Miracle-dz/UniFAR-FTUs huggingface dataset:
Epitaxial InSb for elevated temperature operation of large IR focal plane arrays

The use of epitaxially grown indium antimonide (InSb) has previously been demonstrated for the production of large 2D focal plane arrays. It confers several advantages over conventional, bulk InSb photo-voltaic detectors, such as reduced cross-talk, however here we focus on the improvement in operating temperature that can be achieved because more complex structures can be grown. Diode resistance, imaging, NETD and operability results are presented for a progression of structures that reduce the diode leakage current as the temperature is raised above 80K, compared with a basic p+-n-n+ structure presented previously. These include addition of a thin region of InAlSb to reduce p-contact leakage current, and construction of the whole device from InAlSb to reduce thermal generation in the active region of the detector. An increase in temperature to 110K, whilst maintaining full 80K performance, is achieved, and imaging up to 130K is demonstrated. This gives the prospect of significant benefits for the cooling systems, including, for example, use of argon in Joule-Thomson coolers or an increase in the life and/or decrease in the cost; power consumption and cool-down time of Stirling engines by several tens of per cent.
New Methods & Metrics for LFQA tasks

Long-form question answering (LFQA) tasks require retrieving the documents pertinent to a query, using them to form a paragraph-length answer. Despite considerable progress in LFQA modeling, fundamental issues impede its progress: i) train/validation/test dataset overlap, ii) absence of automatic metrics and iii) generated answers not being"grounded"in retrieved documents. This work addresses every one these critical bottlenecks, contributing natural language inference/generation (NLI/NLG) methods and metrics that make significant strides to their alleviation.
Muon g-2 in a Type-X 2HDM assisted by inert scalars: probing at the LHC

A scenario augmenting the well known Type-X Two-Higgs doublet model (2HDM) with an additional inert doublet is proposed. The Type-X 2HDM is known to oﬀer a solution to the muon g − 2 anomaly for a light pseudoscalar. We show that the proposed framework can accomodate a heavier pseudoscalar on account of two-loop Barr-Zee (BZ) contributions to muon g − 2 stemming from the inert doublet. We subsequently explore an interesting τ + τ − + missing transverse energy signal that can be used to probe the present scenario at the 14 TeV LHC.
Faraday optical fiber current sensor with phase conjugate device

In a Faraday optical-fiber current sensor, it has many kinds of linear double refraction inevitably because of the circular degree error of the optical fiber, deformation of pressure, temperature effect and other reasons. It results in an additional phase difference, which will affect the detecting sensitivity and even decrease it to zero. A new method that can eliminate the effect of linear double refraction is offered in this paper with phase conjugate device after analyzing the foundational operation principle of the Faraday optical-fiber current sensor and the effect of the linear double refraction.
Analysis of a diffuse interface method for the Stokes-Darcy coupled problem

We consider the interaction between a free flowing fluid and a porous medium flow, where the free flowing fluid is described using the time dependent Stokes equations, and the porous medium flow is described using Darcy’s law in the primal formulation. To solve this problem numerically, we use a diffuse interface approach, where the weak form of the coupled problem is written on an extended domain which contains both Stokes and Darcy regions. This is achieved using a phase-field function which equals one in the Stokes region and zero in the Darcy region, and smoothly transitions between these two values on a diffuse region of width O(ϵ) around the Stokes-Darcy interface. We prove convergence of the diffuse interface formulation to the standard, sharp interface formulation, and derive rates of convergence. This is performed by deriving a priori error estimates for discretizations of the diffuse interface method, and by analyzing the modeling error of the diffuse interface approach at the continuous level. The convergence rates are also shown computationally in a numerical example.
Evaluating Software User Feedback Classifiers on Unseen Apps, Datasets, and Metadata

Listening to user's requirements is crucial to building and maintaining high quality software. Online software user feedback has been shown to contain large amounts of information useful to requirements engineering (RE). Previous studies have created machine learning classifiers for parsing this feedback for development insight. While these classifiers report generally good performance when evaluated on a test set, questions remain as to how well they extend to unseen data in various forms. This study evaluates machine learning classifiers performance on feedback for two common classification tasks (classifying bug reports and feature requests). Using seven datasets from prior research studies, we investigate the performance of classifiers when evaluated on feedback from different apps than those contained in the training set and when evaluated on completely different datasets (coming from different feedback platforms and/or labelled by different researchers). We also measure the difference in performance of using platform-specific metadata as a feature in classification. We demonstrate that classification performance is similar on feedback from unseen apps compared to seen apps in the majority of cases tested. However, the classifiers do not perform well on unseen datasets. We show that multi-dataset training or zero shot classification approaches can somewhat mitigate this performance decrease. Finally, we find that using metadata as features in classifying bug reports and feature requests does not lead to a statistically significant improvement in the majority of datasets tested. We discuss the implications of these results on developing user feedback classification models to analyse and extract software requirements.
Optimization of dummy pattern for mask data size reduction

