Miracle-dz/UniFAR-FTUs huggingface dataset:
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning

Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database. The common routine is to map 2D images and 3D shapes into an embedding space and define (or learn) a shape similarity measure. While metric learning with some adaptation techniques seems to be a natural solution to shape similarity learning, the performance is often unsatisfactory for fine-grained shape retrieval. In the paper, we identify the source of the poor performance and propose a practical solution to this problem. We find that the shape difference between a negative pair is entangled with the texture gap, making metric learning ineffective in pushing away negative pairs. To tackle this issue, we develop a geometry-focused multi-view metric learning framework empowered by texture synthesis. The synthesis of textures for 3D shape models creates hard triplets, which suppress the adverse effects of rich texture in 2D images, thereby push the network to focus more on discovering geometric characteristics. Our approach shows state-of-the-art performance on a recently released large-scale 3D-FUTURE[1] repository, as well as three widely studied benchmarks, including Pix3D[2], Stanford Cars[3], and Comp Cars[4]. Codes will be made public available at: this https URL
High Tc superconductivity in heavy Rare Earth Hydrides: correlation between the presence of the f states on the Fermi surface, nesting and the value of Tc

Lanthanum hydrides, containing hydrogen framework structures under compression, display a superconducting state with a high observed critical temperature. However, this phenomenon has so far only been observed at very high pressures. Here, we computationally search for superconductors with very high critical temperatures, but at much lower pressures. We uncover two such sodalite-type clathrate hydrides, YbH6 and LuH6 (Tc =145 K at P=70 GPa for YbH6 and, especially, Tc =273 K at P=100 GPa for LuH6). These striking properties are a consequence of the strong interrelationship between the f states present at the Fermi surface, structural stability and Tc value. For example, TmH6, with unfilled 4f orbitals, is stable at 50 GPa, while it has a relatively low value of Tc of 25 K. As for the YbH6 and LuH6 compounds, they have filled f-shells and the decrease of the f-energy below the Fermi level leads to formation of the nesting regions on the Fermi surface and, as a result, to phonon "softening" and an increase in Tc.
HateBERT: Retraining BERT for Abusive Language Detection in English

We introduce HateBERT, a re-trained BERT model for abusive language detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit comments in English from communities banned for being offensive, abusive, or hateful that we have curated and made available to the public. We present the results of a detailed comparison between a general pre-trained language model and the retrained version on three English datasets for offensive, abusive language and hate speech detection tasks. In all datasets, HateBERT outperforms the corresponding general BERT model. We also discuss a battery of experiments comparing the portability of the fine-tuned models across the datasets, suggesting that portability is affected by compatibility of the annotated phenomena.
One-shot Learning for Temporal Knowledge Graphs

Most real-world knowledge graphs are characterized by a long-tail relation frequency distribution where a significant fraction of relations occurs only a handful of times. This observation has given rise to recent interest in low-shot learning methods that are able to generalize from only a few examples. The existing approaches, however, are tailored to static knowledge graphs and not easily generalized to temporal settings, where data scarcity poses even bigger problems, e.g., due to occurrence of new, previously unseen relations. We address this shortcoming by proposing a one-shot learning framework for link prediction in temporal knowledge graphs. Our proposed method employs a self-attention mechanism to effectively encode temporal interactions between entities, and a network to compute a similarity score between a given query and a (one-shot) example. Our experiments show that the proposed algorithm outperforms the state of the art baselines for two well-studied benchmarks while achieving significantly better performance for sparse relations.
Primal-Dual Mesh Convolutional Neural Networks

Recent works in geometric deep learning have introduced neural networks that allow performing inference tasks on three-dimensional geometric data by defining convolution, and sometimes pooling, operations on triangle meshes. These methods, however, either consider the input mesh as a graph, and do not exploit specific geometric properties of meshes for feature aggregation and downsampling, or are specialized for meshes, but rely on a rigid definition of convolution that does not properly capture the local topology of the mesh. We propose a method that combines the advantages of both types of approaches, while addressing their limitations: we extend a primal-dual framework drawn from the graph-neural-network literature to triangle meshes, and define convolutions on two types of graphs constructed from an input mesh. Our method takes features for both edges and faces of a 3D mesh as input and dynamically aggregates them using an attention mechanism. At the same time, we introduce a pooling operation with a precise geometric interpretation, that allows handling variations in the mesh connectivity by clustering mesh faces in a task-driven fashion. We provide theoretical insights of our approach using tools from the mesh-simplification literature. In addition, we validate experimentally our method in the tasks of shape classification and shape segmentation, where we obtain comparable or superior performance to the state of the art.
Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising

