Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined experimental and theoretical study of hydrostatic He-gas pressure effects in 
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We report a detailed experimental and theoretical study on the effect of hydrostatic pressure on the structural and magnetic aspects of the layered honeycomb antiferromagent $\alpha$-RuCl$_{3}$. Magnetic susceptibility measurements performed under almost ideal hydrostatic-pressure conditions yield that the phase transition to zigzag-type antiferromagnetic order at $T_N$ = 7.3 K can be rapidly suppressed to about 6.1 K. A further suppression with increasing pressure is impeded due to the occurrence of a pressure-induced structural transition at $p \geq$ 104 MPa, accompanied by a strong dimerization of Ru-Ru bonds, which gives rise to a collapse of the magnetic susceptibility. Whereas the dimerization transition is strongly first order, as reflected by large discontinuous changes in $\chi$ and pronounced hysteresis effects, the magnetic transition under varying pressure and magnetic field also reveals indications for a weakly first-order transition. We assign this observation to a strong magnetoelastic coupling in this system. Measurements of $\chi$ under varying pressure in the paramagnetic regime ($T>T_N$) and before dimerization ($p<$ 100 MPa) reveal a considerable increase of $\chi$ with pressure. These experimental observations are consistent with the results of ab-initio Density Functional Theory (DFT) calculations on the pressure-dependent structure and the corresponding pressure-dependent magnetic model. Comparative susceptibility measurements on a second crystal showing two consecutive magnetic transitions instead of one, indicating the influence of stacking faults. Using different temperature-pressure protocols the effect of these stacking faults can be temporarily overcome, transforming the magnetic state from a multiple-$T_N$ into a single-$T_N$ state.
FROM Si NANOWIRES TO Ge NANOCRYSTALS FOR VIS-NIR-SWIR SENSORS AND NON-VOLATILE MEMORIES: A REVIEW

"Nanocrystalline Si and Ge are ofhigh interestfor integrated Si photonics related to light emission, opticul sensors, photodetectors, solar energy harvesting and conversion devices, and also forfloating gate non-volatile memories (NVMs). In this review, we have focused on nanocrystalline porous Si (nc-PS) with extension to Si nanodots, and Ge nanocrystals (NCs)Zquantum dots (QDs)/nanoparticles (NPs) embedded in oxides (SiCh, TiCE, HfCh, AI2O3). The great asset ofnc-PS is its intense photoluminescence in VIS at room temperature (RT), while Ge NCs/NPs embedded in oxides show high photosensitivity in VISNIR-SWIR in the spectral photocurrent up to 1325 nm at RT. Ge NCs/NPs/QDsfloating gate NVMs present high memory performance, the retention characteristics corresponding to the state of the art for NCs floating gate NVMs. We prove the relevance of controlling the preparation parametersfor obtainingfilms with targetedphotoluminescence, photosensitivity and charge storage properties for applications, e.g. VIS-NIR-SWIR optical sensors and photodetectors, and electronic and photoelectric NVMs. We evidence the correlation of preparation conditions, morphology, composition and crystalline structure with optical, electrical, photoelectrical and charge storage properties and also evidence the contribution ofquantum confinement effect, localized States and trapping centers. "
DeepAL for Regression Using ε-weighted Hybrid Query Strategy

Designing an inexpensive approximate surrogate model that captures the salient features of an expensive high-ﬁdelity behavior is a prevalent approach in design optimization. In recent times, Deep Learning (DL) models are being used as a promising surrogate computational model for engineering problems. However, the main challenge in creating a DL-based surrogate is to simulate/label a large number of design points, which is time-consuming for computationally costly and/or high dimensional engineering problems. In the present work, we propose a novel sampling technique by combining the active learning (AL) method with DL. We call this method ε -weighted hybrid query strategy ( ε -HQS) , which focuses on the evaluation of the surrogate at each learning iteration and provides an estimate of the failure probability of the surrogate in the Design Space. By reusing already collected training and test data, the learned failure probability guides the next iteration’s sampling process to the region of the high probability of failure. We empirically evaluated this method in two different engineering design domains, ﬁnite element based static stress analysis of submarine pressure vessel (computationally costly process) and second subma-rine propeller design ( high dimensional problem). Empirical evaluation shows better performance of our technique as compared to other methods and in one case was able to achieve same level of prediction accuracy as random sample selection process with 58% less number of samples. https://github.com/vardhah/epsilon_weighted_Hybrid_Query_Strategy .
Synergistic approach towards reproducible high zT in superionic thermoelectric Ag2Te