Dummy ("waffling") features on masks improve pattern density on wafers, helping chemical-mechanical polishing (CMP) achieve uniform thickness of residual oxide. However, getting the required pattern density may substantially complicate mask creation algorithms, e.g., for metal lines at loose pitch not matching the waffling grid. Without proper optimization, multiple waffling passes with decreasing waffle sizes increase design database and mask writing time, not always achieving the planarization goal. In this work, we investigated the effect of waffle type, size, spacing, and number of waffling runs (passes) on die database with variable line density and pitch. We correlated the extracted pattern density with the simulated residual oxide thickness and proposed to use partial waffles created from the large size, original on-grid waffles, by subtracting the sized geometries of drawn lines running over them. We found that, surprisingly, multiple waffling passes do not significantly impact neither pattern density, nor the database file size; they are also not efficient in improving die planarity. In contrast, routines using partial waffles would produce very uniform, high density pattern, resulting in superb wafer planarity confirmed by the CMP simulation. However, this was accomplished at the expense of a threefold growth of the database size, as compared to the standard waffling routines, or about 30 time increase compared to the original drawn design. We then proposed an alternative approach using a "waffle add" CAD layer to achieve a significant reduction in waffle count and parasitic capacitance.
Parameter Differentiation based Multilingual Neural Machine Translation

Multilingual neural machine translation (MNMT) aims to translate multiple languages with a single model and has been proved successful thanks to effective knowledge transfer among different languages with shared parameters. However, it is still an open question which parameters should be shared and which ones need to be task-specific. Currently, the common practice is to heuristically design or search language-specific modules, which is difficult to find the optimal configuration. In this paper, we propose a novel parameter differentiation based method that allows the model to determine which parameters should be language-speciﬁc during training. Inspired by cellular differentiation, each shared parameter in our method can dynamically differentiate into more specialized types. We further deﬁne the differentiation criterion as inter-task gradient similarity. Therefore, parameters with conﬂicting inter-task gradients are more likely to be language-specific. Extensive experiments on multilingual datasets have demonstrated that our method signiﬁcantly outperforms various strong baselines with different parameter sharing conﬁgurations. Further analysis reveals that the parameter sharing configuration obtained by our method correlates well with the linguistic proximities.
Faster Rates for Compressed Federated Learning with Client-Variance Reduction

Due to the communication bottleneck in distributed and federated learning applications, algorithms using communication compression have attracted significant attention and are widely used in practice. Moreover, the huge number, high heterogeneity and limited availability of clients result in high client-variance. This paper addresses these two issues together by proposing compressed and client-variance reduced methods COFIG and FRECON. We prove an $O(\frac{(1+\omega)^{3/2}\sqrt{N}}{S\epsilon^2}+\frac{(1+\omega)N^{2/3}}{S\epsilon^2})$ bound on the number of communication rounds of COFIG in the nonconvex setting, where $N$ is the total number of clients, $S$ is the number of clients participating in each round, $\epsilon$ is the convergence error, and $\omega$ is the variance parameter associated with the compression operator. In case of FRECON, we prove an $O(\frac{(1+\omega)\sqrt{N}}{S\epsilon^2})$ bound on the number of communication rounds. In the convex setting, COFIG converges within $O(\frac{(1+\omega)\sqrt{N}}{S\epsilon})$ communication rounds, which, to the best of our knowledge, is also the first convergence result for compression schemes that do not communicate with all the clients in each round. We stress that neither COFIG nor FRECON needs to communicate with all the clients, and they enjoy the first or faster convergence results for convex and nonconvex federated learning in the regimes considered. Experimental results point to an empirical superiority of COFIG and FRECON over existing baselines.
What do Large Language Models Learn about Scripts?

