Miracle-dz/UniFAR-FTUs huggingface dataset:
Bending effects and optical properties of WSe2 nanoribbons of topological phase

A WSe2 monolayer of 1T' phase is a large band gap quantum spin Hall insulator, supporting dissipationless charge and spin transports through the topologically protected edge states. In this work, we explore the nanoribbon forms of 1T' phase WSe2 by first-principles density functional calculations and the many-body perturbation GW and Bethe-Salpeter equation method. We found that the 1T' WSe2 nanoribbon can show topological edge states with a ribbon width of ~4-6 nm. Those edge bands show crossing through the Fermi level an odd number of times, with one kind of spin-polarization connecting the valence band continuum and conduction band continuum. The topological features of the edge bands hold even under small and medium bending in the nanoribbon, while large bending induces large band splitting, resulting in a topological switch-off in the edge bands. The semiconducting 1T' WSe2 nanoribbon shows a large tunability with bending in optical absorption spectra and exciton states. The lowest-energy exciton is changed from optically dark in the flat nanoribbon to bright in the bent nanoribbons. These properties in the 1T' WSe2 nanoribbons suggest potential applications in controllable quantum electronics and exciton-based quantum information processes.
Geometric quantum discord and coherence in a dipolar interacting magnetic system

The study of low-dimensional metal complexes has revealed fascinating characteristics regarding the ground-state crossover shown by spin-gaped systems. In this context, this work explores the effect of the quantum-level crossing, induced by the magnetic anisotropies of dipolar interaction, on the quantum discord and coherence of a dinuclear spin-1/2 system. The analytical expressions for the quantum discord, based on Schatten 1-norm, and the l 1 norm quantum coherence for dinuclear spin-1/2 systems, are provided in terms of the magnetic anisotropies. The results show that, while the quantum discord has a clear signature of the quantum level-crossing, the basis dependence of the axial quantum coherence hides the crossover regarding the measured basis. Moreover, global coherence was expressed in terms of the co-latitude and longitude angles of the Bloch sphere representation. Through this result, the average quantum coherence is numerically measured in order to obtain a basis-independent perspective for the l 1 quantum coherence. The results show that the average measurement revealed the signature of the energy-level crossover obtained in the measurement of quantum discord, being wholly stored within the correlations of the system, even in the absence of entanglement.
Large-scale Global Low-rank Optimization for Computational Compressed Imaging

Computational reconstruction plays a vital role in computer vision and computational photography. Most of the conventional optimization and deep learning techniques explore local information for reconstruction. Recently, nonlocal low-rank (NLR) reconstruction has achieved remarkable success in improving accuracy and generalization. However, the computational cost has inhibited NLR from seeking global structural similarity, which consequentially keeps it trapped in the tradeoff between accuracy and efficiency and prevents it from high-dimensional large-scale tasks. To address this challenge, we report here the global low-rank (GLR) optimization technique, realizing highly-efficient large-scale reconstruction with global self-similarity. Inspired by the self-attention mechanism in deep learning, GLR extracts exemplar image patches by feature detection instead of conventional uniform selection. This directly produces key patches using structural features to avoid burdensome computational redundancy. Further, it performs patch matching across the entire image via neural-based convolution, which produces the global similarity heat map in parallel, rather than conventional sequential block-wise matching. As such, GLR improves patch grouping efficiency by more than one order of magnitude. We experimentally demonstrate GLR's effectiveness on temporal, frequency, and spectral dimensions, including different computational imaging modalities of compressive temporal imaging, magnetic resonance imaging, and multispectral filter array demosaicing. This work presents the superiority of inherent fusion of deep learning strategies and iterative optimization, and breaks the persistent dilemma of the tradeoff between accuracy and efficiency for various large-scale reconstruction tasks.
Inferring ECG from PPG for Continuous Cardiac Monitoring Using Lightweight Neural Network

This paper presents a computational solution for continuous cardiac monitoring. While some smartwatches now allow users to obtain a 30-second ECG test by tapping a built-in bio-sensor, these short-term ECG tests often miss intermittent and asymptomatic abnormalities of the heart. It is also infeasible to expect persistently active user participation for long-term continuous cardiac monitoring in order to capture these and other types of abnormalities of the heart. To alleviate the need for continuous user attention and active participation, a lightweight neural network is designed to infer electrocardiogram (ECG) from the photoplethysmogram (PPG) signal sensed at the skin surface by a wearable optical sensor. To increase the utility of reconstructed ECG signals for screening cardiovascular diseases (CVDs), a diagnosis-oriented training strategy is developed to encourage the neural network to capture the pathological features of ECG. Model interpretation can be leveraged to obtain insights from data-driven models, for example, to reveal some associations between CVDs and ECG/PPG and to demonstrate how the neural network copes with motion artifacts in the ambulatory application. The experimental results on three datasets demonstrate the feasibility of inferring ECG from PPG, achieving a high fidelity of ECG reconstruction with only about 40K parameters.
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction

