Miracle-dz/UniFAR-FTUs huggingface dataset:
Fibercuit: Prototyping High-Resolution Flexible and Kirigami Circuits with a Fiber Laser Engraver

Prototyping compact devices with unique form factors often requires the PCB manufacturing process to be outsourced, which can be expensive and time-consuming. In this paper, we present Fibercuit, a set of rapid prototyping techniques to fabricate high-resolution, flexible circuits on-demand using a fiber laser engraver. We showcase techniques that can laser cut copper-based composites to form fine-pitch conductive traces, laser fold copper substrates that can form kirigami structures, and laser solder surface-mount electrical components using off-the-shelf soldering pastes. Combined with our software pipeline, an end user can design and fabricate flexible circuits which are dual-layer and three-dimensional, thereby exhibiting a wide range of form factors. We demonstrate Fibercuit by showcasing a set of examples, including a custom dice, flex cables, custom end-stop switches, electromagnetic coils, LED earrings and a circuit in the form of kirigami crane.
Safe Perception-Based Control with Minimal Worst-Case Dynamic Regret

—We enable safe control of linear time-varying systems in the presence of unknown and unpredictable process and measurement noise. We introduce a control algorithm that minimizes dynamic regret , i.e. , that minimizes the subop-timality against an optimal clairvoyant controller that knows the unpredictable future a priori. Speciﬁcally, our algorithm minimizes the worst-case dynamic regret among all possible noise realizations given a worst-case total noise magnitude. To this end, the control algorithm accounts for three key challenges: safety constraints; partially-observed time-varying systems; and unpredictable process and measurement noise. We are motivated by the future of autonomy where robots will autonomously perform complex tasks despite unknown and unpredictable disturbances leveraging their on-board control and perception capabilities. To synthesize our minimal-regret perception-based controller, we formulate a constrained semi-deﬁnite program based on a System Level Synthesis approach we enable for partially-observed time-varying systems. We validate our algorithm in simulated scenarios, including trajectory tracking scenarios of a hovering quadrotor collecting GPS and IMU measurements and of an omnidirectional robot collecting range measurements from landmarks. Our algorithm is observed to be superior to either or both the H 2 and H ∞ controllers, demonstrating a Best of Both Worlds performance.
NeIF: Representing General Reflectance as Neural Intrinsics Fields for Uncalibrated Photometric Stereo

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by unknown light. Existing solutions alleviate the ambiguity by either explicitly associating reflectance to light conditions or resolving light conditions in a supervised manner. This paper establishes an implicit relation between light clues and light estimation and solves UPS in an unsupervised manner. The key idea is to represent the reflectance as four neural intrinsics fields, i.e., position, light, specular, and shadow, based on which the neural light field is implicitly associated with light clues of specular reflectance and cast shadow. The unsupervised, joint optimization of neural intrinsics fields can be free from training data bias as well as accumulating error, and fully exploits all observed pixel values for UPS. Our method achieves a superior performance advantage over state-of-the-art UPS methods on public and self-collected datasets, under regular and challenging setups. The code will be released soon.
Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI

The effective representation, precessing, analysis, and visualization of large-scale structured data over graphs are gaining a lot of attention. So far most of the literature has focused on real-valued signals. However, signals are often sparse in the Fourier domain, and more informative and compact representations for them can be obtained using the complex envelope of their spectral components, as opposed to the original real-valued signals. Motivated by this fact, in this work we generalize graph convolutional neural networks (GCN) to the complex domain, deriving the theory that allows to incorporate a complex-valued graph shift operators (GSO) in the definition of graph filters (GF) and process complex-valued graph signals (GS). The theory developed can handle spatio-temporal complex network processes. We prove that complex-valued GCNs are stable with respect to perturbations of the underlying graph support, the bound of the transfer error and the bound of error propagation through multiply layers. Then we apply complex GCN to power grid state forecasting, power grid cyber-attack detection and localization.
EEG-BBNet: a Hybrid Framework for Brain Biometric using Graph Connectivity

Brain biometrics based on electroencephalography (EEG) have been used increasingly for personal identification. Traditional machine learning techniques as well as modern day deep learning methods have been applied with promising results. In this paper we present EEG-BBNet, a hybrid network which integrates convolutional neural networks (CNN) with graph convolutional neural networks (GCNN). The benefit of the CNN in automatic feature extraction and the capability of GCNN in learning connectivity between EEG electrodes through graph representation are jointly exploited. We examine various connectivity measures, namely the Euclidean distance, Pearson's correlation coefficient, phase-locked value, phase-lag index, and Rho index. The performance of the proposed method is assessed on a benchmark dataset consisting of various brain-computer interface (BCI) tasks and compared to other state-of-the-art approaches. We found that our models outperform all baselines in the event-related potential (ERP) task with an average correct recognition rates up to 99.26% using intra-session data. EEG-BBNet with Pearson's correlation and RHO index provide the best classification results. In addition, our model demonstrates greater adaptability using inter-session and inter-task data. We also investigate the practicality of our proposed model with smaller number of electrodes. Electrode placements over the frontal lobe region appears to be most appropriate with minimal lost in performance.
Lessons from a Space Lab - An Image Acquisition Perspective

