Miracle-dz/UniFAR-FTUs huggingface dataset:
Benefits of the task for the delivery of negative feedback

Over 50 years of research has supported the positive relationship between feedback and performance improvement. A recent meta-analysis suggests that feedback may not be beneficial for performance, and that it may actually be harmful for performance (Kluger & DeNisi, 1996). This study suggests that these inconsistencies exist because positive and negative feedback are treated like opposite sides of the same scale. In reality, positive and negative feedback are two very different types of information and should be treated differently. Current research examines feedback delivered interpersonally. When delivering feedback this way, positive feedback is often accepted while negative feedback is rejected. The current study states that alternate delivery methods may be better for the acceptance and use of negative feedback. It is suggested that negative feedback received directly from the task itself may be more accepted, more intrinsically motivating, and result in less negative emotion for receivers than negative feedback from interpersonal sources. Two hundred and two university students participated in a simple computer simulation task. They received feedback regarding their performance and then participated in the task a second time. Results revealed no differences between conditions in acceptance, possibly a result of task. When receiving negative feedback from the task, participants experienced greater intrinsic motivation than when receiving negative feedback from interpersonal sources. Finally, negative feedback from the task resulted in less negative emotion than negative feedback from interpersonal sources. By removing the interpersonal interaction, the task removes a great deal of negative emotion associated with the supervisor. This study revealed great potential for the task as a source of negative feedback. Although the workforce may not be ready for a full task-feedback system, it may serve as a good supplement for interpersonal feedback and worthy of future research in the field
The Shellless Supernova Remnant B0532–67.5 in the Large Magellanic Cloud

The supernova remnant (SNR) B0532−67.5 in the Large Magellanic Cloud (LMC) was first diagnosed by its nonthermal radio emission, and its SNR nature was confirmed by the observation of diffuse X-ray emission; however, no optical SNR shell is detected. The OB association LH75, or NGC 2011, is projected within the boundary of this SNR. We have analyzed the massive star population in and around SNR B0532−67.5 using optical photometric data to construct color–magnitude diagrams, using stellar evolutionary tracks to estimate stellar masses, and using isochrones to assess the stellar ages. From these analyses, we find a 20–25 Myr population in LH75 and a younger population less than 10 Myr old to the southwest of LH75. The center of SNR B0532−67.5 is located closer to the core of LH75 than to the massive stars to its southwest. We conclude that the supernova progenitor was probably a member of LH75 with an initial mass of ∼15 M ⊙. The supernova exploded in an H i cavity excavated by the energy feedback of LH75. The low density of the ambient medium prohibits the formation of a visible nebular shell. Despite the low density in the ambient medium, physical properties of the hot gas within the SNR interior do not differ from SNRs with a visible shell by more than a factor of 2–3. The large-scale H i map shows that SNR B0532−67.5 is projected in a cavity that appears to be connected with the much larger cavity of the supergiant shell LMC-4.
Apparent Motion of the Circumstellar Envelope of CQ Tau in Scattered Light

The study of spiral structures in protoplanetary disks is of great importance for understanding the processes in the disks, including planet formation. Bright spiral arms were detected in the disk of young star CQ Tau by Uyama et al. in the H and L bands. The spiral arms are located inside the gap in millimeter-sized dust, discovered earlier using Atacama Large Millimeter/submillimeter Array observations. To explain the gap, Ubeira Gabellini et al. proposed the existence of a planet with the semimajor axis of 20 au. We obtained multi-epoch observations of a spiral feature in the circumstellar envelope of CQ Tau in the I c band using a novel technique of differential speckle polarimetry. The observations covering a period from 2015 to 2021 allow us to estimate the pattern speed of the spiral: −0. °2 ± 1. °1 yr−1 (68% credible interval; positive value indicates counterclockwise rotation), assuming a face-on orientation of the disk. This speed is significantly smaller than expected for a companion-induced spiral, if the perturbing body has a semimajor axis of 20 au. We emphasize that the morphology of the spiral structure is likely to be strongly affected by shadows of a misaligned inner disk detected by Eisner et al.
Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues

Robust state tracking for task-oriented dialogue systems currently remains restricted to a few popular languages.This paper shows that given a large-scale dialogue data set in one language, we can automatically produce an effective semantic parser for other languages using machine translation. We propose automatic translation of dialogue datasets with alignment to ensure faithful translation of slot values and eliminate costly human supervision used in previous benchmarks. We also propose a new contextual semantic parsing model, which encodes the formal slots and values, and only the last agent and user utterances. We show that the succinct representation reduces the compounding effect of translation errors, without harming the accuracy in practice.We evaluate our approach on several dialogue state tracking benchmarks. On RiSAWOZ, CrossWOZ, CrossWOZ-EN, and MultiWOZ-ZH datasets we improve the state of the art by 11%, 17%, 20%, and 0.3% in joint goal accuracy. We present a comprehensive error analysis for all three datasets showing erroneous annotations can lead to misguided judgments on the quality of the model. Finally, we present RiSAWOZ English and German datasets, created using our translation methodology. On these datasets, accuracy is within 11% of the original showing that high-accuracy multilingual dialogue datasets are possible without relying on expensive human annotations. We release our datasets and software open source.
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso

While Weighted Lasso sparse regression has appealing statistical guarantees that would entail a major real-world impact in nance, genomics, and brain imaging applications, it is typically scarcely adopted due to its complex high-dimensional space composed by thousands of hyperparameters. On the other hand, the latest progress with high-dimensional hyperparameter optimization (HD-HPO) methods for black-box functions demonstrates that high-dimensional applications can indeed be eciently optimized. Despite this initial success, HD-HPO approaches are mostly applied to synthetic problems with a moderate number of dimensions, which limits its impact in scientic and engineering applications. We propose LassoBench , the rst benchmark suite tailored for Weighted Lasso regression. LassoBench consists of benchmarks for both well-controlled synthetic setups (number of samples, noise level, ambient and eective dimensionalities, and multiple delities) and real-world datasets, which enables the use of many avors of HPO algorithms to be studied and extended to the high-dimensional Lasso setting. We evaluate 6 state-of-the-art HPO methods and 3 Lasso baselines, and demonstrate that Bayesian optimization and evolutionary strategies can improve over the methods commonly used for sparse regression while highlighting limitations of these frameworks in very high-dimensional and noisy settings.
Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks

In this paper, we investigate the performance of a practical aggregated LiFi-WiFi system with the discrete constellation inputs from a practical view. We derive the achievable rate expressions of the aggregated LiFi-WiFi system for the first time. Then, we study the rate maximization problem via optimizing the constellation distribution and power allocation jointly. Specifically, a multilevel mercy-filling power allocation scheme is proposed by exploiting the relationship between the mutual information and minimum mean-squared error (MMSE) of discrete inputs. Meanwhile, an inexact gradient descent method is proposed for obtaining the optimal probability distributions. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes the lower bound of the achievable rate where the optimal power allocation can be obtained in closed forms and the constellation distributions problem can be solved efficiently by Frank-Wolfe method. Extensive numerical results show that the optimized strategies are able to provide significant gains over the state-of-the-art schemes in terms of the achievable rate.
$pp$ Solar Neutrinos at DARWIN

The DARWIN collaboration recently argued that DARWIN (DARk matter WImp search with liquid xenoN) can collect, via neutrino–electron scattering, a large, useful sample of solar pp -neutrinos, and measure their survival probability with sub-percent precision. We explore the physics potential of such a sample in more detail. We estimate that, with 300 ton-years of data, DARWIN can also measure, with the help of current solar neutrino data, the value of sin 2 θ 13 , with the potential to exclude sin 2 θ 13 = 0 close to the three-sigma level. We explore in some detail how well DARWIN can constrain the existence of a new neutrino mass-eigenstate ν 4 that is quasi-mass-degenerate with ν 1 and ﬁnd that DARWIN’s sensitivity supersedes that of all current and near-future searches for new, very light neutrinos. In particular, DARWIN can test the hypothesis that ν 1 is a pseudo-Dirac fermion as long as the induced mass-squared diﬀerence is larger than 10 − 13 eV 2 , one order of magnitude more sensitive than existing constraints. Throughout, we allowed for the hypotheses that DARWIN is ﬁlled with natural xenon or 136 Xe-depleted xenon.
Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning

Dexterous manipulation of arbitrary objects, a fundamental daily task for humans, has been a grand challenge for autonomous robotic systems. Although data-driven approaches using reinforcement learning can develop specialist policies that discover behaviors to control a single object, they often exhibit poor generalization to unseen ones. In this work, we show that policies learned by existing reinforcement learning algorithms can in fact be generalist when combined with multi-task learning and a well-chosen object representation. We show that a single generalist policy can perform in-hand manipulation of over 100 geometrically-diverse real-world objects and generalize to new objects with unseen shape or size. Interestingly, we find that multi-task learning with object point cloud representations not only generalizes better but even outperforms the single-object specialist policies on both training as well as held-out test objects. Video results at https://huangwl18.github. io/geometry-dex
idSTLPy: A Python Toolbox for Active Perception and Control

This paper describes a Python toolbox for active perception and control synthesis of probabilistic signal temporal logic (PrSTL) formulas of switched linear systems with additive Gaussian disturbances and measurement noises. We implement a counterexample-guided synthesis strategy that combines Bounded Model Checking, linear programming, and sampling-based motion planning techniques. We illustrate our approach and the toolbox throughout the paper with a motion planning example for a vehicle with noisy localization. The code is available at \url{https://codeocean.com/capsule/0013534/tree}.
Speech recognition for air traffic control via feature learning and end-to-end training

In this work, we propose a new automatic speech recognition (ASR) system based on feature learning and an end-to-end training procedure for air traffic control (ATC) systems. The proposed model integrates the feature learning block, recurrent neural network (RNN), and connectionist temporal classification loss to build an end-to-end ASR model. Facing the complex environments of ATC speech, instead of the handcrafted features, a learning block is designed to extract informative features from raw waveforms for acoustic modeling. Both the SincNet and 1D convolution blocks are applied to process the raw waveforms, whose outputs are concatenated to the RNN layers for the temporal modeling. Thanks to the ability to learn representations from raw waveforms, the proposed model can be optimized in a complete end-to-end manner, i.e., from waveform to text. Finally, the multilingual issue in the ATC domain is also considered to achieve the ASR task by constructing a combined vocabulary of Chinese characters and English letters. The proposed approach is validated on a multilingual real-world corpus (ATCSpeech), and the experimental results demonstrate that the proposed approach outperforms other baselines, achieving a 6.9\% character error rate.
Finite-Time Consensus Learning for Decentralized Optimization with Nonlinear Gossiping

Distributed learning has become an integral tool for scaling up machine learning and addressing the growing need for data privacy. Although more robust to the network topology, decentralized learning schemes have not gained the same level of popularity as their centralized counterparts for being less competitive performance-wise. In this work, we attribute this issue to the lack of synchronization among decentralized learning workers, showing both empirically and theoretically that the convergence rate is tied to the synchronization level among the workers. Such motivated, we present a novel decentralized learning framework based on nonlinear gossiping (NGO), that enjoys an appealing finite-time consensus property to achieve better synchronization. We provide a careful analysis of its convergence and discuss its merits for modern distributed optimization applications, such as deep neural networks. Our analysis on how communication delay and randomized chats affect learning further enables the derivation of practical variants that accommodate asynchronous and randomized communications. To validate the effectiveness of our proposal, we benchmark NGO against competing solutions through an extensive set of tests, with encouraging results reported.
Identifying nonlinear dynamical systems from multi-modal time series data

modality-speciﬁc streams, followed by two additional hidden layers of size d h . All layers were fully connected with ReLU activation, except for the output layer. As the graphs indicate, additional observations from other modalities again help to reconstruct the attractor geometry if the Gaussian observations are very noisy. We also observed, however, that it is difﬁcult to reconstruct the Lorenz system from (by deﬁnition non-negative) gamma or ZIP observations alone. At least for ZIP-distributed data this is easy to explain (see example time series in Fig. 6B), as these - by model deﬁnition - low and sparse counts provide only very little information about the Lorenz attractor’s complex geometry.
Host galaxy properties of X-ray active galactic nuclei in the local Universe