Script Knowledge (Schank and Abelson, 1975) has long been recognized as crucial for language understanding as it can help in filling in unstated information in a narrative. However, such knowledge is expensive to produce manually and difficult to induce from text due to reporting bias (Gordon and Van Durme, 2013). In this work, we are interested in the scientific question of whether explicit script knowledge is present and accessible through pre-trained generative language models (LMs). To this end, we introduce the task of generating full event sequence descriptions (ESDs) given a scenario as a natural language prompt. Through zero-shot probing, we find that generative LMs produce poor ESDs with mostly omitted, irrelevant, repeated or misordered events. To address this, we propose a pipeline-based script induction framework (SIF) which can generate good quality ESDs for unseen scenarios (e.g., bake a cake). SIF is a two-staged framework that fine-tunes LM on a small set of ESD examples in the first stage. In the second stage, ESD generated for an unseen scenario is post-processed using RoBERTa-based models to filter irrelevant events, remove repetitions, and reorder the temporally misordered events. Through automatic and manual evaluations, we demonstrate that SIF yields substantial improvements (1-3 BLEU points) over a fine-tuned LM. However, manual analysis shows that there is great room for improvement, offering a new research direction for inducing script knowledge.
Ultrasound Speckle Suppression and Denoising using MRI-derived Normalizing Flow Priors

Ultrasonography offers an inexpensive, widely-accessible and compact medical imaging solution. However, compared to other imaging modalities such as CT and MRI, ultrasound images notoriously suffer from strong speckle noise, which originates from the random interference of sub-wavelength scattering. This deteriorates ultrasound image quality and makes interpretation challenging. We here propose a new unsupervised ultrasound speckle reduction and image denoising method based on maximum-a-posteriori estimation with deep generative priors that are learned from high-quality MRI images. To model the generative tissue reflectivity prior, we exploit normalizing flows, which in recent years have shown to be very powerful in modeling signal priors across a variety of applications. To facilitate generaliation, we factorize the prior and train our flow model on patches from the NYU fastMRI (fully-sampled) dataset. This prior is then used for inference in an iterative denoising scheme. We first validate the utility of our learned priors on noisy MRI data (no prior domain shift), and then turn to evaluating performance on both simulated and in-vivo ultrasound images from the PICMUS and CUBDL datasets. The results show that the method outperforms other (unsupervised) ultrasound denoising methods (NLM and OBNLM) both quantitatively and qualitatively.
Will you dance to the challenge?: predicting user participation of TikTok challenges

TikTok is a popular new social media, where users express themselves through short video clips. A common form of interaction on the platform is participating in "challenges", which are songs and dances for users to iterate upon. Challenge contagion can be measured through replication reach, i.e., users uploading videos of their participation in the challenges. The uniqueness of the TikTok platform where both challenge content and user preferences are evolving requires the combination of challenge and user representation. This paper investigates social contagion of TikTok challenges through predicting a user's participation. We propose a novel deep learning model, deepChallenger, to learn and combine latent user and challenge representations from past videos to perform this user-challenge prediction task. We collect a dataset of over 7,000 videos from 12 trending challenges on the ForYouPage, the app's landing page, and over 10,000 videos from 1303 users. Extensive experiments are conducted and the results show that our proposed deepChallenger (F1=0.494) outperforms baselines (F1=0.188) in the prediction task.
Electron drag force in EUV induced pulsed hydrogen plasmas

Extreme ultraviolet (EUV) induced pulsed plasma is unique due to its transient characteristics: the plasma switches between non-thermal state (when EUV power is ON at the beginning of the pulse) and thermal state (end of the pulse at ∼20 μs). It is shown that although electron drag force acting on nm size particles in hydrogen plasma is negligible compared to the ion drag force at the beginning of the pulse, however it can be dominant at the end of the pulse and can play important role in particle transport leading to defectivity issues for semiconductor chip production technologies.
Junction and dielectrically isolated lateral ESTs for power ICs

Conventional and dual-channel dielectrically isolated (DI) and junction-isolated (JI) lateral emitter switched thyristors (LESTs) have been fabricated using the reduced surface field (RESURF) principle. The breakdown voltage for then JI LEST was found to be greater than 700 V, while that for the DI LEST was up to 500 V depending on the silicon drift layer and isolating oxide layer thickness. The DI devices exhibited marginally higher forward voltage drops compared to the corresponding JI LESTs. The switching time for the DI structures was found to be an order of magnitude faster than for the JL LESTs, which 2D device simulation shows to be due to elimination of injection of carriers into the substrate. The dual-channel LESTs has a higher forward voltage drop than the conventional structures and exhibited high-voltage current saturation behavior. Dual-channel LEST structures with different anode structure, i.e., the conventional LEST, the anode shorted LEST and the hybrid Schottky LEST, were also examined. <<ETX>>
Leaderless Consensus of Heterogeneous Multiple Euler–Lagrange Systems With Unknown Disturbance

