Miracle-dz/UniFAR-FTUs huggingface dataset:
Micromechanical aspects of the shear strength of wet granular soils

This paper presents a micromechanical model for the analysis of wet granular soils at low saturation (below 30%). The discrete element method is employed to model the solid particles. The capillary water is assumed to be in a pendular state and thus exists in the form of liquid bridges at the particle‐to‐particle contacts. The resulting inter‐particle adhesion is accounted for using the toroidal approximation of the bridge. Hydraulic hysteresis is accounted for based on the possible mechanism of the formation and breakage of the liquid bridges during wetting and drying phases. Shear test computational simulations were conducted at different water contents under relatively low net normal stresses. The results of these simulations suggest that capillary‐induced attractive forces and hydraulic hysteresis play an important role in affecting the shear strength of the soil. These attractive forces produce a tensile stress that contributes to the apparent cohesion of the soil and increases its stiffness. During a drying phase, capillary‐induced tensile stresses, and hence shear strength, tend to be larger than those during a wetting phase. The proposed model appears to capture the macroscopic response of wet granular materials and revealed a number of salient micromechanical mechanisms and response patterns consistent with theoretical considerations. Copyright © 2008 John Wiley & Sons, Ltd.
The solvability of a finite group by the sum of powers of element orders

We prove a new criterion for the solvability of the ﬁnite groups, depending on the function ψ k ( G ) which is deﬁned as the sum of k -th powers of the element orders of G . We show that our result can be used to show the solvability of some groups for which the solvability does not follow from earlier similar kind of results and we emphasize the following: looking at ψ k ( G ) for k > 1 can be useful to get further pieces of information about the group G .
Classification-Based Opinion Formation Model Embedding Agents' Psychological Traits

We propose an agent-based opinion formation model characterised by a two-fold novelty. First, we realistically assume that each agent cannot measure the opinion of its neighbours with infinite resolution and accuracy, and hence it can only classify the opinion of others as agreeing much more, or more, or comparably, or less, or much less (than itself) with a given statement. This leads to a classification-based rule for opinion update. Second, we consider three complementary agent traits suggested by significant sociological and psychological research: conformism, radicalism and stubbornness. We rely on World Values Survey data to show that the proposed model has the potential to predict the evolution of opinions in real life: the classification-based approach and complementary agent traits produce rich collective behaviours, such as polarisation, consensus, and clustering, which can yield predicted opinions similar to survey results.
Modes in SAW grating resonators

A Rayleigh wave of finite width, propagating along the surface of an isotropic medium, is shown to obey approximately a paraxial wave equation if the width is large compared with a wavelength. Results of laser wave propagation can be applied to the Rayleigh‐wave case. Furthermore, an equation is derived for the two‐dimensional Rayleigh‐wave propagation underneath a grating surface. A grating of finite width is shown to provide transverse confinement (guidance) of the wave. The mode patterns are obtained of the fundamental and higher‐order guided modes under uniform gratings of finite width. For a grating resonator formed of two uniform gratings separated by a quarter‐wave section, a variational expression is developed for the resonance frequencies. Using a simple mode pattern for the trial solution, the resonance frequencies of the higher‐order modes are evaluated. They all lie on the high side of the Bragg frequency, increasing with increasing mode number.
Attention as a Guide for Simultaneous Speech Translation

In simultaneous speech translation (SimulST), effective policies that determine when to write partial translations are crucial to reach high output quality with low latency. Towards this objective, we propose EDAtt (Encoder-Decoder Attention), an adaptive policy that exploits the attention patterns between audio source and target textual translation to guide an offline-trained ST model during simultaneous inference. EDAtt exploits the attention scores modeling the audio-translation relation to decide whether to emit a partial hypothesis or wait for more audio input. This is done under the assumption that, if attention is focused towards the most recently received speech segments, the information they provide can be insufficient to generate the hypothesis (indicating that the system has to wait for additional audio input). Results on en->de, es show that EDAtt yields better results compared to the SimulST state of the art, with gains respectively up to 7 and 4 BLEU points for the two languages, and with a reduction in computational-aware latency up to 1.4s and 0.7s compared to existing SimulST policies applied to offline-trained models.
IMoS: Intent‐Driven Full‐Body Motion Synthesis for Human‐Object Interactions