We study the host galaxy properties of active galactic nuclei (AGN) that have been detected in X-rays in the nearby Universe ( z < 0 . 2). For that purpose, we use the catalogue provided by the ROSAT-2RXS in the 0.1–2.4keV energy band, one of the largest X-ray datasets with spectroscopic observations. Our sample consists of ∼ 900 X-ray AGN. The catalogue provides classiﬁcation of the sources into type 1 and 2 based on optical spectra. Approximately 25% of the AGN are type 2. We use the available optical, near-IR, and mid-IR photometry to construct spectral energy distributions (SEDs). We measure the stellar mass ( M ∗ ) and star formation rate (SFR) of the AGN by ﬁtting these SEDs with the X-CIGALE code. We compare the M ∗ and SFR of the two AGN populations, taking their di ﬀ erent redshift and luminosity distributions into account. Based on our results, type 2 AGN tend to live in more massive galaxies compared to their type 1 counterparts (log[ M ∗ ( M (cid:12) )] = 10 . 49 + 0 . 16 − 0 . 10 vs. 10 . 23 + 0 . 05 − 0 . 08 ), in agreement with previous studies at higher redshifts. In terms of SFRs, our analysis shows that, in the nearby Universe, the number of X-ray AGN that live in quiescent systems is higher compared to at higher redshifts, in accordance with previous studies in the local Universe. However, the majority of AGN ( ∼ 75%) live inside or above the main sequence.
A Tutorial of Cyber-Syndrome viewed from Cyber-Physical-Social-Thinking Space and Maslow's Hierarchy of Needs

Cyber-Physical-Social-Thinking Space and Maslow’s Hierarchy of Needs Feifei Shi, Huansheng Ning, Senior Member, IEEE, Sahraoui Dhelim Abstract With the increase of active Internet users, various physical, social, and mental disorders have recently emerged because of the excessive use of technology. Cyber-Syndrome is known as the condition that appears due to the excessive interaction with the cyberspace, and it affects the users' physical, social, and mental states. In this paper, we discuss the etiology and symptoms of Cyber-Syndrome according to theories of Cyber-Physical-Social-Thinking (CPST) space and Maslow’s Hierarchy of Needs. In addition, we also propose an entropy-based mechanism for recovery of Cyber-Syndrome, to provide potential guidance for clinical detection and diagnosis. Cyber-Syndrome has attracted much attention these days, and more indepth exploration is needed in the future.
Auto Tuning of Hadoop and Spark parameters

Data of the order of terabytes, petabytes, or beyond is known as Big Data. This data cannot be processed using the traditional database software, and hence there comes the need for Big Data Platforms. By combining the capabilities and features of various big data applications and utilities, Big Data Platforms form a single solution. It is a platform that helps to develop, deploy and manage the big data environment. Hadoop and Spark are the two open-source Big Data Platforms provided by Apache. Both these platforms have many configurational parameters, which can have unforeseen effects on the execution time, accuracy, etc. Manual tuning of these parameters can be tiresome, and hence automatic ways should be needed to tune them. After studying and analyzing various previous works in automating the tuning of these parameters, this paper proposes two algorithms - Grid Search with Finer Tuning and Controlled Random Search. The performance indicator studied in this paper is Execution Time. These algorithms help to tune the parameters automatically. Experimental results have shown a reduction in execution time of about 70% and 50% for Hadoop and 81.19% and 77.77% for Spark by Grid Search with Finer Tuning and Controlled Random Search, respectively.
A deep ensemble approach to X-ray polarimetry