Recent years have witnessed the great success of deep convolutional neural networks (CNNs) in image denoising. Albeit deeper network and larger model capacity generally benefit performance, it remains a challenging practical issue to train a very deep image denoising network. Using multilevel wavelet-CNN (MWCNN) as an example, we empirically find that the denoising performance cannot be significantly improved by either increasing wavelet decomposition levels or increasing convolution layers within each level. To cope with this issue, this paper presents a multi-level wavelet residual network (MWRN) architecture as well as a progressive training (PTMWRN) scheme to improve image denoising performance. In contrast to MWCNN, our MWRN introduces several residual blocks after each level of discrete wavelet transform (DWT) and before inverse discrete wavelet transform (IDWT). For easing the training difficulty, scale-specific loss is applied to each level of MWRN by requiring the intermediate output to approximate the corresponding wavelet subbands of ground-truth clean image. To ensure the effectiveness of scale-specific loss, we also take the wavelet subbands of noisy image as the input to each scale of the encoder. Furthermore, progressive training scheme is adopted for better learning of MWRN by beigining with training the lowest level of MWRN and progressively training the upper levels to bring more fine details to denoising results. Experiments on both synthetic and real-world noisy images show that our PT-MWRN performs favorably against the state-of-the-art denoising methods in terms both quantitative metrics and visual quality.
GW190521 May Be an Intermediate-mass Ratio Inspiral

GW190521 is the first confident observation of a binary black hole merger with total mass . Given the lack of observational constraints at these masses, we analyze GW190521 considering two different priors for the binary’s masses: uniform in mass ratio and source-frame total mass, and uniform in source-frame component masses. For the uniform in mass-ratio prior, we find that the component masses are and . The uniform in component-mass prior yields a bimodal posterior distribution. There is a low-mass-ratio mode ( ) with and and a high-mass-ratio mode ( ) with and . Although the two modes have nearly equal posterior probability, the maximum-likelihood parameters are in the high-mass-ratio mode, with and , and a signal-to-noise ratio of 16. These results are consistent with the proposed “mass gap” produced by pair-instability in supernovae. Our results differ from those published in Abbott et al. We find that a combination of the prior used and the constraints applied may have prevented that analysis from sampling the high-mass-ratio mode. An accretion flare in AGN J124942.3+344929 was observed in possible coincidence with GW190521 by the Zwicky Transient Facility. We report parameters assuming a common origin; however, the spatial agreement of GW190521 and the electromagnetic flare alone does not provide convincing evidence for the association ( ).
Effects of high density phase transitions on neutron star dynamics

Various theoretical arguments motivate an expectation of a phase transition in matter at extreme densities above nuclear density, accompanied by hopes that gravitational wave observations may reveal the properties of such a transition. Instead of adopting a particular theory, we consider here a generic form of first order phase transition using a piecewise polytropic equation of state, and evolve both isolated neutron stars and neutron star binaries, including unequal mass binaries and, in some cases, magnetic field, looking at dynamical effects. Of particular interest are effects that may be observable either via gravitational waves or electromagnetic observations.
An Analysis of LIME for Text Data

Text data are increasingly handled in an automated fashion by machine learning algorithms. But the models handling these data are not always well-understood due to their complexity and are more and more often referred to as "black-boxes. " Interpretability methods aim to explain how these models operate. Among them, LIME has become one of the most popular in recent years. However, it comes without theoretical guarantees: even for simple models, we are not sure that LIME behaves accurately. In this paper, we provide a first theoretical analysis of LIME for text data. As a consequence of our theoretical findings, we show that LIME indeed provides meaningful explanations for simple models, namely decision trees and linear models.
Finding Genomic Hybrid Incompatibilities Between Members of the Anopheles gambiae Complex

Author(s): Kay, Raissa Genevieve-Green | Advisor(s): White, Bradley J | Abstract: The Batson-Dobzhasnky-Muller model predicts that as species diverge over time they will accumulate genetic differences, which may be incompatible with each other when combined into the same genetic background. Despite the central importance of this process to diversification and speciation, identification of genes causing hybrid dysfunction have been limited primarily to model species. Anopheles gambiae, the principal mosquito vector of malaria in Africa, belongs to a complex of at least nine isomorphic species. In accordance with Haldane’s rule, reciprocal crosses between members of the Anopheles gambiae complex resulted in completely sterile males, a fact that was used to classify different mosquito populations into separate species. Some of these hybrid crosses were also found to have sex-ratio distortion implying some form of female hybrid inviability, refuting Haldane’s rule. Those females that were viable were also found to be fertile. Using reciprocal crosses between An. coluzzii and An. merus we found that when F1 hybrid females were backcrossed, the resulting male progeny displayed a range of phenotypes from completely sterile to completely fertile providing the foundation for quantitative trait locus (QTL) mapping. We performed reciprocal backcrosses, phenotyped ~2500 males from those backcrosses, and are in the process of genotyping each individual male at an average of g10,000 markers across the genome using a reduced representation sequencing approach. By doing RNA-seq on F1 hybrid testes we also found evidence of disruption of sex chromosome inactivation in one of our crosses, an under-appreciated mechanism of Haldane’s rule.
Contrastive Learning with Adversarial Examples

