Miracle-dz/UniFAR-FTUs huggingface dataset:
Smectic and Soap Bubble Optofluidic Lasers

Soap bubbles are simple, yet very unique and marvelous objects. They exhibit a number of interesting properties such as beautiful interference colors and the formation of minimal surfaces. Various optical phenomena have been studied in soap films and bubbles, but so far they were not employed as optical cavities. Here we demonstrate, that dye doped soap or smectic liquid crystal bubbles can support whispering gallery mode lasing, which is observed in the spectrum as hundreds of regularly spaced peaks, resembling a frequency comb. The lasing enabled the measurement of size changes as small as 10 nm in a millimeter-sized, $\sim$100 nm thick bubble. Bubble lasers were used as extremely sensitive electric field sensors with a smallest measurable electric field of 110 Vm$^{-1}$Hz$^{-1/2}$. They also enable the measurement of pressures up to a 100 bar with a resolution of 1.5 Pa, resulting in a dynamic range of almost $10^7$. By connecting the bubble to a reservoir of air, almost arbitrarily low pressure changes can be measured while maintaining an outstanding dynamic range. The demonstrated soap bubble lasers are a very unique type of microcavities which are one of the best electric field and pressure microsensors to date and could in future also be employed to study thin films and cavity optomechanics.
Timely Processing Of Updates From Multiple Sources

We consider a system where the updates from independent sources are disseminated via a publish-subscribe mechanism. The sources are the publishers and a decision process (DP), acting as a subscriber, derives decision updates from the source data. We derive the stationary expected age of information (AoI) of decision updates delivered to a monitor. We show that a lazy computation policy in which the DP may sit idle before computing its next decision update can reduce the average AoI at the monitor even though the DP exerts no control over the generation of source updates. This AoI reduction is shown to occur because lazy computation can offset the negative effect of high variance in the computation time.
Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers

Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems are unable to achieve improved performance in cross-language settings. In this paper, we propose a Multimodal Dual Attention Transformer (MDAT) model to improve cross-language SER. Our model utilises pre-trained models for multimodal feature extraction and is equipped with a dual attention mechanism including graph attention and co-attention to capture complex dependencies across different modalities and achieve improved cross-language SER results using minimal target language data. In addition, our model also exploits a transformer encoder layer for high-level feature representation to improve emotion classification accuracy. In this way, MDAT performs refinement of feature representation at various stages and provides emotional salient features to the classification layer. This novel approach also ensures the preservation of modality-specific emotional information while enhancing cross-modality and cross-language interactions. We assess our model's performance on four publicly available SER datasets and establish its superior effectiveness compared to recent approaches and baseline models.
Routing One Million Customers in a Handful of Minutes

In this paper, we propose a new dataset of Capacitated Vehicle Routing Problem instances which are up to two orders of magnitude larger than those in the currently used benchmarks. Despite these sizes might not have an immediate application to real-world logistic scenarios, we believe they could foster fresh new research efforts on the design of effective and efficient algorithmic components for routing problems. We provide computational results for such instances by running FILO2, an adaptation of the FILO algorithm proposed in Accorsi and Vigo (2021), designed to handle extremely large-scale CVRP instances. Solutions for such instances are obtained by using an a standard personal computer in a considerably short computing time, thus showing the effectiveness of the acceleration and pruning techniques already proposed in FILO. Finally, results of FILO2 on well-known literature instances show that the newly introduced changes improve the overall scalability of the approach with respect to the previous FILO design.
Optimal and Stable Multi-Layer Object Rearrangement on a Tabletop

Object rearrangement is a fundamental sub-task in accomplishing a great many physical tasks. As such, effectively executing rearrangement is an important skill for intelligent robots to master. In this study, we conduct the first algorithmic study on optimally solving the problem of Multi-layer Object Rearrangement on a Tabletop (MORT), in which one object may be relocated at a time, and an object can only be moved if other objects do not block its top surface. In addition, any intermediate structure during the reconfiguration process must be physically stable, i.e., it should stand without external support. To tackle the dual challenges of untangling the dependencies between objects and ensuring structural stability, we develop an algorithm that interleaves the computation of the optimal rearrangement plan and structural stability checking. Using a carefully constructed integer linear programming (ILP) model, our algorithm, Stability-Aware Rearrangement Programming (SARP), readily scales to optimally solve complex rearrangement problems of 3D structures with over 60 building blocks, with solution quality significantly outperforming natural greedy best-first approaches. Upon the publication of the manuscript source code and data will be available at https//github.com/arc-1/mort/.
Comparison of Pre-trained Language Models for Turkish Address Parsing

