Miracle-dz/UniFAR-FTUs huggingface dataset:
Localization-Based Tracking

End-to-end production of object tracklets from high resolution video in real-time and with high accuracy remains a challenging problem due to the cost of object detection on each frame. In this work we present Localization-based Tracking (LBT), an extension to any tracker that follows the tracking by detection or joint detection and tracking paradigms. Localization-based Tracking focuses only on regions likely to contain objects to boost detection speed and avoid matching errors. We evaluate LBT as an extension to two example trackers (KIOU and SORT) on the UA-DETRAC and MOT20 datasets. LBT-extended trackers outperform all other reported algorithms in terms of PR-MOTA, PR-MOTP, and mostly tracked objects on the UA-DETRAC benchmark, establishing a new state-of-the art. relative to tracking by detection with KIOU, LBT-extended KIOU achieves a 25% higher frame-rate and is 1.1% more accurate in terms of PR-MOTA on the UA-DETRAC dataset. LBT-extended SORT achieves a 62% speedup and a 3.2% increase in PR-MOTA on the UA-DETRAC dataset. On MOT20, LBT-extended KIOU has a 50% higher frame-rate than tracking by detection and is 0.4% more accurate in terms of MOTA. As of submission time, our LBT-extended KIOU tracker places 10th overall on the MOT20 benchmark.
Influence of Dislocations on the Shock Sensitivity of RDX: Molecular Dynamics Simulations by Reactive Force Field

Molecular dynamics simulations of the chemical responses of shocked dislocation-contained and perfect (p) 1,3,5-trinitro-1,3,5-triazinane (RDX) crystals were performed using the ReaxFF force field combined with the multiscale shock technique. The shear dynamics of four types of dislocated RDX crystals are also modeled. The predicted mobilities of the crystals decrease in the order of (010) [001]/screw (s2) > (010) [001]/edge (e2) > (010) 1/2[100]/screw (s1) > (010)1/2[100]/edge (e1) according to their shear stress barriers, thus revealing the initial driving force required to activate a slip system. In view of the evolution of temperatures, pressures, and reactant decay rates of the shocked perfect and dislocated RDX, we confirm that shock sensitivity follows the order of e2 > e1 > s1 ≈ s2 > p. In particular, all dislocations enhance the shock sensitivity of RDX; in particular, edge dislocations enhance shock sensitivity significantly, whereas screw dislocations enhance it slightly. Shock sensitivity is n.
Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack

Model extraction increasingly attracts research attentions as keeping commercial AI models private can retain a competitive advantage. In some scenarios, AI models are trained proprietarily, where neither pre-trained models nor sufficient in-distribution data is publicly available. Model extraction attacks against these models are typically more devastating. Therefore, in this paper, we empirically investigate the behaviors of model extraction under such scenarios. We find the effectiveness of existing techniques significantly affected by the absence of pre-trained models. In addition, the impacts of the attacker's hyperparameters, e.g. model architecture and optimizer, as well as the utilities of information retrieved from queries, are counterintuitive. We provide some insights on explaining the possible causes of these phenomena. With these observations, we formulate model extraction attacks into an adaptive framework that captures these factors with deep reinforcement learning. Experiments show that the proposed framework can be used to improve existing techniques, and show that model extraction is still possible in such strict scenarios. Our research can help system designers to construct better defense strategies based on their scenarios.
On Saint-Venant Compatibility and Stress Potentials in Manifolds with Boundary and Constant Sectional Curvature

We address three related problems in the theory of elasticity, formulated in the framework of double forms: the Saint-Venant compatibility condition, the existence and uniqueness of solutions for equations arising in incompatible elasticity, and the existence of stress potentials. The scope of this work is for manifolds with boundary of arbitrary dimension, having constant sectional curvature. The central analytical machinery is the regular ellipticity of a boundary-value problem for a bilaplacian operator, and its consequences, which were developed in [KL21]. One of the novelties of this work is that stress potentials can be used in non-Euclidean geometries, and that the gauge freedom can be exploited to obtain a generalization for the biharmonic equation for the stress potential in dimensions greater than two.
Learning from Executions for Semantic Parsing