Recently, the superionic thermoelectrics, which typify the novel ‘phonon-liquid electron-crystal’ concept, have attracted enormous attention due to their ultralow thermal conductivity and high ﬁgure-of-merit (zT). However, their high zT is generally obtained deep inside the superionic phase, e.g., near 1000 K in Cu 2 X (X: chalcogen atom) family where the superionic transition is close to 400 K. At such high temperatures, the liquid-like ﬂow of the metal ions under an electric ﬁeld or a temperature gradient, both of which are integral to the working of a thermoelectric device, results in device degradation. To harness the full potential of the superionic thermoelectrics, it is therefore necessary to reach high zT at low temperatures where the metal-ion diﬀusion is not an issue. Here, we present a novel all-room-temperature route to fabricate 100% dense, nanostructured Ag 2 Te with highly reproducible thermoelectric properties and a high zTof 1.2 at 570 K, i.e., merely 150 K above its superionic transition. The samples show a broad particle-size distribution ranging from a few nm to a few µ m. This hierarchical nanostructuring is shown to suppress the thermal conductivity of Ag 2 Te beyond the phonon-liquid electron-crystal limit to ultra low values, leading to a remarkable enhancement of 87% in the zT over that of the ingot sample. These values supersede the zT of any Ag 2 Te previously reported. Our results are supported by ﬁrst-principles density functional theory calculations of the electronic and thermal properties.
Prompting Decision Transformer for Few-Shot Policy Generalization

Humans can leverage prior experience and learn novel tasks from a handful of demonstrations. In contrast to offline meta-reinforcement learning, which aims to achieve quick adaptation through better algorithm design, we investigate the effect of architecture inductive bias on the few-shot learning capability. We propose a Prompt-based Decision Transformer (Prompt-DT), which leverages the sequential modeling ability of the Transformer architecture and the prompt framework to achieve few-shot adaptation in offline RL. We design the trajectory prompt, which contains segments of the few-shot demonstrations, and encodes task-specific information to guide policy generation. Our experiments in five MuJoCo control benchmarks show that Prompt-DT is a strong few-shot learner without any extra finetuning on unseen target tasks. Prompt-DT outperforms its variants and strong meta offline RL baselines by a large margin with a trajectory prompt containing only a few timesteps. Prompt-DT is also robust to prompt length changes and can generalize to out-of-distribution (OOD) environments.
Bilevel Optimization of the Kantorovich Problem and its Quadratic Regularization Part I: Existence Results

This paper is concerned with an optimization problem governed by the Kantorovich optimal transportation problem. This gives rise to a bilevel optimization problem, which can be reformulated as a mathematical problem with complementarity constraints in the space of regular Borel measures. Because of the non-smoothness induced by the complementarity relations, problems of this type are frequently regularized. Here we apply a quadratic regularization of the Kantorovich problem. As the title indicates, this is the first part in a series of three papers. It addresses the existence of optimal solutions to the bilevel Kantorovich problem and its quadratic regularization, whereas part II and III are dedicated to the convergence analysis for vanishing regularization.
Learning to Rearrange with Physics-Inspired Risk Awareness