Can we make virtual characters in a scene interact with their surrounding objects through simple instructions? Is it possible to synthesize such motion plausibly with a diverse set of objects and instructions? Inspired by these questions, we present the first framework to synthesize the full‐body motion of virtual human characters performing specified actions with 3D objects placed within their reach. Our system takes textual instructions specifying the objects and the associated ‘intentions’ of the virtual characters as input and outputs diverse sequences of full‐body motions. This contrasts existing works, where full‐body action synthesis methods generally do not consider object interactions, and human‐object interaction methods focus mainly on synthesizing hand or finger movements for grasping objects. We accomplish our objective by designing an intent‐driven full‐body motion generator, which uses a pair of decoupled conditional variational auto‐regressors to learn the motion of the body parts in an autoregressive manner. We also optimize the 6‐DoF pose of the objects such that they plausibly fit within the hands of the synthesized characters. We compare our proposed method with the existing methods of motion synthesis and establish a new and stronger state‐of‐the‐art for the task of intent‐driven motion synthesis.
Retrieval-based Disentangled Representation Learning with Natural Language Supervision

Disentangled representation learning remains challenging as the underlying factors of variation in the data do not naturally exist. The inherent complexity of real-world data makes it unfeasible to exhaustively enumerate and encapsulate all its variations within a finite set of factors. However, it is worth noting that most real-world data have linguistic equivalents, typically in the form of textual descriptions. These linguistic counterparts can represent the data and effortlessly decomposed into distinct tokens. In light of this, we present Vocabulary Disentangled Retrieval (VDR), a retrieval-based framework that harnesses natural language as proxies of the underlying data variation to drive disentangled representation learning. Our approach employ a bi-encoder model to represent both data and natural language in a vocabulary space, enabling the model to distinguish dimensions that capture intrinsic characteristics within data through its natural language counterpart, thus facilitating disentanglement. We extensively assess the performance of VDR across 15 retrieval benchmark datasets, covering text-to-text and cross-modal retrieval scenarios, as well as human evaluation. Our experimental results compellingly demonstrate the superiority of VDR over previous bi-encoder retrievers with comparable model size and training costs, achieving an impressive 8.7% improvement in NDCG@10 on the BEIR benchmark, a 5.3% increase on MS COCO, and a 6.0% increase on Flickr30k in terms of mean recall in the zero-shot setting. Moreover, The results from human evaluation indicate that interpretability of our method is on par with SOTA captioning models.
Vision Transformers are Parameter-Efficient Audio-Visual Learners

Vision transformers (ViTs) have achieved impressive results on various computer vision tasks in the last several years. In this work, we study the capability of frozen ViTs, pretrained only on visual data, to generalize to audio-visual data without finetuning any of its original parameters. To do so, we propose a latent audio-visual hybrid (LAVISH) adapter that adapts pretrained ViTs to audio-visual tasks by injecting a small number of trainable parameters into every layer of a frozen ViT. To efficiently fuse visual and audio cues, our LAVISH adapter uses a small set of latent tokens, which form an attention bottleneck, thus, eliminating the quadratic cost of standard cross-attention. Compared to the existing modality-specific audio-visual methods, our approach achieves competitive or even better performance on various audio-visual tasks while using fewer tunable parameters and without relying on costly audio pretraining or external audio encoders. Our code is available at https://genjib.github. io/project_page/LAVISH/
Laser ablation rate of materials using the generated acoustic waves

It is shown that the acoustic waves generated during laser ablation can be used to determine the ablation threshold and the ablation rate for different fluences and depths and also to characterize the different regions of the process. On-line measurement of the individual ablation rate is possible by observation of the delay of the arrival of the acoustic wave to a piezoelectric detector located behind the sample.
The optical rogue wave patterns in coupled defocusing systems

We systematically investigate rogue wave's spatial-temporal pattern in $N$ $(N\geq2)$-component coupled defocusing nonlinear Schr\"{o}dinger equations. The fundamental rogue wave solutions are given in a unified form for both focusing and defocusing cases. We establish the quantitative correspondence between modulation instability and rogue wave patterns, which develops the previously reported inequality relation into an equation correspondence. As an example, we demonstrate phase diagrams for rogue wave patterns in a two-component coupled system, based on the complete classification of their spatial-temporal structures. The phase diagrams enable us to predict various rogue wave patterns, such as the ones with a four-petaled structure in both components. These results are meaningful for controlling the rogue wave excitations in two orthogonal polarization optical fibers.
Spatially-resolved Thermometry from Line-of-Sight Emission Spectroscopy via Machine Learning