The use of deep learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models trained on synthetic data are often susceptible to performance degradation when tested in real-world environments. In this context, the Interdisciplinary Center of Security, Reliability and Trust (SnT) at the University of Luxembourg has developed the “SnT Zero-G Lab,” for training and validating vision-based space algorithms in conditions emulating real-world space environments. An important aspect of the SnT Zero-G Lab development was the equipment selection. From the lessons learned during the lab development, this article presents a systematic approach combining market survey and experimental analyses for equipment selection. In particular, the article focuses on the image acquisition equipment in a space lab: background materials, cameras, and illumination lamps. The results from the experiment analyses show that the market survey complimented by experimental analyses is required for effective equipment selection in a space lab development project.
NET-FLEET: achieving linear convergence speedup for fully decentralized federated learning with heterogeneous data

Federated learning (FL) has received a surge of interest in recent years thanks to its benefits in data privacy protection, efficient communication, and parallel data processing. Also, with appropriate algorithmic designs, one could achieve the desirable linear speedup for convergence effect in FL. However, most existing works on FL are limited to systems with i.i. d. data and centralized parameter servers and results on decentralized FL with heterogeneous datasets remains limited. Moreover, whether or not the linear speedup for convergence is achievable under fully decentralized FL with data heterogeneity remains an open question. In this paper, we address these challenges by proposing a new algorithm, called NET-FLEET, for fully decentralized FL systems with data heterogeneity. The key idea of our algorithm is to enhance the local update scheme in FL (originally intended for communication efficiency) by incorporating a recursive gradient correction technique to handle heterogeneous datasets. We show that, under appropriate parameter settings, the proposed NET-FLEET algorithm achieves a linear speedup for convergence. We further conduct extensive numerical experiments to evaluate the performance of the proposed NET-FLEET algorithm and verify our theoretical findings.
Sequence Prediction under Missing Data: An RNN Approach without Imputation

Missing data scenarios are very common in ML applications in general and time-series/sequence applications are no exceptions. This paper pertains to a novel Recurrent Neural Network (RNN) based solution for sequence prediction under missing data. Our method is distinct from all existing approaches. It tries to encode the missingness patterns in the data directly without trying to impute data either before or during model building. Our encoding is lossless and achieves compression. It can be employed for both sequence classification and forecasting. We focus on forecasting here in a general context of multi-step prediction in presence of possible exogenous inputs. In particular, we propose novel variants of Encoder-Decoder (Seq2Seq) RNNs for this. The encoder here adopts the above mentioned pattern encoding, while at the decoder which has a different structure, multiple variants are feasible. We demonstrate the utility of our proposed architecture via multiple experiments on both single and multiple sequence (real) data-sets. We consider both scenarios where (i)data is naturally missing and (ii)data is synthetically masked.
The Long Stare at Hercules X-1. I. Emission Lines from the Outer Disk, the Magnetosphere Boundary, and the Accretion Curtain

Hercules X-1 is a nearly edge-on accreting X-ray pulsar with a warped accretion disk, precessing with a period of about 35 days. The disk precession allows for unique and changing sightlines toward the X-ray source. To investigate the accretion flow at a variety of sightlines, we obtained a large observational campaign on Her X-1 with XMM-Newton (380 ks exposure) and Chandra (50 ks exposure) for a significant fraction of a single disk precession cycle, resulting in one of the best data sets taken to date on a neutron star X-ray binary. Here we present the spectral analysis of the high state high-resolution grating and CCD data sets, including the extensive archival data available for this famous system. The observations reveal a complex Fe K region structure, with three emission line components of different velocity widths. Similarly, the high-resolution soft X-ray spectra reveal a number of emission lines of various widths. We correct for the uncertain gain of the European Photon Imaging Camera pn Timing mode spectra, and track the evolution of these spectral components with Her X-1 precession phase and observed luminosity. We find evidence for three groups of emission lines, the first of which originates in the outer accretion disk (105 R G from the neutron star). The second line group plausibly originates at the boundary between the inner disk and the pulsar magnetosphere (103 R G). The last group is too broad to arise in the magnetically truncated disk and instead must originate very close to the neutron star surface, likely from X-ray reflection from the accretion curtain (∼102 R G).
Have your QEC and Bandwidth too! A lightweight cryogenic decoder for common / trivial errors, and efﬁcient bandwidth + execution management otherwise.