Despite impressive performance, deep neural networks require significant memory and computation costs, prohibiting their application in resource-constrained scenarios. Sparse training is one of the most common techniques to reduce these costs, however, the sparsity constraints add difficulty to the optimization, resulting in an increase in training time and instability. In this work, we aim to overcome this problem and achieve space-time co-efficiency. To accelerate and stabilize the convergence of sparse training, we analyze the gradient changes and develop an adaptive gradient correction method. Specifically, we approximate the correlation between the current and previous gradients, which is used to balance the two gradients to obtain a corrected gradient. Our method can be used with the most popular sparse training pipelines under both standard and adversarial setups. Theoretically, we prove that our method can accelerate the convergence rate of sparse training. Extensive experiments on multiple datasets, model architectures, and sparsities demonstrate that our method outperforms leading sparse training methods by up to \textbf{5.0\%} in accuracy given the same number of training epochs, and reduces the number of training epochs by up to \textbf{52.1\%} to achieve the same accuracy. Our code is available on: \url{https://github.com/StevenBoys/AGENT}.
Modeling Scattering Coefficients using Self-Attentive Complex Polynomials with Image-based Representation

Finding antenna designs that satisfy frequency requirements and are also optimal with respect to multiple physical criteria is a critical component in designing next generation hardware. However, such a process is non-trivial because the objective function is typically highly nonlinear and sensitive to subtle design change. Moreover, the objective to be optimized often involves electromagnetic (EM) simulations, which is slow and expensive with commercial simulation software. In this work, we propose a sample-efﬁcient and accurate surrogate model, named CZP (Constant Zeros Poles), to directly estimate the scattering coefﬁcients in the frequency domain of a given 2D planar antenna design, without using a simulator. CZP achieves this by predicting the complex zeros and poles for the frequency response of scattering coefﬁcients, which we have theoretically justiﬁed for any linear PDE, including Maxwell’s equations. Moreover, instead of using low-dimensional representations, CZP leverages a novel image-based representation for antenna topology inspired by the existing mesh-based EM simulation techniques, and attention-based neural network architectures. We demonstrate experimentally that CZP not only outperforms baselines in terms of test loss, but also is able to ﬁnd 2D antenna designs veriﬁable by commercial software with only 40k training samples, when coupling with advanced sequential search techniques like reinforcement learning.
Automatic Standardization of Arabic Dialects for Machine Translation

Based on an annotated multimedia corpus, television series Marāyā 2013, we dig into the question of “automatic standardization” of Arabic dialects for machine translation. Here we distinguish between rule-based machine translation and statistical machine translation. Machine translation from Arabic most of the time takes standard or modern Arabic as the source language and produces quite satisfactory translations thanks to the availability of the translation memories necessary for training the models. The case is different for the translation of Arabic dialects. The productions are much less efficient. In our research we try to apply machine translation methods to a dialect/standard (or modern) Arabic pair to automatically produce a standard Arabic text from a dialect input, a process we call “automatic standardization”. we opt here for the application of "statistical models" because "automatic standardization" based on rules is more hard with the lack of "diglossic" dictionaries on the one hand and the difficulty of creating linguistic rules for each dialect on the other. Carrying out this research could then lead to combining "automatic standardization" software and automatic translation software so that we take the output of the first software and introduce it as input into the second one to obtain at the end a quality machine translation. This approach may also have educational applications such as the development of applications to help understand different Arabic dialects by transforming dialectal texts into standard Arabic.
Active Learning for Abstractive Text Summarization

Construction of human-curated annotated datasets for abstractive text summarization (ATS) is very time-consuming and expensive because creating each instance requires a human annotator to read a long document and compose a shorter summary that would preserve the key information relayed by the original document. Active Learning (AL) is a technique developed to reduce the amount of annotation required to achieve a certain level of machine learning model performance. In information extraction and text classification, AL can reduce the amount of labor up to multiple times. Despite its potential for aiding expensive annotation, as far as we know, there were no effective AL query strategies for ATS. This stems from the fact that many AL strategies rely on uncertainty estimation, while as we show in our work, uncertain instances are usually noisy, and selecting them can degrade the model performance compared to passive annotation. We address this problem by proposing the first effective query strategy for AL in ATS based on diversity principles. We show that given a certain annotation budget, using our strategy in AL annotation helps to improve the model performance in terms of ROUGE and consistency scores. Additionally, we analyze the effect of self-learning and show that it can further increase the performance of the model.
Elastic relationships in layered composite media with approximation for the case of thin films on a thick substrate