This article studies the leaderless consensus problem of heterogeneous multiple networked Euler–Lagrange systems subject to persistent disturbances with unknown constant biases, amplitudes, initial phases, and frequencies. The main characteristic of this study is that none of the agents has information of a common reference model or of a common reference trajectory. Therefore, the agents must simultaneously and in a distributed way: achieve consensus to a common reference model (group model); achieve consensus to a common reference trajectory; and reject the unknown disturbances. We show that this is possible via a suitable combination of techniques of distributed “observers,” internal model principle and adaptive regulation. The proposed design generalizes recent results on group model learning, which have been studied for linear agents over undirected networks. In this article, group model learning is achieved for Euler–Lagrange dynamics over directed networks in the presence of persistent unknown disturbances.
Enabling Real-Time On-Chip Audio Super Resolution for Bone-Conduction Microphones

Voice communication using an air-conduction microphone in noisy environments suffers from the degradation of speech audibility. Bone-conduction microphones (BCM) are robust against ambient noises but suffer from limited effective bandwidth due to their sensing mechanism. Although existing audio super-resolution algorithms can recover the high-frequency loss to achieve high-fidelity audio, they require considerably more computational resources than is available in low-power hearable devices. This paper proposes the first-ever real-time on-chip speech audio super-resolution system for BCM. To accomplish this, we built and compared a series of lightweight audio super-resolution deep-learning models. Among all these models, ATS-UNet was the most cost-efficient because the proposed novel Audio Temporal Shift Module (ATSM) reduces the network’s dimensionality while maintaining sufficient temporal features from speech audio. Then, we quantized and deployed the ATS-UNet to low-end ARM micro-controller units for a real-time embedded prototype. The evaluation results show that our system achieved real-time inference speed on Cortex-M7 and higher quality compared with the baseline audio super-resolution method. Finally, we conducted a user study with ten experts and ten amateur listeners to evaluate our method’s effectiveness to human ears. Both groups perceived a significantly higher speech quality with our method when compared to the solutions with the original BCM or air-conduction microphone with cutting-edge noise-reduction algorithms.
Chiral limits and effect of light on the Hofstadter butterfly in twisted bilayer graphene

We study the magnetic field induced Hofstadter butterfly in twisted bilayer graphene (TBG) in various kinds of situations. First, we study the equilibrium case and identify the interlayer hopping processes that are most crucial for the appearance of a Hofstadter butterfly. Surprisingly, the hopping processes that are important for the appearance of the Hofstadter butterfly can be categorized as AA stacking type that is interlayer hoppings between equivalent sublattices. This is in contrast to AB/BA-type hoppings that are important for the appearance of flat bands in magic angle TBG and were discussed in [1]. We also find that if AB-type interlayer-hopping processes are turned off the resulting model is chiral but differs from the model discussed in [1]. Therefore, TBG has two separate chiral limits one of them is important to understand the formation of flat bands and the other for the Hofstadter butterfly. Taking this as motivation we discuss how the role of AA-type hoppings in combination with lattice relaxation effects can make individual Landau levels slightly harder to resolve in an experimental setting than one would expect from a non-relaxed lattice setting. Finally, we consider the impact of different forms of light on the fractal structure of the butterfly. Particularly, we study the impact of circularly polarized light and longitudinal light originating from a waveguide. As the system is exposed to circularly polarized light we find butterflies with increasingly pronounced asymmetry with respect to energy E = 0. This is due to the introduction of a gap term that breaks the chiral symmetries for both of the two chiral limits mentioned above. Lastly, we study the effect of longitudinal light that can be produced at the exit of a waveguide, in a slightly simplified model. Here, we find that no additional terms that break chiral symmetry are introduced. Therefore, it is found to lead to no increase in asymmetry of the energy spectrum. In fact, we identify specific experimentally accessible driving regimes in which the TBG achieves any of the two chiral limits.
UV-SLAM: Unconstrained Line-Based SLAM Using Vanishing Points for Structural Mapping

In feature-based simultaneous localization and mapping (SLAM), line features complement the sparsity of point features, making it possible to map the surrounding environment structure. Existing approaches utilizing line features have primarily employed a measurement model that uses line re-projection. However, the direction vectors used in the 3D line mapping process cannot be corrected because the line measurement model employs only the lines’ normal vectors in the Plücker coordinate. As a result, problems like degeneracy that occur during the 3D line mapping process cannot be solved. To tackle the problem, this letter presents a UV-SLAM, which is an unconstrained line-based SLAM using vanishing points for structural mapping. This letter focuses on using structural regularities without any constraints, such as the Manhattan world assumption. For this, we use the vanishing points that can be obtained from the line features. The difference between the vanishing point observation calculated through line features in the image and the vanishing point estimation calculated through the direction vector is defined as a residual and added to the cost function of optimization-based SLAM. Furthermore, through Fisher information matrix rank analysis, we prove that vanishing point measurements guarantee a unique mapping solution. Finally, we demonstrate that the localization accuracy and mapping quality are improved compared to the state-of-the-art algorithms using public datasets.