The overheads of classical decoding for quantum error correction grow rapidly with the number of logical qubits and their correction code distance. Decoding at room temperature is bottle-necked by refrigerator I/O bandwidth while cryogenic on-chip decoding is limited by area/power/thermal budget. To overcome these overheads, we are motivated by the observation that in the common case (over 90% of the time), error signatures are fairly trivial with high redundancy / sparsity, since the error correction codes are over-provisioned to be able to correct for uncommon worst-case complex scenarios (to ensure substantially low logical error rates). If suitably exploited, these trivial signatures can be decoded and corrected with insigniﬁcant overhead, thereby alleviating the bottlenecks described above, while still handling the worst-case complex signatures by state-of-the-art means. Our proposal, targeting Surface Codes, consists of:
Enhanced photocurrent efficiency of a carbon nanotube p–n junction electromagnetically coupled to a photonic structure

We present photocurrent power-enhancement calculations of a carbon nanotube p–n junction electromagnetically coupled to a highly efficient photonic structure. Particular attention is paid to a GaAs photonic structure specifically modified to increase the intensity of infrared light onto the nanotube region for effective energy conversion. Using finite-difference time-domain calculations, we compute a significant increase in electric field intensity in the nanotube region which enables an estimation of power efficiency. These results demonstrate the potential of using a photonic structure to couple large-scale infrared sources with carbon nanotubes while still retaining all the unique optoelectronic properties found at the nanoscale.
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation

Building dialogue generation systems in a zero-shot scenario remains a huge challenge, since the typical zero-shot approaches in dialogue generation rely heavily on large-scale pre-trained language generation models such as GPT-3 and T5. The research on zero-shot dialogue generation without cumbersome language models is limited due to lacking corresponding parallel dialogue corpora. In this paper, we propose a simple but effective Multilingual learning framework for Zero-shot Dialogue Generation (dubbed as MulZDG) that can effectively transfer knowledge from an English corpus with large-scale training samples to a non-English corpus with zero samples. Besides, MulZDG can be viewed as a multilingual data augmentation method to improve the performance of the resource-rich language. First, we construct multilingual code-switching dialogue datasets via translation utterances randomly selected from monolingual English datasets. Then we employ MulZDG to train a unified multilingual dialogue model based on the code-switching datasets. The MulZDG can conduct implicit semantic alignment between different languages. Experiments on DailyDialog and DSTC7 datasets demonstrate that MulZDG not only achieve competitive performance under zero-shot case compared to training with sufficient examples but also greatly improve the performance of the source language.
Multi-modal imaging using a cascaded microscope design

We present a multi-modal fiber array snapshot technique (M-FAST) based on an array of 96 compact cameras placed behind a primary objective lens and a fiber bundle array. Our technique is capable of large-area, high-resolution, multi-channel video acquisition. The proposed design provides two key improvements to prior cascaded imaging system approaches: a novel optical arrangement that accommodates the use of planar camera arrays, and a new ability to acquire multi-modal image data acquisition. M-FAST is a multi-modal, scalable imaging system that can acquire snapshot dual-channel fluorescence images as well as differential phase contrast measurements over a large 6.59 mm × 9.74 mm field-of-view at 2.2-μm center full-pitch resolution.
Transcending XAI Algorithm Boundaries through End-User-Inspired Design

The boundaries of existing explainable artificial intelligence (XAI) algorithms are confined to problems grounded in technical users' demand for explainability. This research paradigm disproportionately ignores the larger group of non-technical end users, who have a much higher demand for AI explanations in diverse explanation goals, such as making safer and better decisions and improving users' predicted outcomes. Lacking explainability-focused functional support for end users may hinder the safe and accountable use of AI in high-stakes domains, such as healthcare, criminal justice, finance, and autonomous driving systems. Built upon prior human factor analysis on end users' requirements for XAI, we identify and model four novel XAI technical problems covering the full spectrum from design to the evaluation of XAI algorithms, including edge-case-based reasoning, customizable counterfactual explanation, collapsible decision tree, and the verifiability metric to evaluate XAI utility. Based on these newly-identified research problems, we also discuss open problems in the technical development of user-centered XAI to inspire future research. Our work bridges human-centered XAI with the technical XAI community, and calls for a new research paradigm on the technical development of user-centered XAI for the responsible use of AI in critical tasks.
Precoding for High-Throughput Satellite Communication Systems: A Survey