Transformer based pre-trained models such as BERT and its variants, which are trained on large corpora, have demonstrated tremendous success for natural language processing (NLP) tasks. Most of academic works are based on the English language; however, the number of multilingual and language specific studies increase steadily. Furthermore, several studies claimed that language specific models outperform multilingual models in various tasks. Therefore, the community tends to train or fine-tune the models for the language of their case study, specifically. In this paper, we focus on Turkish maps data and thoroughly evaluate both multilingual and Turkish based BERT, DistilBERT, ELECTRA and RoBERTa. Besides, we also propose a MultiLayer Perceptron (MLP) for fine-tuning BERT in addition to the standard approach of one-layer fine-tuning. For the dataset, a mid-sized Address Parsing corpus taken with a relatively high quality is constructed. Conducted experiments on this dataset indicate that Turkish language specific models with MLP fine-tuning yields slightly better results when compared to the multilingual fine-tuned models. Moreover, visualization of address tokens' representations further indicates the effectiveness of BERT variants for classifying a variety of addresses.
The Brauer-Manin obstruction on algebraic stacks

For algebraic stacks over a number field, we define their Brauer-Manin pairings, Brauer-Manin sets, and extend the descent theory of Colliot-Th\'el\`ene and Sansuc. By extending Sansuc's exact sequence, we show the torsionness of Brauer groups of stacks that are locally quotients of varieties by connected groups. With mild assumptions, for stacks that are locally quotients or Deligne-Mumford, we show that the Brauer-Manin obstruction coincides with some other cohomological obstructions such as obstructions given by torsors under connected groups or abelian gerbes, generalizing Harari's original results. For Brauer-Manin sets on these stacks, we show the properties such as descent along a torsor, product preservation are still correct. These results extend classical theories of those on varieties.
High-resolution SOFIA/EXES Spectroscopy of Water Absorption Lines in the Massive Young Binary W3 IRS 5

We present in this paper mid-infrared (5–8 μm) spectroscopy toward the massive young binary W3 IRS 5, using the Echelon Cross Echelle Spectrograph (EXES) spectrometer in high-resolution mode (R ∼ 50,000) from the NASA Stratospheric Observatory for Infrared Astronomy (SOFIA). Many (∼180) ν 2 = 1–0 and (∼90) ν 2 = 2–1 absorption rovibrational transitions are identified. Two hot components over 500 K and one warm component of 190 K are identified through Gaussian fittings and rotation diagram analysis. Each component is linked to a CO component identified in the IRTF/iSHELL observations (R = 88,100) through their kinematic and temperature characteristics. Revealed by the large scatter in the rotation diagram, opacity effects are important, and we adopt two curve-of-growth analyses, resulting in column densities of ∼1019 cm−2. In one analysis, the model assumes a foreground slab. The other assumes a circumstellar disk with an outward-decreasing temperature in the vertical direction. The disk model is favored because fewer geometry constraints are needed, although this model faces challenges as the internal heating source is unknown. We discuss the chemical abundances along the line of sight based on the CO-to-H2O connection. In the hot gas, all oxygen not locked in CO resides in water. In the cold gas, we observe a substantial shortfall of oxygen and suggest that the potential carrier could be organics in solid ice.
Self-Assembly of Rhamnolipids Bioamphiphiles: Understanding Structure-Properties Relationship using Small-Angle X-Ray Scattering

The structure-properties relationship of rhamnolipids, RLs, well known microbial bioamphiphiles (biosurfactants), is exlored in detail by coupling cryogenic transmission electron microscopy (cryo-TEM) and both ex situ and in situ small angle X-ray scattering (SAXS). The self-assembly of three RLs with reasoned variation of their molecular structure (RhaC10, RhaC10C10 and RhaRhaC10C10) and a rhamnose-free C10C10 fatty acid is studied in water as a function of pH. It is found that RhaC10 and RhaRhaC10C10 form micelles in a broad pH range and RhaC10C10 undergoes a micelle-to-vesicle transition from basic to acid pH occurring at pH 6.5. Modelling coupled to fitting SAXS data allows a good estimation of the hydrophobic core radius (or length), the hydrophilic shell thickness, the aggregation number and the surface area per RL. The essentially micellar morphology found for RhaC10 and
Constraint-aware and Ranking-distilled Token Pruning for Efficient Transformer Inference

Deploying pre-trained transformer models like BERT on downstream tasks in resource-constrained scenarios is challenging due to their high inference cost, which grows rapidly with input sequence length. In this work, we propose a constraint-aware and ranking-distilled token pruning method ToP, which selectively removes unnecessary tokens as input sequence passes through layers, allowing the model to improve online inference speed while preserving accuracy. ToP overcomes the limitation of inaccurate token importance ranking in the conventional self-attention mechanism through a ranking-distilled token distillation technique, which distills effective token rankings from the final layer of unpruned models to early layers of pruned models. Then, ToP introduces a coarse-to-fine pruning approach that automatically selects the optimal subset of transformer layers and optimizes token pruning decisions within these layers through improved L0 regularization. Extensive experiments on GLUE benchmark and SQuAD tasks demonstrate that ToP outperforms state-of-the-art token pruning and model compression methods with improved accuracy and speedups. ToP reduces the average FLOPs of BERT by 8.1X while achieving competitive accuracy on GLUE, and provides a real latency speedup of up to 7.4X on an Intel CPU. Code is available at https://github.com/microsoft/Moonlit/tree/main/ToP
ModMax oscillators and root-
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-like flows in supersymmetric quantum mechanics