X-ray polarimetry will soon open a new window on the high energy universe with the launch of NASA's Imaging X-ray Polarimetry Explorer (IXPE). Polarimeters are currently limited by their track reconstruction algorithms, which typically use linear estimators and do not consider individual event quality. We present a modern deep learning method for maximizing the sensitivity of X-ray telescopic observations with imaging polarimeters, with a focus on the gas pixel detectors (GPDs) to be flown on IXPE. We use a weighted maximum likelihood combination of predictions from a deep ensemble of ResNets, trained on Monte Carlo event simulations. We derive and apply the optimal event weighting for maximizing the polarization signal-to-noise ratio (SNR) in track reconstruction algorithms. For typical power-law source spectra, our method improves on the current state of the art, providing a ~40% decrease in required exposure times for a given SNR.
Computational Analysis and Modeling Techniques for Monolithic Membrane Reactors Related to CO2 Free Power Processes

Funded by European Community and Swiss government research project AZEP (Advanced Zero Emission Power Plant), the project was carried out at the Department of Energy Sciences at Lund Faculty of Engineering (LTH). The project addressed the development of a specific, zero emissions, gas turbine-based, power generation process to reduce local and global emissions in a cost-effective way. In this project a unique monolith heat and mass exchanger reactor with an oxygen permeable membrane was proposed by the Norsk Hydro Oil and Energy Research Center in Norway. The aim of this thesis is to describe a complete design of a monolithic reactor where both heat and oxygen transport takes place. This led to the development of a mathematical model of an oxygen membrane reactor which fulfils the boundary conditions set by the AZEP process. Further, an investigation of the total pressure drop and pressure distribution of the manifolding system using the Computational Fluid Dynamics (CFD) tool FLUENT was carried out. A further goal was also to perform dynamic analysis of the AZEP reactor. In order to perform such analysis, calculation tools were needed to describe the performance of CO2 capture. The object-oriented programming languages Modelica and Dymola were used to describe dynamic behavior of the oxygen transfer reactor. Sensitivity studies performed with the membrane model showed that a high inlet sweep temperature would raise the magnitude of the oxygen permeation flux through the MCMmembrane. At the same time high inlet air temperatures would be necessary to keep the driving potential through the length of the membrane at a higher level which would be important for achieving the targeted industrial values of oxygen fluxes. The dynamic simulations showed that disturbances on sweep flow had larger impact on the membranes performance than disturbances introduced on the air side of the reactor. By introduction of a new solution of flow manifolds (linear channel arrangement) a less complex header system than the one in the AZEP solution was achieved. Since the implementation of detailed membrane model in heat and mass balance calculations for system studies would result in excessive calculation time, results from this study were utilized for the generation of correlations describing the oxygen transfer as a function of operating parameters such as temperature and partial pressure. This modeling approach was expected to improve the accuracy of the system studies. (Less)
Convex Chance-Constrained Programs with Wasserstein Ambiguity

Chance constraints yield non-convex feasible regions in general. In particular, when the uncertain parameters are modeled by a Wasserstein ball, arXiv:1806.07418 and arXiv:1809.00210 showed that the distributionally robust (pessimistic) chance constraint admits a mixed-integer conic representation. This paper identifies sufficient conditions that lead to convex feasible regions of chance constraints with Wasserstein ambiguity. First, when uncertainty arises from the left-hand side of a pessimistic individual chance constraint, we derive a convex and conic representation if the Wasserstein ball is centered around a Gaussian distribution. Second, when uncertainty arises from the right-hand side of a pessimistic joint chance constraint, we show that the ensuing feasible region is convex if the Wasserstein ball is centered around a log-concave distribution (or, more generally, an $\alpha$-concave distribution with $\alpha \geq -1$). In addition, we propose a block coordinate ascent algorithm for this class of chance constraints and prove its convergence to global optimum. Furthermore, we extend the convexity results and conic representation to optimistic chance constraints.