With the expanding demand for high data rates and extensive coverage, high throughput satellite (HTS) communication systems are emerging as a key technology for future communication generations. However, current frequency bands are increasingly congested. Until the maturity of communication systems to operate on higher bands, the solution is to exploit the already existing frequency bands more efficiently. In this context, precoding emerges as one of the prolific approaches to increasing spectral efficiency. This survey presents an overview and a classification of the recent precoding techniques for HTS communication systems from two main perspectives: 1) a problem formulation perspective and 2) a system design perspective. From a problem formulation point of view, precoding techniques are classified according to the precoding optimization problem, group, and level. From a system design standpoint, precoding is categorized based on the system architecture, the precoding implementation, and the type of the provided service. Further, practical system impairments are discussed, and robust precoding techniques are presented. Finally, future trends in precoding for satellites are addressed to spur further research.
A Systematic Study of Spin‐Dependent Recombination in GaAs1−xNx as a Function of Nitrogen Content

A systematic study of spin‐dependent recombination (SDR) under steady‐state optical pumping conditions as a function of nitrogen content, x, in dilute nitride alloys of the form GaAs1−xNx is reported. Use of high‐excitation power densities up to 107 W cm−2 allows measurement of the full SDR versus power curves, even at relatively high nitrogen contents of x = 0.039. Alloy contents for 0.022≤x≤0.039 are determined within δx = ±0.005 by fitting the photoluminescence (PL) spectra using a Roosbroeck–Shockley relation, and values consistent with those obtained by studying the intensity of the GaN‐like LO2 Raman mode are found. PL intensity increases by a factor known as the SDR ratio when switching from linearly to circularly polarized pump excitation. This factor reaches 5 for x = 0.022 and decreases with increasing x, falling to 1.5 for x = 0.039. Moreover, the excitation power required for maximum SDR increases with increasing x, varying from 0.6 mW for x = 0.022 to 15 mW for x = 0.039. These observations indicate an increase in the density of electronically active defects with increasing nitrogen content, both responsible for the SDR and other, standard Shockley–Read–Hall centers. The result demonstrates the importance of including nonspin‐dependent recombination channels in a complete model of SDR.
Long-term dynamics of fairness: understanding the impact of data-driven targeted help on job seekers

The use of data-driven decision support by public agencies is becoming more widespread and already influences the allocation of public resources. This raises ethical concerns, as it has adversely affected minorities and historically discriminated groups. In this paper, we use an approach that combines statistics and machine learning with dynamical modeling to assess long-term fairness effects of labor market interventions. Specifically, we develop and use a model to investigate the impact of decisions caused by a public employment authority that selectively supports job-seekers through targeted help. The selection of who receives what help is based on a data-driven intervention model that estimates an individual’s chances of finding a job in a timely manner and is based on data that describes a population in which skills relevant to the labor market are unevenly distributed between two groups (e.g., males and females). The intervention model has incomplete access to the individual’s actual skills and can augment this with knowledge of the individual’s group affiliation, thus using a protected attribute to increase predictive accuracy. We assess this intervention model’s dynamics - especially fairness-related issues and trade-offs between different fairness goals-over time and compare it to an intervention model that does not use group affiliation as a predictive feature. We conclude that in order to quantify the trade-off correctly and to assess the long-term fairness effects of such a system in the real-world, careful modeling of the surrounding labor market is indispensable.
Irida-Graphene: A New 2d Carbon Allotrope

: Several 2D carbon-based materials have been computationally designed in the last years due to the success achieved by graphene. Here, we propose a new 2D all-sp 2 carbon allotrope, named Irida-Graphene (IG), using a bottom-up approach. IG is composed of fused rings containing 3-6-8 carbon atoms. We employed density functional theory calculations and reactive (ReaxFF) molecular dynamics simulations to examine its mechanical, structural, electronic, and optical properties. Results showed that IG exhibits good dynamical and thermal stabilities. Its estimated elastic modulus varies between 80-113 GPa. IG is a metallic material and presents a Dirac cone above the Fermi level in the center of the band. The intense optical activity of IG is restricted to the infrared and violet regions. IG can act as a violet collector for photon energies of about 3.0 eV since it presents very low reﬂectivity and refractive index greater than one.
A Novel Survey for Young Substellar Objects with the W-band Filter. V. IC 348 and Barnard 5 in the Perseus Cloud

We report the discovery of substellar objects in the young star cluster IC 348 and the neighboring Barnard 5 dark cloud, both at the eastern end of the Perseus star-forming complex. The substellar candidates are selected using narrowband imaging, i.e., on and off photometric technique with a filter centered around the water absorption feature at 1.45 μm, a technique proven to be efficient in detecting water-bearing substellar objects. Our spectroscopic observations confirm three brown dwarfs in IC 348. In addition, the source WBIS 03492858+3258064, reported in this work, is the first confirmed brown dwarf discovered toward Barnard 5. Together with the young stellar population selected via near- and mid-infrared colors using the Two Micron All Sky Survey and the Wide-field Infrared Survey Explorer, we diagnose the relation between stellar versus substellar objects with the associated molecular clouds. Analyzed by Gaia EDR3 parallaxes and kinematics of the cloud members across the Perseus region, we propose the star formation scenario of the complex under influence of the nearby OB association.