Real-world applications require a robot operating in the physical world with awareness of potential risks besides accomplishing the task. A large part of risky behaviors arises from interacting with objects in ignorance of affordance. To prevent the agent from making unsafe decisions, we propose to train a robotic agent by reinforcement learning to execute tasks with an awareness of physical properties such as mass and friction in an indoor environment. We achieve this through a novel physics-inspired reward function that encourages the agent to learn a policy discerning different masses and friction coefficients. We introduce two novel and challenging indoor rearrangement tasks -- the variable friction pushing task and the variable mass pushing task -- that allow evaluation of the learned policies in trading off performance and physics-inspired risk. Our results demonstrate that by equipping with the proposed reward, the agent is able to learn policies choosing the pushing targets or goal-reaching trajectories with minimum physical cost, which can be further utilized as a precaution to constrain the agent's behavior in a safety-critic environment.
Constrained Shortest-Path Reformulations for Discrete Bilevel and Robust Optimization

Many discrete optimization problems are amenable to constrained shortest-path reformulations in an extended network space, a technique that has been key in convexification, bound strengthening, and search mechanisms for a large array of nonlinear problems. In this paper, we expand this methodology and propose constrained shortest-path models for challenging discrete variants of bilevel and robust optimization, where traditional methods (e.g., dualize-and-combine) are not applicable compared to their continuous counterparts. Specifically, we propose a framework that models problems as decision diagrams and introduces side constraints either as linear inequalities in the underlying polyhedral representation, or as state variables in shortest-path calculations. We show that the first case generalizes a common class of combinatorial bilevel problems where the follower's decisions are separable with respect to the leader's decisions. In this setting, the side constraints are indicator functions associated with arc flows, and we leverage polyhedral structure to derive an alternative single-level reformulation of the bilevel problem. The case where side constraints are incorporated as node states, in turn, generalizes classical robust optimization. For this scenario, we leverage network structure to derive an iterative augmenting state-space strategy akin to an L-shaped method. We evaluate both strategies on a bilevel competitive project selection problem and the robust traveling salesperson with time windows, observing considerable improvements in computational efficiency as compared to current state-of-the-art methods in the respective areas.
Binary and multinomial classification through evolutionary symbolic regression

We present three evolutionary symbolic regression-based classification algorithms for binary and multinomial datasets: GPLearnClf CartesianClf and ClaSyCo. Tested over 162 datasets and compared to three state-of-the-art machine learning algorithms---XGBoost, LightGBM, and a deep neural network---we find our algorithms to be competitive. Further, we demonstrate how to find the best method for one's dataset automatically, through the use of a state-of-the-art hyperparameter optimizer.
EVALUATION METHOD OF SEISMIC DEFORMATION FOR AIRPORT BASIC FACILITIES ON GRID-TYPE IMPROVED GROUND

A method of evaluating deformation of asphalt pavements on the ground improved by grid-type improvement was examined during and after earthquake. First, a liquefaction analysis was conducted to evaluate the effect of liquefaction countermeasure during the earthquake. Then, a consolidation analysis was conducted to evaluate ground deformation caused by dissipation of excess porewater pressure. To verify the effects of the countermeasure, the analysis results of the grid-type improved ground were compared with non-improved ground and chemical grouting improved ground. As a result, a deformation reducing effect equivalent to that of the chemical grouting improvement was obtained from the grid-type improvement. It was shown that the combination of the liquefaction analysis and consolidation analysis is useful for evaluating deformation of the ground improved by the grid-type improvement. Furthermore, the grid-type improvement with wide grid-spacing indicated an effect on reducing subsidence.
A Coupled Neural Circuit Design for Guillain-Barre Syndrome

Guillain-Barre syndrome is a rare neurological condition in which the human immune system attacks the peripheral nervous system. A peripheral nervous system appears as a diffusively connected system of mathematical models of neuron models, and the system's period becomes shorter than the periods of each neural circuit. The stimuli in the conduction path that will address the myelin sheath that has lost its function are received by the axons and are conveyed externally to the target organ, aiming to solve the problem of decreased nerve conduction. In the NEURON simulation environment, one can create a neuron model and define biophysical events that take place within the system for study. In this environment, signal transmission between cells and dendrites is obtained graphically. The simulated potassium and sodium conductance are replicated adequately, and the electronic action potentials are quite comparable to those measured experimentally. In this work, we propose an analog and digital coupled neuron model comprising individual excitatory and inhibitory neural circuit blocks for a low-cost and energy-efficient system. Compared to digital design, our analog design performs in lower frequency but gives a 32.3\% decreased energy efficiency. Thus, the resulting coupled analog hardware neuron model can be a proposed model for the simulation of reduced nerve conduction. As a result, the analog coupled neuron, (even with its greater design complexity) serious contender for the future development of a wearable sensor device that could help with Guillain-Barre syndrome and other neurologic diseases.
Ultra-fast synthesis and thermodynamic analysis of MoAlB by self-propagating high-temperature combustion synthesis