Contrastive learning (CL) is a popular technique for self-supervised learning (SSL) of visual representations. It uses pairs of augmentations of unlabeled training examples to define a classification task for pretext learning of a deep embedding. Despite extensive works in augmentation procedures, prior works do not address the selection of challenging negative pairs, as images within a sampled batch are treated independently. This paper addresses the problem, by introducing a new family of adversarial examples for constrastive learning and using these examples to define a new adversarial training algorithm for SSL, denoted as CLAE. When compared to standard CL, the use of adversarial examples creates more challenging positive pairs and adversarial training produces harder negative pairs by accounting for all images in a batch during the optimization. CLAE is compatible with many CL methods in the literature. Experiments show that it improves the performance of several existing CL baselines on multiple datasets.
Neural Passage Retrieval with Improved Negative Contrast

In this paper we explore the effects of negative sampling in dual encoder models used to retrieve passages for automatic question answering. We explore four negative sampling strategies that complement the straightforward random sampling of negatives, typically used to train dual encoder models. Out of the four strategies, three are based on retrieval and one on heuristics. Our retrieval-based strategies are based on the semantic similarity and the lexical overlap between questions and passages. We train the dual encoder models in two stages: pre-training with synthetic data and fine tuning with domain-specific data. We apply negative sampling to both stages. The approach is evaluated in two passage retrieval tasks. Even though it is not evident that there is one single sampling strategy that works best in all the tasks, it is clear that our strategies contribute to improving the contrast between the response and all the other passages. Furthermore, mixing the negatives from different strategies achieve performance on par with the best performing strategy in all tasks. Our results establish a new state-of-the-art level of performance on two of the open-domain question answering datasets that we evaluated.
GiBERT: Introducing Linguistic Knowledge into BERT through a Lightweight Gated Injection Method

Large pre-trained language models such as BERT have been the driving force behind recent improvements across many NLP tasks. However, BERT is only trained to predict missing words - either behind masks or in the next sentence - and has no knowledge of lexical, syntactic or semantic information beyond what it picks up through unsupervised pre-training. We propose a novel method to explicitly inject linguistic knowledge in the form of word embeddings into any layer of a pre-trained BERT. Our performance improvements on multiple semantic similarity datasets when injecting dependency-based and counter-fitted embeddings indicate that such information is beneficial and currently missing from the original model. Our qualitative analysis shows that counter-fitted embedding injection particularly helps with cases involving synonym pairs.
Superorbital Modulation in the High-mass X-Ray Binary 4U 1538–52 and Possible Modulation in IGR J16393–4643

Hard X-ray observations with the Neil Gehrels Swift Observatory Burst Alert Telescope (BAT) reveal superorbital modulation in the wind-accreting supergiant high-mass X-ray binary (HMXB) 4U 1538−52 at a period of 14.9130 ± 0.0026 days that is consistent with four times the 3.73 day orbital period. These periods agree with a previously suggested correlation between superorbital and orbital periods in similar HMXBs. During the ∼14 yr of observations the superorbital modulation changes amplitude, and since ∼MJD 57,650 it was no longer detected in the power spectrum, although a peak near the second harmonic of this was present for some time. Measurements of the spin period of the neutron star in the system with the Fermi Gamma-ray Burst Monitor show a long-term spin-down trend, which halted toward the end of the light curve, suggesting a connection between and superorbital modulation, as proposed for 2S 0114+650. However, an earlier torque reversal from INTEGRAL observations was not associated with superorbital modulation changes. B- and V-band photometry from the Las Cumbres Observatory reveals orbital ellipsoidal photometric variability, but no superorbital optical modulation. However the photometry was obtained when the 14.9130 day period was no longer detected in the BAT power spectrum. We revisit possible superorbital modulation in BAT observations of IGR J16393−4643 but cannot conclusively determine whether this is present, although it is not persistent. We consider superorbital modulation mechanisms, and suggest that the corotating interaction region model, with small deviations from orbital synchronization, appears promising.
Saudi Arabian Parents' Perception of Online Marital Matchmaking Technologies