We construct a deformation of any $(0+1)$-dimensional theory of $N$ bosons with $SO(N)$ symmetry which is driven by a function of conserved quantities that resembles the root-$T \overline{T}$ operator of $2d$ quantum field theories. In the special case of $N=2$ bosons and a harmonic oscillator potential, the solution to the flow equation is the ModMax oscillator of arXiv:2209.06296. We argue that the deforming operator is related, in a particular special regime, to the dimensional reduction of the $2d$ root-$T \overline{T}$ operator on a spatial circle. It follows that the ModMax oscillator is a dimensional reduction of the $4d$ ModMax theory to quantum mechanics, justifying the name. We then show how to construct a manifestly supersymmetric extension of this root-$T \overline{T}$-like operator for any $(0+1)$-dimensional theory with $SO(N)$ symmetry and $\mathcal{N}=2$ supersymmetry by defining a flow equation directly in superspace.
Multi-Agent Deep Reinforcement Learning for Distributed Load Restoration

This paper addresses the load restoration problem after power outage events. Our primary proposed methodology is using multi-agent deep reinforcement learning to optimize the load restoration process in distribution systems, modeled as networked microgrids, via determining the optimal operational sequence of circuit breakers (switches). An innovative invalid action masking technique is incorporated into the multi-agent method to handle both the physical constraints in the restoration process and the curse of dimensionality as the action space of operational decisions grows exponentially with the number of circuit breakers. The features of our proposed method include centralized training for multi-agents to overcome non-stationary environment problems, decentralized execution to ease the deployment, and zero constraint violations to prevent harmful actions. Our simulations are performed in OpenDSS and Python environments to demonstrate the effectiveness of the proposed approach using the IEEE 13, 123, and 8500-node distribution test feeders. The results show that the proposed algorithm can achieve a significantly better learning curve and stability than the conventional methods.
Enhancing Adversarial Training via Reweighting Optimization Trajectory

Despite the fact that adversarial training has become the de facto method for improving the robustness of deep neural networks, it is well-known that vanilla adversarial training suffers from daunting robust overfitting, resulting in unsatisfactory robust generalization. A number of approaches have been proposed to address these drawbacks such as extra regularization, adversarial weights perturbation, and training with more data over the last few years. However, the robust generalization improvement is yet far from satisfactory. In this paper, we approach this challenge with a brand new perspective -- refining historical optimization trajectories. We propose a new method named \textbf{Weighted Optimization Trajectories (WOT)} that leverages the optimization trajectories of adversarial training in time. We have conducted extensive experiments to demonstrate the effectiveness of WOT under various state-of-the-art adversarial attacks. Our results show that WOT integrates seamlessly with the existing adversarial training methods and consistently overcomes the robust overfitting issue, resulting in better adversarial robustness. For example, WOT boosts the robust accuracy of AT-PGD under AA-$L_{\infty}$ attack by 1.53\% $\sim$ 6.11\% and meanwhile increases the clean accuracy by 0.55\%$\sim$5.47\% across SVHN, CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets.
Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications

The multiple-choice knapsack problem (MCKP) is a classic NP-hard combinatorial optimization problem. Motivated by several significant real-world applications, this work investigates a novel variant of MCKP called chance-constrained multiple-choice knapsack problem (CCMCKP), where the item weights are random variables. In particular, we focus on the practical scenario of CCMCKP, where the probability distributions of random weights are unknown but only sample data is available. We first present the problem formulation of CCMCKP and then establish two benchmark sets. The first set contains synthetic instances and the second set is devised to simulate a real-world application scenario of a certain telecommunication company. To solve CCMCKP, we propose a data-driven adaptive local search (DDALS) algorithm. The main novelty of DDALS lies in its data-driven solution evaluation approach that can effectively handle unknown probability distributions of item weights. Moreover, in cases with unknown distributions, high intensity of chance constraints, limited amount of sample data and large-scale problems, it still exhibits good performance. Experimental results demonstrate the superiority of DDALS over other baselines on the two benchmarks. Additionally, ablation studies confirm the effectiveness of each component of the algorithm. Finally, DDALS can serve as the baseline for future research, and the benchmark sets are open-sourced to further promote research on this challenging problem.
Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?