Semantic parsing aims at translating natural language (NL) utterances onto machine-interpretable programs, which can be executed against a real-world environment. The expensive annotation of utterance-program pairs has long been acknowledged as a major bottleneck for the deployment of contemporary neural models to real-life applications. In this work, we focus on the task of semi-supervised learning where a limited amount of annotated data is available together with many unlabeled NL utterances. Based on the observation that programs which correspond to NL utterances should always be executable, we propose to encourage a parser to generate executable programs for unlabeled utterances. Due to the large search space of executable programs, conventional methods that use beam-search for approximation, such as self-training and top-k marginal likelihood training, do not perform as well. Instead, we propose a set of new training objectives that are derived by approaching the problem of learning from executions from the posterior regularization perspective. Our new objectives outperform conventional methods on Overnight and GeoQuery, bridging the gap between semi-supervised and supervised learning.
Semiring Primitives for Sparse Neighborhood Methods on the GPU

High-performanceprimitivesformathematicaloperations onsparse vectors must deal with the challenges of skewed degree distributions and limits on memory consumption that are typically not issues in dense operations. We demonstrate that a sparse semiring primitive can be flexible enough to support a wide range of critical distance measures while maintaining performance and memory efficiency on the GPU. We further show that this primitive is a foundational component for enabling many neighborhood-based information retrieval and machine learning algorithms to accept sparse input. To our knowledge, this is the first work aiming to unify the computation of several critical distance measures on the GPU under a single flexible design paradigm and we hope that it provides a good baseline for future research in this area. Our implementation is fully open source and publicly available at https://github.com/rapidsai/cuml.
Applying inviscid linear unsteady lifting-line theory to viscous large-amplitude problems

Unsteady Lifting-Line Theory (ULLT) is a low order method capable of modeling interacting unsteady and finite wing effects at low computational cost. Most formulations of the method assume inviscid flow and small amplitudes. Whilst these assumptions might be suitable for small-amplitude aeroelastic problems at high Reynolds numbers, modern engineering applications increasingly involve lower Reynolds numbers, large amplitude kinematics and vortex structures that lead to aerodynamic non-linearities. This paper establishes that ULLT still provides a good solution for low Reynolds number, large-amplitude kinematics problems, by comparing ULLT results against those of experimentally validated computational fluid dynamics simulations at Re=10 000. Three-dimensional (3D) effects stabilize Leading Edge Vortex (LEV) structures, resulting in a good prediction of whole wing force coefficients by ULLT. Whilst the inviscid spanwise force distributions are accurate for small-amplitude kinematics, the ULLT cannot model 3D vortical structures, and thus it cannot correctly predict the force distribution due the LEV. It can however predict the shedding of LEVs to a limited extent via the leading edge suction parameter criterion. This can then be used as an indicator of the usefulness of the force distribution results.
Optical nebular emission following the most luminous outburst of Aquila X-1

Aquila X-1 is a prototypical neutron star low mass X-ray binary and one of the most studied X-ray transients. We present optical spectroscopy obtained with the Gran Telescopio Canarias (10.4 m) during the 2016 outburst, the brightest recorded in recent times and which showed a standard evolution with hard and soft accretion states. Our dataset includes a dense coverage of the brightest phases of the event, as well as the decay towards quiescence. We searched for optical winds by studying the profiles and evolution of the main emission lines and found no indisputable wind signatures, such as P-Cyg profiles. Nonetheless, our detailed analysis of the particularly strong and broad Hα emission line, detected at the end of the outburst, is consistent with the presence of a nebular phase produced by optically thin ejecta at ∼800 km s−1 or, alternatively, an extended disc atmosphere. We discuss these possibilities as well as the similarities with the phenomenology observed in other black hole and neutron star systems. Our study suggests that optical nebular phases might be a relatively common observational feature during the late stages of low mass X-ray binaries’ outbursts, enabling us to probe the presence of outflows at low-to-intermediate orbital inclinations.
Mediators in Determining what Processing BERT Performs First

Probing neural models for the ability to perform downstream tasks using their activation patterns is often used to localize what parts of the network specialize in performing what tasks. However, little work addressed potential mediating factors in such comparisons. As a test-case mediating factor, we consider the prediction’s context length, namely the length of the span whose processing is minimally required to perform the prediction. We show that not controlling for context length may lead to contradictory conclusions as to the localization patterns of the network, depending on the distribution of the probing dataset. Indeed, when probing BERT with seven tasks, we find that it is possible to get 196 different rankings between them when manipulating the distribution of context lengths in the probing dataset. We conclude by presenting best practices for conducting such comparisons in the future.
Discourse Probing of Pretrained Language Models