A general theory is presented for the elastic interactions in a composite plate of layers with different relaxed planar dimensions. Solutions are obtained for the case of equivalent elastic properties and for the case of different elastic properties among the layers. Approximations for the case of thin films on a substrate lead to the governing equations for stresses in multilayered thin‐film systems. Finally, the experimental determination of the planar substrate strain, which is first order in the film/substrate thickness ratio, is presented as a rigorous test of the correctness of the theory.
A rocky exoplanet classification method and its application to calculating surface pressure and surface temperature

With over 5,000 exoplanets currently detected, there is a need for a primary classification method to prioritise candidates for biosignature observations. Here, we develop a classification method to categorise rocky exoplanets based on their closest solar system analogue using available data of observed stellar and planetary features, masses, and radii, to model non-thermal atmospheric escape, thermal atmospheric escape, and stellar irradiation boundaries. Applying this classification method to the 720 rocky exoplanets in our sample with uncertainties in planetary masses, radii, stellar temperatures, and fluxes propagated via a Monte Carlo model indicates that 22% ± 8% are Mercury analogues, 39% ± 4% are Mars analogues, 11% ± 1% are Venus analogues, 2% ± 1% are Earth analogues, and 26% ± 12% are without a known planetary counterpart in our solar system. Extrapolating to conditions on LHS 3844b and GJ 1252b, our classification method gives results reasonably consistent with current observations. Subsequently, to demonstrate the functionality of this classification method, we plot our catalogued sample of exoplanets on an adjusted surface pressure versus temperature phase diagram, presenting more realistic estimates of the potential surface phases (gas, liquid or ice). Our new classification method could help target selection for future exoplanet characterisation missions.
Robust knapsack ordering for a partially-informed newsvendor with budget constraint

This paper studies the multi-item newsvendor problem with a constrained budget and information about demand limited to its range, mean and mean absolute deviation. We consider a minimax model that determines order quantities by minimizing the expected overage and underage costs for the worst-case demand distributions. The resulting optimization problem turns out to be solvable by a method reminiscent of the greedy algorithm that solves the continuous knapsack problem, purchasing items in order of marginal value. This method has lower computational complexity compared to directly solving the model and leads to a simple policy that (i) sorts items based on their marginal eﬀect on the total cost and (ii) determines order quantities according to this ranking until the budget is spent.
Applying Automated Machine Translation to Educational Video Courses

We studied the capability of automated machine translation in the online video education space by automatically translating Khan Academy videos with state-of-the-art translation models and applying text-to-speech synthesis and audio/video synchronization to build engaging videos in target languages. We also analyzed and established two reliable translation confidence estimators based on round-trip translations in order to efficiently manage translation quality and reduce human translation effort. Finally, we developed a deployable system to deliver translated videos to end users and collect user corrections for iterative improvement.
Assigning Agents to Increase Network-Based Neighborhood Diversity

Motivated by real-world applications such as the allocation of public housing, we examine the problem of assigning a group of agents to vertices (e.g., spatial locations) of a network so that the diversity level is maximized. Specifically, agents are of two types (characterized by features), and we measure diversity by the number of agents who have at least one neighbor of a different type. This problem is known to be NP-hard, and we focus on developing approximation algorithms with provable performance guarantees. We first present a local-improvement algorithm for general graphs that provides an approximation factor of 1/2. For the special case where the sizes of agent subgroups are similar, we present a randomized approach based on semidefinite programming that yields an approximation factor better than 1/2. Further, we show that the problem can be solved efficiently when the underlying graph is treewidth-bounded and obtain a polynomial time approximation scheme (PTAS) for the problem on planar graphs. Lastly, we conduct experiments to evaluate the per-performance of the proposed algorithms on synthetic and real-world networks.
Grokking modular arithmetic

We present a simple neural network that can learn modular arithmetic tasks and exhibits a sudden jump in generalization known as ``grokking''. Concretely, we present (i) fully-connected two-layer networks that exhibit grokking on various modular arithmetic tasks under vanilla gradient descent with the MSE loss function in the absence of any regularization; (ii) evidence that grokking modular arithmetic corresponds to learning specific feature maps whose structure is determined by the task; (iii) analytic expressions for the weights -- and thus for the feature maps -- that solve a large class of modular arithmetic tasks; and (iv) evidence that these feature maps are also found by vanilla gradient descent as well as AdamW, thereby establishing complete interpretability of the representations learnt by the network.
Chiral organic molecular structures supported by multilayer surfaces