A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in nonhomogeneous temperature fields. The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN). In total, combinations of fifteen feature groups and fifteen classical machine learning models, and eleven CNN models are considered and their performances explored. The results indicate that the combination of feature engineering and machine learning provides better performance than the direct use of CNN. Notably, feature engineering which is comprised of physics-guided transformation, signal representation-based feature extraction and Principal Component Analysis is found to be the most effective. Moreover, it is shown that when using the extracted features, the ensemble-based, light blender learning model offers the best performance with RMSE, RE, RRMSE and R values of 64.3, 0.017, 0.025 and 0.994, respectively. The proposed method, based on feature engineering and the light blender model, is capable of measuring nonuniform temperature distributions from low-resolution spectra, even when the species concentration distribution in the gas mixtures is unknown.
Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization

The goal of multimodal abstractive summarization (MAS) is to produce a concise summary given the multimodal data (text and vision). Existing studies on MAS mainly focus on how to effectively use the extracted visual features, having achieved impressive success on the high-resource English dataset. However, less attention has been paid to the quality of the visual features to the summary, which may limit the model performance, especially in the low- and zero-resource scenarios. In this paper, we propose to improve the summary quality through summary-oriented visual features. To this end, we devise two auxiliary tasks including vision to summary task and masked image modeling task. Together with the main summarization task, we optimize the MAS model via the training objectives of all these tasks. By these means, the MAS model can be enhanced by capturing the summary-oriented visual features, thereby yielding more accurate summaries. Experiments on 44 languages, covering mid-high-, low-, and zero-resource scenarios, verify the effectiveness and superiority of the proposed approach, which achieves state-of-the-art performance under all scenarios. Additionally, we will contribute a large-scale multilingual multimodal abstractive summarization (MM-Sum) dataset to the research community.
Mechanics of nonlinear random wave groups interacting with a vertical wall

Nonlinear random wave groups interacting with a vertical wall are investigated. The analytical solution for the second-order free surface displacement and velocity potential when a high crest occurs at some fixed point on, or close to, the vertical wall is obtained. The solution is exact for any water depth, and it is given as a function of the frequency spectrum of the incident waves. It is obtained that the effects of nonlinearity strongly modify the linear structure of wave groups both in the space and the time domain. The maximum effect of nonlinearity occurs when the high wave hits the wall. Furthermore, it is shown that in finite water depth, the nonlinearity increases as the bottom depth decreases. Finally, a validation by means of Monte Carlo simulations of nonlinear random waves in reflection is given.
DeepJoin: Joinable Table Discovery with Pre-trained Language Models

Due to the usefulness in data enrichment for data analysis tasks, joinable table discovery has become an important operation in data lake management. Existing approaches target equi-joins, the most common way of combining tables for creating a unified view, or semantic joins, which tolerate misspellings and different formats to deliver more join results. They are either exact solutions whose running time is linear in the sizes of query column and target table repository, or approximate solutions lacking precision. In this paper, we propose DeepJoin, a deep learning model for accurate and efficient joinable table discovery. Our solution is an embedding-based retrieval, which employs a pre-trained language model (PLM) and is designed as one framework serving both equi- and semantic (with a similarity condition on word embeddings) joins for textual attributes with fairly small cardinalities. We propose a set of contextualization options to transform column contents to a text sequence. The PLM reads the sequence and is fine-tuned to embed columns to vectors such that columns are expected to be joinable if they are close to each other in the vector space. Since the output of the PLM is fixed in length, the subsequent search procedure becomes independent of the column size. With a state-of-the-art approximate nearest neighbor search algorithm, the search time is sublinear in the repository size. To train the model, we devise the techniques for preparing training data as well as data augmentation. The experiments on real datasets demonstrate that by training on a small subset of a corpus, DeepJoin generalizes to large datasets and its precision consistently outperforms other approximate solutions'. DeepJoin is even more accurate than an exact solution to semantic joins when evaluated with labels from experts. Moreover, when equipped with a GPU, DeepJoin is up to two orders of magnitude faster than existing solutions.
RWEN-TTS: Relation-aware Word Encoding Network for Natural Text-to-Speech Synthesis

With the advent of deep learning, a huge number of text-to-speech (TTS) models which produce human-like speech have emerged. Recently, by introducing syntactic and semantic information w.r. t the input text, various approaches have been proposed to enrich the naturalness and expressiveness of TTS models. Although these strategies showed impressive results, they still have some limitations in utilizing language information. First, most approaches only use graph networks to utilize syntactic and semantic information without considering linguistic features. Second, most previous works do not explicitly consider adjacent words when encoding syntactic and semantic information, even though it is obvious that adjacent words are usually meaningful when encoding the current word. To address these issues, we propose Relation-aware Word Encoding Network (RWEN), which effectively allows syntactic and semantic information based on two modules (i.e., Semantic-level Relation Encoding and Adjacent Word Relation Encoding). Experimental results show substantial improvements compared to previous works.
Interacting Bose-condensed gases

