spatial and repeated measures studies. The htmcglm package provides a user friendly interface to perform MANOVA like tests as well as multivariate hypothesis tests for models of the mcglm class. We describe the package implementation and illustrate it through the analysis of two data sets. The ﬁrst deals with an experiment on soybean yield; the problem has three response variables of diﬀerent types (continuous, counting and binomial) and three explanatory variables (amount of water, fertilization and block). The second dataset addresses a problem where responses are longitudinal bivariate counts of hunting animals; the explanatory variables used are the hunting method and sex of the animal. With these examples we were able to illustrate several tests in which the proposal proves to be useful for the evaluation of regression and dispersion parameters both in problems with dependent or independent observations.
A framework for high-throughput sequence alignment using real processing-in-memory systems

Abstract Motivation Sequence alignment is a memory bound computation whose performance in modern systems is limited by the memory bandwidth bottleneck. Processing-in-memory (PIM) architectures alleviate this bottleneck by providing the memory with computing competencies. We propose Alignment-in-Memory (AIM), a framework for high-throughput sequence alignment using PIM, and evaluate it on UPMEM, the first publicly available general-purpose programmable PIM system. Results Our evaluation shows that a real PIM system can substantially outperform server-grade multi-threaded CPU systems running at full-scale when performing sequence alignment for a variety of algorithms, read lengths, and edit distance thresholds. We hope that our findings inspire more work on creating and accelerating bioinformatics algorithms for such real PIM systems. Availability and implementation Our code is available at https://github.com/safaad/aim.
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning

Traditional learning-based approaches to student modeling generalize poorly to underrepresented student groups due to biases in data availability. In this paper, we propose a methodology for predicting student performance from their online learning activities that optimizes inference accuracy over different demographic groups such as race and gender. Building upon recent foundations in federated learning, in our approach, personalized models for individual student subgroups are derived from a global model aggregated across all student models via meta-gradient updates that account for subgroup heterogeneity. To learn better representations of student activity, we augment our approach with a self-supervised behavioral pretraining methodology that leverages multiple modalities of student behavior (e.g., visits to lecture videos and participation on forums), and include a neural network attention mechanism in the model aggregation stage. Through experiments on three real-world datasets from online courses, we demonstrate that our approach obtains substantial improvements over existing student modeling baselines in predicting student learning outcomes for all subgroups. Visual analysis of the resulting student embeddings confirm that our personalization methodology indeed identifies different activity patterns within different subgroups, consistent with its stronger inference ability compared with the baselines.
Concentration inequalities for correlated network-valued processes with applications to community estimation and changepoint analysis

Network-valued time series are currently a common form of network data. However, the study of the aggregate behavior of network sequences generated from network-valued stochastic processes is relatively rare. Most of the existing research focuses on the simple setup where the networks are independent (or conditionally independent) across time, and all edges are updated synchronously at each time step. In this paper, we study the concentration properties of the aggregated adjacency matrix and the corresponding Laplacian matrix associated with network sequences generated from lazy network-valued stochastic processes, where edges update asynchronously, and each edge follows a lazy stochastic process for its updates independent of the other edges. We demonstrate the usefulness of these concentration results in proving consistency of standard estimators in community estimation and changepoint estimation problems. We also conduct a simulation study to demonstrate the effect of the laziness parameter, which controls the extent of temporal correlation, on the accuracy of community and changepoint estimation.
Firewalls from wormholes

Spacetime wormholes can lead to surprises in black hole physics. We show that a very old black hole can tunnel to a white hole/ﬁrewall by emitting a large baby universe. We study the process for a perturbed thermoﬁeld double black hole in Jackiw-Teitelboim (JT) gravity, using the lowest order (genus one) spacetime wormhole that corresponds to single baby-universe emission. The probability for tunneling to a white hole is proportional to t 2 e − 2 S where t is the age of the black hole and S is the entropy of one black hole.
Extremal numbers of disjoint triangles in r-partite graphs

For two graphs $G$ and $F$, the extremal number of $F$ in $G$, denoted by {ex}$(G,F)$, is the maximum number of edges in a spanning subgraph of $G$ not containing $F$ as a subgraph. Determining {ex}$(K_n,F)$ for a given graph $F$ is a classical extremal problem in graph theory. In 1962, Erd\H{o}s determined {ex}$(K_n,kK_3)$, which generalized Mantel's Theorem. On the other hand, in 1974, {Bollob\'{a}s}, Erd\H{o}s, and Straus determined {ex}$(K_{n_1,n_2,\dots,n_r},K_t)$, which extended Tur\'{a}n's Theorem to complete multipartite graphs. { In this paper,} we determine {ex}$(K_{n_1,n_2,\dots,n_r},kK_3)$ for $r\ge 4$ and $10k-4\le n_1+4k\le n_2\le n_3\le \cdots \le n_r$.
2022 Excitation spectrum and momentum distribution of the extended Bose-Hubbard model