China Abstract . MoAlB as a typical member of the MAB phases has attracted much growing attention due to its unique properties. However, the low production of MoAlB powders limits its further development and potential applications. To respond this challenge, the ultra-fast preparation of high-purity MoAlB powders is achieved in a few seconds by self-propagating high temperature combustion synthesis (SHS) using the raw powder mixture at the atomic ratio of 1Mo/1.3Al/1B. The reaction mechanism in SHS process was obtained by analyzing the corresponding composition changes of starting materials. Furthermore, the thermodynamic prediction for the SHS reaction of Mo + (1+ y ) Al + B = MoAlB + y Al is consistent with the present experiments, where the preparation of MoAlB also conforms to two common self-propagating conditions of SHS. Enthalpy vs temperature curve shows that the adiabatic temperature of the reaction decreases with increasing the amount of excuse Al, but increases when pre-heating the reactants. The thermodynamic calculation method also provides a new idea for the preparation of other MAB phases by
Generating Diverse Vocal Bursts with StyleGAN2 and MEL-Spectrograms

We describe our approach for the generative emotional vocal burst task (ExVo Generate) of the ICML Expressive Vocalizations Competition. We train a conditional StyleGAN2 architecture on mel-spectrograms of preprocessed versions of the audio samples. The mel-spectrograms generated by the model are then inverted back to the audio domain. As a result, our generated samples substantially improve upon the baseline provided by the competition from a qualitative and quantitative perspective for all emotions. More precisely, even for our worst-performing emotion (awe), we obtain an FAD of 1.76 compared to the baseline of 4.81 (as a reference, the FAD between the train/validation sets for awe is 0.776).
BRL/Pisa/IIT SoftHand: A Low-Cost, 3D-Printed, Underactuated, Tendon-Driven Hand With Soft and Adaptive Synergies

This letter introduces the BRL/Pisa/IIT (BPI) SoftHand: a single actuator-driven, low-cost, 3D-printed, tendon-driven, underactuated robot hand that can be used to perform a range of grasping tasks. Based on the adaptive synergies of the Pisa/IIT SoftHand, we design a new joint system and tendon routing to facilitate the inclusion of both soft and adaptive synergies, which helps us balance durability, affordability and grasping performance of the hand. The focus of this work is on the design, simulation, synergies and grasping tests of this SoftHand. The novel phalanges are designed and printed based on linkages, gear pairs and geometric restraint mechanisms, and can be applied to most tendon-driven robotic hands. We show that the robot hand can successfully grasp and lift various target objects and adapt to hold complex geometric shapes, reflecting the successful adoption of the soft and adaptive synergies. We intend to open-source the design of the hand so that it can be built cheaply on a home 3D-printer.
SLIC: Self-Supervised Learning with Iterative Clustering for Human Action Videos

Self-supervised methods have significantly closed the gap with end-to-end supervised learning for image classification [13], [24]. In the case of human action videos, however, where both appearance and motion are significant factors of variation, this gap remains significant [28], [58]. One of the key reasons for this is that sampling pairs of similar video clips, a required step for many self-supervised contrastive learning methods, is currently done conservatively to avoid false positives. A typical assumption is that similar clips only occur temporally close within a single video, leading to insufficient examples of motion similarity. To mitigate this, we propose SLIC, a clustering-based self-supervised contrastive learning method for human action videos. Our key contribution is that we improve upon the traditional intra-video positive sampling by using iterative clustering to group similar video instances. This enables our method to leverage pseudo-labels from the cluster assignments to sample harder positives and negatives. SLIC outperforms state-of-the-art video retrieval baselines by $+15.4\%$ on top-1 recall on UCF101 and by $+5.7\%$ when directly transferred to HMDB51. With end-to-end finetuning for action classi-fication, SLIC achieves 83.2% top-1 accuracy $(+0.8\%)$ on UCF101 and 54.5% on HMDB51 $(+1.6\%$,. SLIC is also competitive with the state-of-the-art in action classification after self-supervised pretraining on Kinetics400.
Pathways to a clean gamma (phi3): From B to Super B-factories