Existing work on probing of pretrained language models (LMs) has predominantly focused on sentence-level syntactic tasks. In this paper, we introduce document-level discourse probing to evaluate the ability of pretrained LMs to capture document-level relations. We experiment with 7 pretrained LMs, 4 languages, and 7 discourse probing tasks, and find BART to be overall the best model at capturing discourse — but only in its encoder, with BERT performing surprisingly well as the baseline model. Across the different models, there are substantial differences in which layers best capture discourse information, and large disparities between models.
Targeted Adversarial Training for Natural Language Understanding

We present a simple yet effective Targeted Adversarial Training (TAT) algorithm to improve adversarial training for natural language understanding. The key idea is to introspect current mistakes and prioritize adversarial training steps to where the model errs the most. Experiments show that TAT can significantly improve accuracy over standard adversarial training on GLUE and attain new state-of-the-art zero-shot results on XNLI. Our code will be released upon acceptance of the paper.
Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization

Training deep networks with limited labeled data while achieving a strong generalization ability is key in the quest to reduce human annotation efforts. This is the goal of semi-supervised learning, which exploits more widely available unlabeled data to complement small labeled data sets. In this paper, we propose a novel framework for discriminative pixel-level tasks using a generative model of both images and labels. Concretely, we learn a generative adversarial network that captures the joint image-label distribution and is trained efficiently using a large set of un-labeled images supplemented with only few labeled ones. We build our architecture on top of StyleGAN2 [45], augmented with a label synthesis branch. Image labeling at test time is achieved by first embedding the target image into the joint latent space via an encoder network and test-time optimization, and then generating the label from the inferred embedding. We evaluate our approach in two important domains: medical image segmentation and part-based face segmentation. We demonstrate strong in-domain performance compared to several baselines, and are the first to showcase extreme out-of-domain generalization, such as transferring from CT to MRI in medical imaging, and photographs of real faces to paintings, sculptures, and even cartoons and animal faces. Project Page: https://nv-tlabs.github. io/semanticGAN/
Improving Long-Tailed Classification from Instance Level

Data in the real world tends to exhibit a long-tailed label distribution, which poses great challenges for neural networks in classiﬁcation. Existing methods tackle this problem mainly from the coarse-grained class level, ignoring the difference among instances, e.g., hard samples vs. easy samples. In this paper, we revisit the long-tailed problem from the instance level and propose two instance-level components to improve long-tailed classiﬁcation. The ﬁrst one is an Adaptive Logit Adjustment (ALA) loss, which applies an adaptive adjusting term to the logit. Different from the adjusting terms in existing methods that are class-dependent and only focus on tail classes, we carefully design an instance-speciﬁc term and add it on the class-dependent term to make the network pay more attention to not only tailed class, but more importantly hard samples. The second one is a Mixture-of-Experts (MoE) network, which contains a multi-expert module and an instance-aware routing module. The routing module is designed to dynamically integrate the results of multiple experts according to each input instance, and is trained jointly with the experts network in an end-to-end manner. Extensive experiment results show that our method outperforms the state-of-the-art methods by 1% to 5% on common long-tailed benchmarks including ImageNet-LT and iNaturalist.
Multivariate Deep Evidential Regression

There is significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware neural networks (NNs), based on learning evidential distributions for aleatoric and epistemic uncertainties, shows promise over traditional deterministic methods and typical Bayesian NNs, yet several important gaps in the theory and implementation of these networks remain. We discuss three issues with a proposed solution to extract aleatoric and epistemic uncertainties from regression-based neural networks. The approach derives a technique by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparameters of the evidential distribution. Doing so allows for the simultaneous extraction of both uncertainties without sampling or utilization of out-of-distribution data for univariate regression tasks. We describe the outstanding issues in detail, provide a possible solution, and generalize the deep evidential regression technique for multivariate cases.
Understanding Transformers for Bot Detection in Twitter

In this paper we shed light on the impact of fine-tuning over social media data in the internal representations of neural language models. We focus on bot detection in Twitter, a key task to mitigate and counteract the automatic spreading of disinformation and bias in social media. We investigate the use of pre-trained language models to tackle the detection of tweets generated by a bot or a human account based exclusively on its content. Unlike the general trend in benchmarks like GLUE, where BERT generally outperforms generative transformers like GPT and GPT-2 for most classification tasks on regular text, we observe that fine-tuning generative transformers on a bot detection task produces higher accuracies. We analyze the architectural components of each transformer and study the effect of fine-tuning on their hidden states and output representations. Among our findings, we show that part of the syntactical information and distributional properties captured by BERT during pre-training is lost upon fine-tuning while the generative pre-training approach manage to preserve these properties.
A Replication Study of Dense Passage Retriever