Finding a date or a spouse online is usually considered an individualistic endeavor in Western cultures. This presents a challenge for collectivist non-Western cultures such as Saudi Arabia where choosing a spouse is viewed as a union of two families with parents of both spouses being heavily involved. Our work aims to investigate how Saudi Arabian parents view the utilization of technology by their young adults to seek potential spouses online. We report our findings of interviews conducted with 16 Saudi Arabian parents (8 fathers, 6 mothers and 1 couple). We generate qualitative themes that provide insights about how parents wanted to preserve their values, integrate technology into the traditional process and protect their young adults from potential harms. These themes lead to implications for designing suitable marital matchmaking technologies in Saudi Arabia and opportunities for future work.
Language Models are Open Knowledge Graphs

This paper shows how to construct knowledge graphs (KGs) from pre-trained language models (e.g., BERT, GPT-2/3), without human supervision. Popular KGs (e.g, Wikidata, NELL) are built in either a supervised or semi-supervised manner, requiring humans to create knowledge. Recent deep language models automatically acquire knowledge from large-scale corpora via pre-training. The stored knowledge has enabled the language models to improve downstream NLP tasks, e.g., answering questions, and writing code and articles. In this paper, we propose an unsupervised method to cast the knowledge contained within language models into KGs. We show that KGs are constructed with a single forward pass of the pre-trained language models (without fine-tuning) over the corpora. We demonstrate the quality of the constructed KGs by comparing to two KGs (Wikidata, TAC KBP) created by humans. Our KGs also provide open factual knowledge that is new in the existing KGs. Our code and KGs will be made publicly available.
NetSquid, a discrete-event simulation platform for quantum networks

In order to bring quantum networks into the real world, we would like to determine the requirements of quantum network protocols including the underlying quantum hardware. Since sufficiently detailed architecture proposals are generally too complex for mathematical analysis, it is natural to employ numerical simulation. Here, we introduce NetSquid, a generic discrete-event based platform for simulating all aspects of quantum networks and modular quantum computing systems, ranging from the physical layer hardware and the control plane all the way to the application level. We study several use cases in order to showcase the power of NetSquid. First, we perform a detailed physical layer simulation of repeater chains using processing nodes based on NV centres in diamond, as well as using atomic-ensemble based repeater chains. Second, we study the control plane of a recently introduced quantum switch beyond the analytically known regime. Use of the novel discrete-event paradigm for simulating networks allows the study of time-dependent noise in an entirely modular simulation, predicting the combined performance of physical hardware and control. Finally, we showcase Netsquid's ability to investigate large networks by simulating the distribution of an entangled pair of qubits over a chain of up to one thousand nodes using simplified physical models in less than three seconds.
Experimental investigation of tsunami waves generated by granular collapse into water

Abstract The generation of a tsunami wave by an aerial landslide is investigated through model laboratory experiments. We examine the collapse of an initially dry column of grains into a shallow water layer and the subsequent generation of waves. The experiments show that the collective entry of the granular material into water governs the wave generation process. We observe that the amplitude of the wave relative to the water height scales linearly with the Froude number based on the horizontal velocity of the moving granular front relative to the wave velocity. For all the different parameters considered here, the aspect ratio and the volume of the column, the diameter and density of the grains, and the height of the water, the granular collapse acts like a moving piston displacing the water. We also highlight that the density of the falling grains has a negligible influence on the wave amplitude, which suggests that the volume of grains entering the water is the relevant parameter in the wave generation.
Locally Decodable/Correctable Codes for Insertions and Deletions

Recent efforts in coding theory have focused on building codes for insertions and deletions, called insdel codes, with optimal trade-offs between their redundancy and their error-correction capabilities, as well as efficient encoding and decoding algorithms. In many applications, polynomial running time may still be prohibitively expensive, which has motivated the study of codes with super-efficient decoding algorithms. These have led to the well-studied notions of Locally Decodable Codes (LDCs) and Locally Correctable Codes (LCCs). Inspired by these notions, Ostrovsky and Paskin-Cherniavsky (Information Theoretic Security, 2015) generalized Hamming LDCs to insertions and deletions. To the best of our knowledge, these are the only known results that study the analogues of Hamming LDCs in channels performing insertions and deletions. Here we continue the study of insdel codes that admit local algorithms. Specifically, we reprove the results of Ostrovsky and Paskin-Cherniavsky for insdel LDCs using a different set of techniques. We also observe that the techniques extend to constructions of LCCs. Specifically, we obtain insdel LDCs and LCCs from their Hamming LDCs and LCCs analogues, respectively. The rate and error-correction capability blow up only by a constant factor, while the query complexity blows up by a poly log factor in the block length. Since insdel locally decodable/correctble codes are scarcely studied in the literature, we believe our results and techniques may lead to further research. In particular, we conjecture that constant-query insdel LDCs/LCCs do not exist.