The implementation of various methods for the determination of gamma through direct CP violation arising in the interference of b->c and b->u processes in charged as well as neutral B meson decays are considered. We show that the inclusion of D0 resulting from D*0-->D0+pi0(gamma) say via B-->KD*0 makes a significant difference in the attainable accuracy for gamma. Both exclusive and inclusive decays of the B+- and B0 to states containing D0/D0-bar followed by both inclusive and exclusive decays of the D0 are discussed. It is shown that with statistics which might be obtained at B factories (5-10 X 10^8 B-pairs) a 1-sigma determination of gamma to about +-5 degrees may be possible depending on the efficiency of reconstruction, backgrounds and the details of the decay amplitudes involved. The role of data from a charm factory as well as effects of D0 mixing are discussed. Extraction of gamma with accuracy that is roughly commensurate with the intrinsic theory error of these methods (i.e. around 0.1%), which is an important goal, will require >10^{10} B-pairs, namely a Super-B Factory.
TALCS: An Open-Source Mandarin-English Code-Switching Corpus and a Speech Recognition Baseline

This paper introduces a new corpus of Mandarin-English code-switching speech recognition—TALCS corpus, suitable for training and evaluating code-switching speech recognition systems. TALCS corpus is derived from real online one-to-one English teaching scenes in TAL education group, which contains roughly 587 hours of speech sampled at 16 kHz. To our best knowledge, TALCS corpus is the largest well labeled Mandarin-English code-switching open source automatic speech recognition (ASR) dataset in the world. In this paper, we will introduce the recording procedure in detail, including audio capturing devices and corpus environments. And the TALCS corpus is freely available for download under the permissive license 1 . Using TALCS corpus, we conduct ASR experiments in two popular speech recognition toolkits to make a baseline system, including ESPnet and Wenet. The Mixture Error Rate (MER) performance in the two speech recognition toolkits is compared in TALCS corpus. The experimental results implies that the quality of audio recordings and transcriptions are promising and the baseline system is workable.
Adversarial Example Detection in Deployed Tree Ensembles

Tree ensembles are powerful models that are widely used. However, they are susceptible to adversarial examples, which are examples that purposely constructed to elicit a misprediction from the model. This can degrade performance and erode a user’s trust in the model. Typically, approaches try to alleviate this problem by verifying how robust a learned ensemble is or robustifying the learning process. We take an alternative approach and attempt to detect adversarial examples in a post-deployment setting. We present a novel method for this task that works by analyzing an unseen example’s output conﬁguration, which is the set of predictions made by an ensemble’s constituent trees. Our approach works with any additive tree ensemble and does not require training a separate model. We evaluate our approach on three different tree ensemble learners. We empirically show that our method is currently the best adversarial detection method for tree ensembles.
Rivendell: Project-Based Academic Search Engine

Finding relevant research literature in online databases is a familiar challenge to all researchers. General search approaches trying to tackle this challenge fall into two groups: one-time search and life-time search. We observe that both approaches ignore unique attributes of the research domain and are affected by concept drift. We posit that in searching for research papers, a combination of a life-time search engine with an explicitly-provided context (project) provides a solution to the concept drift problem. We developed and deployed a project-based meta-search engine for research papers called Rivendell. Using Rivendell, we conducted experiments with 199 subjects, comparing project-based search performance to one-time and life-time search engines, revealing an improvement of up to 12.8 percent in project-based search compared to life-time search.