Text retrieval using learned dense representations has recently emerged as a promising alternative to"traditional"text retrieval using sparse bag-of-words representations. One recent work that has garnered much attention is the dense passage retriever (DPR) technique proposed by Karpukhin et al. (2020) for end-to-end open-domain question answering. We present a replication study of this work, starting with model checkpoints provided by the authors, but otherwise from an independent implementation in our group's Pyserini IR toolkit and PyGaggle neural text ranking library. Although our experimental results largely verify the claims of the original paper, we arrived at two important additional findings that contribute to a better understanding of DPR: First, it appears that the original authors under-report the effectiveness of the BM25 baseline and hence also dense--sparse hybrid retrieval results. Second, by incorporating evidence from the retriever and an improved answer span scoring technique, we are able to improve end-to-end question answering effectiveness using exactly the same models as in the original work.
Tuning Hole Mobility of Individual p-Doped GaAs Nanowires by Uniaxial Tensile Stress

Strain engineering provides an effective way of tailoring the electronic and optoelectronic properties of semiconductor nanomaterials and nanodevices, giving rise to novel functionalities. Here, we present direct experimental evidence of strain-induced modifications of hole mobility in individual gallium arsenide (GaAs) nanowires, using in situ transmission electron microscopy (TEM). The conductivity of the nanowires varied with applied uniaxial tensile stress, showing an initial decrease of ∼5–20% up to a stress of 1–2 GPa, subsequently increasing up to the elastic limit of the nanowires. This is attributed to a hole mobility variation due to changes in the valence band structure caused by stress and strain. The corresponding lattice strain in the nanowires was quantified by in situ four dimensional scanning TEM and showed a complex spatial distribution at all stress levels. Meanwhile, a significant red shift of the band gap induced by the stress and strain was unveiled by monochromated electron energy loss spectroscopy.
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering

The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG. Here we propose a new model, QA-GNN, which addresses the above challenges through two key innovations: (i) relevance scoring, where we use LMs to estimate the importance of KG nodes relative to the given QA context, and (ii) joint reasoning, where we connect the QA context and KG to form a joint graph, and mutually update their representations through graph-based message passing. We evaluate QA-GNN on the CommonsenseQA and OpenBookQA datasets, and show its improvement over existing LM and LM+KG models, as well as its capability to perform interpretable and structured reasoning, e.g., correctly handling negation in questions.
A new approach to topological T-duality for principal torus bundles

We introduce a new `Thom class' formulation of topological T-duality for principal torus bundles. This definition is equivalent to the established one of Bunke, Rumpf, and Schick but has the virtue of removing the global assumptions on the H-flux required in the old definition. With the new definition, we provide easier and more transparent proofs of the classification of T-duals and generalise the local formulation of T-duality for circle bundles by Bunke, Schick, and Spitzweck to the torus case.
QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization

Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed. However, it is hard to create a single short summary that covers all the content of a long meeting involving multiple people and topics. In order to satisfy the needs of different types of users, we define a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans of meetings in response to a query, and we introduce QMSum, a new benchmark for this task. QMSum consists of 1,808 query-summary pairs over 232 meetings in multiple domains. Besides, we investigate a locate-then-summarize method and evaluate a set of strong summarization baselines on the task. Experimental results and manual analysis reveal that QMSum presents significant challenges in long meeting summarization for future research. Dataset is available at https://github.com/Yale-LILY/QMSum.
Crystallographic features related to a van der Waals coupling in the layered chalcogenide FePS3

We investigated the crystallographic structure of FePS3 with a layered structure using transmission electron microscopy and powder X-ray diffraction. We found that FePS3 forms a rotational twin structure with the common axis along the c*-axis. The high-resolution transmission electron microscopy images revealed that the twin boundaries were positioned at the van der Waals gaps between the layers. The narrow bands of dark contrast were observed in the bright-field transmission electron microscopy images below the antiferromagnetic transition temperature, TN ≈ 120 K. Low-temperature X-ray diffraction showed a lattice distortion; the a- and b-axes shortened and lengthened, respectively, as the temperature decreased below TN. We propose that the narrow bands of dark contrast observed in the bright-field transmission electron microscopy images are caused by the directional lattice distortion with respect to each micro-twin variant in the antiferromagnetic phase.