We investigate the extended Bose-Hubbard model with the nearest-neighbor repulsion on a square lattice by the standard basis operator method. We derive Green’s functions in random phase approximation and calculate the excitation spectrum and momentum distribution in Mott insulator, charge density wave, superﬂuid, and supersolid phases. The excitation spectrum has gapped modes and gapless Goldstone modes in the superﬂuid and supersolid phases. The momentum distribution has a peak at the zone corner in the supersolid phase and the charge density wave phase close to the phase boundary.
Exploring the GLIDE model for Human Action Effect Prediction

We address the following action-effect prediction task. Given an image depicting an initial state of the world and an action expressed in text, predict an image depicting the state of the world following the action. The prediction should have the same scene context as the input image. We explore the use of the recently proposed GLIDE model for performing this task. GLIDE is a generative neural network that can synthesize (inpaint) masked areas of an image, conditioned on a short piece of text. Our idea is to mask-out a region of the input image where the effect of the action is expected to occur. GLIDE is then used to inpaint the masked region conditioned on the required action. In this way, the resulting image has the same background context as the input image, updated to show the effect of the action. We give qualitative results from experiments using the EPIC dataset of ego-centric videos labelled with actions.
Building and maintaining hierarchical semantic nets

Knowledge intensive AI applications require the development and maintenance of increasingly large, consistent and up-to-date knowledge bases. Knowledge engineering remains largely manual, presenting a major bottleneck to the development of intelligent systems. This research explores methods to automatically build and maintain hierarchical semantic nets. Such semantic nets exist that are manually built and laboriously updated according to some rules that are imprecise at best. The approach followed is one of taking advantage of the structure existing in readily available knowledge sources to build and/or maintain hierarchical semantic nets. Three methods were investigated, dealing with knowledge sources of varying structural complexity. The first two methods, briefly summarized in this thesis, proved useful in the context of an intelligent information retrieval system that used a hierarchical semantic net to match documents to queries. However, both methods suffered, to varying degrees, from the vagueness and lack of clear semantics of hierarchical relations in man-made hierarchies. The third method, DK method, is based on a precise characterization of hierarchical relationships. The DK method is based on the observation that man-made hierarchies are often built in a way such that concepts' properties follow clear patterns, similar to the patterns implied by inheritance in taxonomies. These patterns are described by regularity, a generalized form of inheritance. It is argued that regularity is a fundamental property of hierarchies, and a model of hierarchies (DK model of hierarchies) that embodies regularity is proposed. In this model, hierarchical relations are described by predicates that represent the particular relationships that hold between the properties of a concept and the properties of its descendants. Adding a concept to a DK hierarchy is then reduced to a problem of classifying that concept using the hierarchy as a hierarchical classifier. The cognitive and mathematical foundations of this approach are discussed. In particular, a fuzzy model of DK hierarchies and its associated classification algorithm are proposed to handle the uncertainties and exceptions to regularity that often plague man-made hierarchies. The fuzzy models are then tested on two man-made hierarchies, and the results show the validity of our approach.
A Multi-body Tracking Framework - From Rigid Objects to Kinematic Structures

Kinematic structures are very common in the real world. They range from simple articulated objects to complex mechanical systems. However, despite their relevance, most model-based 3D tracking methods only consider rigid objects. To overcome this limitation, we propose a flexible framework that allows the extension of existing 6DoF algorithms to kinematic structures. Our approach focuses on methods that employ Newton-like optimization techniques, which are widely used in object tracking. The framework considers both tree-like and closed kinematic structures and allows a flexible configuration of joints and constraints. To project equations from individual rigid bodies to a multi-body system, Jacobians are used. For closed kinematic chains, a novel formulation that features Lagrange multipliers is developed. In a detailed mathematical proof, we show that our constraint formulation leads to an exact kinematic solution and converges in a single iteration. Based on the proposed framework, we extend ICG, which is a state-of-the-art rigid object tracking algorithm, to multi-body tracking. For the evaluation, we create a highly-realistic synthetic dataset that features a large number of sequences and various robots. Based on this dataset, we conduct a wide variety of experiments that demonstrate the excellent performance of the developed framework and our multi-body tracker.
Seven-dimensional super Yang-Mills at negative coupling

We consider the partition function for Euclidean SU(N)SU(N) super Yang-Mills on a squashed seven-sphere. We show that the localization locus of the partition function has instanton membrane solutions wrapping the six ``fixed" three-spheres on the \mathbb{S}^7𝕊7. The ADHM variables of these instantons are fields living on the membrane world volume. We compute their contribution by localizing the resulting three-dimensional supersymmetric field theory. In the round-sphere limit the individual instanton contributions are singular, but the singularities cancel when adding the contributions of all six three-spheres. The full partition function on the {\mathbb S}^7𝕊7 is well-defined even when the square of the effective Yang-Mills coupling is negative. We show for an SU(2)SU(2) gauge theory in this regime that the bare negative tension of the instanton membranes is canceled off by contributions from the instanton partition function, indicating the existence of tensionless membranes. We provide evidence that this phase is distinct from the usual weakly coupled super Yang-Mills and, in fact, is gravitational.
Bring the BitCODE-Moving Compute and Data in Distributed Heterogeneous Systems