We study numerically the dynamics of acetanilide (ACN) molecules placed on a flat surface of a multilayer hexagonal boron nitride structure. We demonstrate that the ACN molecules, being achiral in three dimensions, become chiral after being placed on the substrate. Homochirality of the ACN molecules leads to stable secondary structures stabilized by hydrogen bonds between peptide groups of the molecules. Numerical simulations of systems of such molecules reveal that the structure of the resulting hydrogen-bond chains depends on the isomeric composition of the molecules. If all molecules are homochiral (i.e. only one isomer is present), they form secondary structures (chains of hydrogen bonds in the shapes of arcs, circles, and spirals). If the molecules at the substrate form a racemic mixture, then no regular secondary structures appear, and only curvilinear chains of hydrogen bonds of random shapes can emerge. A hydrogen-bond chain can form a straight zigzag only if it has an alternation of isomers. Such chains can create two-dimensional (2D) regular lattices, or 2D crystals. The melting scenarios of such 2D crystals depend on density of its coverage of the substrate. At 25% coverage, melting occurs continuously in a certain temperature interval. For a complete coverage, melting occurs at 415-470 K due to a shift of 11% of all molecules into the second layer of the substrate.
Experimental and Theoretical Study of Solitary-like Wave Dynamics of Liquid Film Flows over a Vibrated Inclined Plane

Solitary-like surface waves that originate from the spatio-temporal evolution of falling liquid films have been the subject of theoretical and experimental research due to their unique properties that are not readily observed in the physical system of other nature. Here we investigate, experimentally and theoretically, the dynamics of solitary-like surface waves in a liquid layer on an inclined plane that is subjected to a harmonic low-frequency vibration in the range from 30 to 50 Hz. We demonstrate that the vibration results in a decrease in the average and peak amplitude of the long solitary-like surface waves. However, the speed of these waves remains largely unaffected by the vibration, implying that they may propagate over large distances almost without changing their amplitude, thus rendering them suitable for a number of practical applications, where the immunity of pulses that carry information to external vibrations is required.
Understanding the (In)Effectiveness of Content Moderation: A Case Study of Facebook in the Context of the U.S. Capitol Riot

Social media networks commonly employ content moderation as a tool to limit the spread of harmful content. However, the efficacy of this strategy in limiting the delivery of harmful content to users is not well understood. In this paper, we create a framework to quantify the efficacy of content moderation and use our metrics to analyze content removal on Facebook within the U.S. news ecosystem. In a data set of over 2M posts with 1.6B user engagements collected from 2,551 U.S. news sources before and during the Capitol Riot on January 6, 2021, we identify 10,811 removed posts. We find that the active engagement life cycle of Facebook posts is very short, with 90% of all engagement occurring within the first 30 hours after posting. Thus, even relatively quick intervention allowed significant accrual of engagement before removal, and prevented only 21% of the predicted engagement potential during a baseline period before the U.S. Capitol attack. Nearly a week after the attack, Facebook began removing older content, but these removals occurred so late in these posts' engagement life cycles that they disrupted less than 1% of predicted future engagement, highlighting the limited impact of this intervention. Content moderation likely has limits in its ability to prevent engagement, especially in a crisis, and we recommend that other approaches such as slowing down the rate of content diffusion be investigated.
Randomization advice and ambiguity aversion

We design and implement lab experiments to evaluate the normative appeal of behavior arising from models of ambiguity-averse preferences. We report two main empirical findings. First, we demonstrate that behavior reflects an incomplete understanding of the problem, providing evidence that subjects do not act on the basis of preferences alone. Second, additional clarification of the decision making environment pushes subjects' choices in the direction of ambiguity aversion models, regardless of whether or not the choices are also consistent with subjective expected utility, supporting the position that subjects find such behavior normatively appealing.
Spin Glass Behavior in Amorphous Cr2Ge2Te6 Phase‐Change Alloy

The layered crystal structure of Cr2Ge2Te6 shows ferromagnetic ordering at the two‐dimensional limit, which holds promise for spintronic applications. However, external voltage pulses can trigger amorphization of the material in nanoscale electronic devices, and it is unclear whether the loss of structural ordering leads to a change in magnetic properties. Here, it is demonstrated that Cr2Ge2Te6 preserves the spin‐polarized nature in the amorphous phase, but undergoes a magnetic transition to a spin glass state below 20 K. Quantum‐mechanical computations reveal the microscopic origin of this transition in spin configuration: it is due to strong distortions of the CrTeCr bonds, connecting chromium‐centered octahedra, and to the overall increase in disorder upon amorphization. The tunable magnetic properties of Cr2Ge2Te6 can be exploited for multifunctional, magnetic phase‐change devices that switch between crystalline and amorphous states.