The rapid advancement of Large Language Models (LLMs) has led to extensive discourse regarding their potential to boost the return of quantitative stock trading strategies. This discourse primarily revolves around harnessing the remarkable comprehension capabilities of LLMs to extract sentiment factors which facilitate informed and high-frequency investment portfolio adjustments. To ensure successful implementations of these LLMs into the analysis of Chinese financial texts and the subsequent trading strategy development within the Chinese stock market, we provide a rigorous and encompassing benchmark as well as a standardized back-testing framework aiming at objectively assessing the efficacy of various types of LLMs in the specialized domain of sentiment factor extraction from Chinese news text data. To illustrate how our benchmark works, we reference three distinctive models: 1) the generative LLM (ChatGPT), 2) the Chinese language-specific pre-trained LLM (Erlangshen-RoBERTa), and 3) the financial domain-specific fine-tuned LLM classifier(Chinese FinBERT). We apply them directly to the task of sentiment factor extraction from large volumes of Chinese news summary texts. We then proceed to building quantitative trading strategies and running back-tests under realistic trading scenarios based on the derived sentiment factors and evaluate their performances with our benchmark. By constructing such a comparative analysis, we invoke the question of what constitutes the most important element for improving a LLM's performance on extracting sentiment factors. And by ensuring that the LLMs are evaluated on the same benchmark, following the same standardized experimental procedures that are designed with sufficient expertise in quantitative trading, we make the first stride toward answering such a question.
On the local consequence of modal Product logic: standard completeness and decidability

Modal extensions of Product fuzzy logic can be considered both over Kripke models whose accessibility relation is valued, and over Kripke models with classical accessibility relation. We study the local consequence of the previous two modal Product logics. We prove that these logics are standard complete, in the sense that the logic defined over Kripke models evaluated over all product algebras coincides with that defined over Kripke models evaluated over the standard product algebra (with universe [0,1]). This holds both for the logic over classical Kripke frames, and for that over frames with a valued accessibility relation. Second, we prove that the previous logics are decidable.
Hybrid Precoder and Combiner Designs for Decentralized Parameter Estimation in mmWave MIMO Wireless Sensor Networks

Hybrid precoder and combiner designs are conceived for decentralized parameter estimation in millimeter wave (mmWave) multiple-input–multiple-output (MIMO) wireless sensor networks (WSNs). More explicitly, efficient pre- and post-processing of the sensor observations and received signal are proposed for the minimum mean square error (MMSE) estimation of a parameter vector. The proposed techniques exploit the limited scattering nature of the mmWave MIMO channel for formulating the hybrid transceiver design framework as a multiple measurement vectors (MMVs)-based sparse signal recovery problem. This is then solved using the iterative appealingly low-complexity simultaneous orthogonal matching pursuit (SOMP). Tailor-made designs are presented for WSNs operating under both total and per-sensor power constraints, while considering ideal noiseless as well as realistic noisy sensors. Furthermore, both the Bayesian Cramer–Rao lower bound and the centralized MMSE bound are derived for benchmarking the proposed decentralized estimation schemes. Our simulation results demonstrate the efficiency of the designs advocated and verify the analytical findings.
Shear viscosity expression for a graphene system in relaxation time approximation

We have gone through the detailed microscopic calculation of the shear viscosity of a 2-dimensional graphene system in the relaxation time approximation-based kinetic theory framework. After getting its final expressions, we compared it with the shear viscosity expressions of other possible 2-dimensional as well as 3-dimensional nonrelativistic and ultra-relativistic fluid systems. The aim of the comparison is to reveal how their different one-body dispersion relations affect their many-body fluid properties like shear viscosity and the viscosity to entropy density ratio. It is also aimed to reveal the 3-dimension to the 2-dimension transformation of their mathematical structures. We have numerically explored the differences in their order of magnitude and dependence on thermodynamical parameters-temperature and chemical potential. Marking two thermodynamical domains-Dirac fluid and Fermi liquid-for a 2-dimensional graphene system, we have noticed that shear viscosity, entropy density as well as their ratios decrease toward saturated values when one goes from Fermi liquid to Dirac fluid domain. When one shifts from mili-electron volt scales of temperature and chemical potential in condensed matter physics location to their mega-electron volt scales in high energy physics location, then the same results may be expected for hot quark matter case, where the transition from the neutron star to early universe domains may be considered as Fermi liquid to Dirac fluid transition.
BOUNDS FOR SPECTRAL PROJECTORS ON THE EUCLIDEAN TWO-DIMENSIONAL TORUS

We consider spectral projectors associated to the Euclidean Laplacian on the twodimensional torus, in the case where the spectral window is narrow. Bounds for their L to L operator norm are derived, extending the classical result of Sogge; a new question on the convolution kernel of the projector is introduced. The methods employed include l decoupling, small cap decoupling, and estimates of exponential sums.