In this paper, we present a framework for moving compute and data between processing elements in a distributed heterogeneous system. The implementation of the framework is based on the LLVM compiler toolchain combined with the UCX communication framework. The framework can generate binary machine code or LLVM bitcode for multiple CPU architectures and move the code to remote machines while dynamically optimizing and linking the code on the target platform. The remotely injected code can recursively propagate itself to other remote machines or generate new code. The goal of this paper is threefold: (a) to present an ar-chitecture and implementation of the framework that provides essential infrastructure to program a new class of disaggregated systems wherein heterogeneous programming elements such as compute nodes and data processing units (DPUs) are distributed across the system, (b) to demonstrate how the framework can be integrated with modern, high-level programming languages such as Julia, and (c) to demonstrate and evaluate a new class of eXtended Remote Direct Memory Access (X-RDMA) communication operations that are enabled by this framework. To evaluate the capabilities of the framework, we used a cluster with Fujitsu CPUs and heterogeneous cluster with Intel CPUs and BlueField-2 DPUs interconnected using high-performance RDMA fabric. We demonstrated an X-RDMA pointer chase application that outperforms an RDMA GET-based implementation by 70% and is as fast as Active Messages, but does not require function predeployment on remote platforms.
Occupation measures for controlled Markov processes: characterization and optimality

For controlled Markov processes taking values in a Polish space, control problems with ergodic cost, infinite-horizon discounted cost and finite-horizon cost are studied. Each is posed as a convex optimization problem wherein one tries to minimize a linear functional on a closed convex set of appropriately defined occupation measures for the problem. These are characterized as solutions of a linear equation asssociated with the problem. This characterization is used to establish the existence of optimal Markov controls. The dual convex optimization problem is also studied.
Simulating gravitational waves passing through the spacetime of a black hole

We investigate how GWs pass through the spacetime of a Schwarzschild black hole using time-domain numerical simulations. Our work is based on the perturbed 3+1 Einstein's equations up to the linear order. We show explicitly that our perturbation equations are covariant under infinitesimal coordinate transformations. Then we solve a symmetric second-order hyperbolic wave equation with a spatially varying wave speed. As the wave speed in our wave equation vanishes at the horizon, our formalism can naturally avoid boundary conditions at the horizon. Our formalism also does not contain coordinate singularities and, therefore, does not need regularity conditions. Then, based on our code, we simulate both finite and continuous initially plane-fronted wave trains passing through the Schwarzschild black hole. We find that for the finite wave train, the wave zone of GWs is wildly twisted by the black hole. While for the continuous wave train, unlike geometric optics, GWs can not be sheltered by the back hole. A strong beam and an interference pattern appear behind the black hole along the optical axis. Moreover, we find that the back-scattering due to the interaction between GWs and the background curvature is strongly dependent on the direction of the propagation of the trailing wavefront relative to the black hole. Finally, for a realistic input waveform generated by binary black holes, we find that the lensed waveform in the merger and ringdown phases is much longer than that of the input waveform due to the effect of back-scattering.
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours

Text classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a custom classifier typically requires coding skills and ML knowledge, which poses a significant barrier for many potential users. To lift this barrier, we introduce Label Sleuth, a free open source system for labeling and creating text classifiers. This system is unique for (a) being a no-code system, making NLP accessible to non-experts, (b) guiding users through the entire labeling process until they obtain a custom classifier, making the process efficient -- from cold start to classifier in a few hours, and (c) being open for configuration and extension by developers. By open sourcing Label Sleuth we hope to build a community of users and developers that will broaden the utilization of NLP models.
Connection Reduction Is All You Need

Convolutional Neural Networks (CNN) increase depth by stacking convolutional layers, and deeper network models perform better in image recognition. Empirical research shows that simply stacking convolutional layers does not make the network train better, and skip connection (residual learning) can improve network model performance. For the image classification task, models with global densely connected architectures perform well in large datasets like ImageNet, but are not suitable for small datasets such as CIFAR-10 and SVHN. Different from dense connections, we propose two new algorithms to connect layers. Baseline is a densely connected network, and the networks connected by the two new algorithms are named ! "#$%&'% ( and ! "#$%&'% ) respectively. The experimental results of image classification on CIFAR-10 and SVHN show that ! "#$%&'% ( has a 5% lower test error rate and 25% faster inference time than Baseline. ! "#$%&'% ) speeds up inference time by 40% with less loss in test accuracy.
Velocity estimation via model order reduction

A novel approach to full waveform inversion (FWI), based on a data driven reduced order model (ROM) of the wave equation operator is introduced. The unknown medium is probed with pulses and the time domain pressure waveform data is recorded on an active array of sensors. The ROM, a projection of the wave equation operator is constructed from the data via a nonlinear process and is used for efficient velocity estimation. While the conventional FWI via nonlinear least-squares data fitting is challenging without low frequency information, and prone to getting stuck in local minima (cycle skipping), minimization of ROM misfit is behaved much better, even for a poor initial guess. For low-dimensional parametrizations of the unknown velocity the ROM misfit function is close to convex. The proposed approach consistently outperforms conventional FWI in standard synthetic tests.