We provide an overview of the effects of interactions in Bose-condensed gases. We focus on phenomena that have been explored in ultracold atom experiments, covering both tuneable contact interactions and dipolar interactions. Our discussion includes: modiﬁcations to the ground state and excitation spectrum, critical behaviour near the Bose–Einstein condensation temperature, the unitary regime where the interactions are as strong as allowed by quantum mechanics, quantum droplets in mixtures, and supersolids in dipolar gases.
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

The availability of challenging benchmarks has played a key role in the recent progress of machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi-Agent Challenge (SMAC) has become a popular testbed for centralised training with decentralised execution. However, after years of sustained improvement on SMAC, algorithms now achieve near-perfect performance. In this work, we conduct new analysis demonstrating that SMAC lacks the stochasticity and partial observability to require complex *closed-loop* policies. In particular, we show that an *open-loop* policy conditioned only on the timestep can achieve non-trivial win rates for many SMAC scenarios. To address this limitation, we introduce SMACv2, a new version of the benchmark where scenarios are procedurally generated and require agents to generalise to previously unseen settings (from the same distribution) during evaluation. We also introduce the extended partial observability challenge (EPO), which augments SMACv2 to ensure meaningful partial observability. We show that these changes ensure the benchmark requires the use of *closed-loop* policies. We evaluate state-of-the-art algorithms on SMACv2 and show that it presents significant challenges not present in the original benchmark. Our analysis illustrates that SMACv2 addresses the discovered deficiencies of SMAC and can help benchmark the next generation of MARL methods. Videos of training are available at https://sites.google. com/view/smacv2.
Random polynomial graphs for random Turán problems

Bukh and Conlon used random polynomial graphs to give effective lower bounds on ex(n,Tℓ) $\text{ex}(n,{{\mathscr{T}}}^{\ell })$ , where Tℓ ${{\mathscr{T}}}^{\ell }$ is the ℓ $\ell $ th power of a balanced rooted tree T $T$ . We extend their result to give effective lower bounds on ex(Gn,p,Tℓ) $\text{ex}({G}_{n,p},{{\mathscr{T}}}^{\ell })$ , which is the maximum number of edges in a Tℓ ${{\mathscr{T}}}^{\ell }$ ‐free subgraph of the random graph Gn,p ${G}_{n,p}$ . Analogous bounds for generalized Turán numbers in random graphs are also proven.
Order Routing and Market Quality: Who Benefits From Internalization?

We analyse two models of liquidity provision to determine the retail traders' preference for marketable order routing. Order internalization is captured by a model of market makers competing for the retail order flow in a Bertrand fashion. On the other hand, the price-taking competitive liquidity providers characterize the open exchange model. We show that, when liquidity providers are risk averse, routing of the marketable orders to the wholesalers is preferred by all retail traders: informed, uninformed and noisy. The unwillingness of liquidity providers to bear risk causes the strategic trader (informed or not) to absorb large shocks in their inventories. This results in mean reverting inventories, price reversal, and lower market depth. The equilibria in both models coincide with Kyle (1985) when liquidity providers are risk neutral. We also identify a universal parameter that allows comparison of market liquidity, profit and value of information across different markets.
Efficient Visual Computing with Camera RAW Snapshots

Conventional cameras capture image irradiance (RAW) on a sensor and convert it to RGB images using an image signal processor (ISP). The images can then be used for photography or visual computing tasks in a variety of applications, such as public safety surveillance and autonomous driving. One can argue that since RAW images contain all the captured information, the conversion of RAW to RGB using an ISP is not necessary for visual computing. In this paper, we propose a novel ρ-Vision framework to perform high-level semantic understanding and low-level compression using RAW images without the ISP subsystem used for decades. Considering the scarcity of available RAW image datasets, we first develop an unpaired CycleR2R network based on unsupervised CycleGAN to train modular unrolled ISP and inverse ISP (invISP) models using unpaired RAW and RGB images. We can then flexibly generate simulated RAW images (simRAW) using any existing RGB image dataset and finetune different models originally trained in the RGB domain to process real-world camera RAW images. We demonstrate object detection and image compression capabilities in RAW-domain using RAW-domain YOLOv3 and RAW image compressor (RIC) on camera snapshots. Quantitative results reveal that RAW-domain task inference provides better detection accuracy and compression efficiency compared to that in the RGB domain. Furthermore, the proposed ρ-Vision generalizes across various camera sensors and different task-specific models. An added benefit of employing the ρ-Vision is the elimination of the need for ISP, leading to potential reductions in computations and processing times.