Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid Support for Older Adults during the Initial Stages of the COVID-19 Pandemic: Results from a Geriatric Psychiatry Helpline

Background: The COVID-19 pandemic and governmental lockdown measures disproportionally impacts older adults. This study presents the results from a psychiatric helpline for older adults in Mannheim, Germany, during the lockdown, set up to provide information and psychosocial support measures. We aim to elucidate the needs of older adults, their reported changes and the psychological impact during the initial stages of the health crisis. Methods: 55 older adults called the psychiatric helpline between April and June 2020. Information on demographics, previous diagnosis of psychiatric and somatic diseases as well as changes in daily life due to the pandemic was collected anonymously. Current mental health status was assessed using the depression HAMD-7 and the anxiety HAM-A scales. Results: Most callers were women, older adults (M = 74.69 years), single and retired. 69% of callers reported new or an increase in psychiatric symptoms, with anxiety and depressive symptoms being the most common ones. Age was significantly negatively correlated to higher levels of anxiety and depression symptoms. Individuals with a previous diagnosis of a psychiatric disease reported significantly higher levels of depressive and anxiety symptoms than those without a diagnosis. Conclusion: In older adults, the perceived psychological impact of the COVID-19 crisis appears to ameliorate with age. Individuals with a history of psychiatric disease are most vulnerable to negative mental health outcomes. Rapid response in the form of a geriatric helpline are useful initiatives to support the needs and the psychological well-being of older adults during a health crisis.
Single-nucleotide polymorphism data describe contemporary population structure and diversity in allochronic lineages of pink salmon (Oncorhynchus gorbuscha)

Pink salmon, the most abundant Pacific salmon, have an obligate 2-year life cycle that leads to reproductively isolated even- and odd-year lineages. Using new and existing data, we examined the genetic structure of both lineages across their distributional range by genotyping 16 681 single-nucleotide polymorphisms for 383 individuals originating from seven pairs of even- and odd-year populations. Distinct differences in standing pools of genetic variation were identified between the lineages; we observed higher levels of heterozygosity, allelic richness, and significantly more private alleles in the odd-year lineage. However, the patterns of population structure were concordant between lineages: the Asian and northern Alaska populations displayed little differentiation but differed significantly from populations in southcentral Alaska and the Pacific Northwest. Our population structure results, in the context of known paleoecological information, suggest that both lineages occupied a northern Beringial refugium as well as a Cascadian refugium in North America during the Last Glacial Maximum. These results highlight the influence of historical patterns of habitat availability on contemporary population structure and support the hypothesis of a pre-glacial origin of the lineages.
The State of Industrial Robotics: Emerging Technologies, Challenges, and Key Research Directions

Robotics and related technologies are central to the ongoing digitization and advancement of manufacturing. In recent years, a variety of strategic initiatives around the world including "Industry 4.0", introduced in Germany in 2011 have aimed to improve and connect manufacturing technologies in order to optimize production processes. In this work, we study the changing technological landscape of robotics and "internet-of-things" (IoT)-based connective technologies over the last 7-10 years in the wake of Industry 4.0. We interviewed key players within the European robotics ecosystem, including robotics manufacturers and integrators, original equipment manufacturers (OEMs), and applied industrial research institutions and synthesize our findings in this paper. We first detail the state-of-the-art robotics and IoT technologies we observed and that the companies discussed during our interviews. We then describe the processes the companies follow when deciding whether and how to integrate new technologies, the challenges they face when integrating these technologies, and some immediate future technological avenues they are exploring in robotics and IoT. Finally, based on our findings, we highlight key research directions for the robotics community that can enable improved capabilities in the context of manufacturing.
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection

Most existing approaches to disfluency detection heavily rely on human-annotated corpora, which is expensive to obtain in practice. There have been several proposals to alleviate this issue with, for instance, self-supervised learning techniques, but they still require human-annotated corpora. In this work, we explore the unsupervised learning paradigm which can potentially work with unlabeled text corpora that are cheaper and easier to obtain. Our model builds upon the recent work on Noisy Student Training, a semi-supervised learning approach that extends the idea of self-training. Experimental results on the commonly used English Switchboard test set show that our approach achieves competitive performance compared to the previous state-of-the-art supervised systems using contextualized word embeddings (e.g. BERT and ELECTRA).
Fusion Models for Improved Visual Captioning

Visual captioning aims to generate textual descriptions given images. Traditionally, the captioning models are trained on human annotated datasets such as Flickr30k and MS-COCO, which are limited in size and diversity. This limitation hinders the generalization capabilities of these models while also rendering them to often make mistakes. Language models can, however, be trained on vast amounts of freely available unlabelled data and have recently emerged as successful language encoders and coherent text generators. Meanwhile, several unimodal and multimodal fusion techniques have been proven to work well for natural language generation and automatic speech recognition. Building on these recent developments, and with an aim of improving the quality of generated captions, the contribution of our work in this paper is two-fold: First, we propose a generic multimodal model fusion framework for caption generation as well as emendation where we utilize different fusion strategies to integrate a pretrained Auxiliary Language Model (AuxLM) within the traditional encoder-decoder visual captioning frameworks. Next, we employ the same fusion strategies to integrate a pretrained Masked Language Model (MLM), namely BERT, with a visual captioning model, viz. Show, Attend, and Tell, for emending both syntactic and semantic errors in captions. Our caption emendation experiments on three benchmark image captioning datasets, viz. Flickr8k, Flickr30k, and MSCOCO, show improvements over the baseline, indicating the usefulness of our proposed multimodal fusion strategies. Further, we perform a preliminary qualitative analysis on the emended captions and identify error categories based on the type of corrections.
Independence Tests Without Ground Truth for Noisy Learners

Exact ground truth invariant polynomial systems can be written for arbitrarily correlated binary classifiers. Their solutions give estimates for sample statistics that require knowledge of the ground truth of the correct labels in the sample. Of these polynomial systems, only a few have been solved in closed form. Here we discuss the exact solution for independent binary classifiers - resolving an outstanding problem that has been presented at this conference and others. Its practical applicability is hampered by its sole remaining assumption - the classifiers need to be independent in their sample errors. We discuss how to use the closed form solution to create a self-consistent test that can validate the independence assumption itself absent the correct labels ground truth. It can be cast as an algebraic geometry conjecture for binary classifiers that remains unsolved. A similar conjecture for the ground truth invariant algebraic system for scalar regressors is solvable, and we present the solution here. We also discuss experiments on the Penn ML Benchmark classification tasks that provide further evidence that the conjecture may be true for the polynomial system of binary classifiers.
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System

The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. However, the normal training paradigm, i.e., fitting a recommender model to recover the user behavior data with pointwise or pairwise loss, makes the model biased towards popular items. This results in the terrible Matthew effect, making popular items be more frequently recommended and become even more popular. Existing work addresses this issue with Inverse Propensity Weighting (IPW), which decreases the impact of popular items on the training and increases the impact of long-tail items. Although theoretically sound, IPW methods are highly sensitive to the weighting strategy, which is notoriously difficult to tune. In this work, we explore the popularity bias issue from a novel and fundamental perspective --- cause-effect. We identify that popularity bias lies in the direct effect from the item node to the ranking score, such that an item's intrinsic property is the cause of mistakenly assigning it a higher ranking score. To eliminate popularity bias, it is essential to answer the counterfactual question that what the ranking score would be if the model only uses item property. To this end, we formulate a causal graph to describe the important cause-effect relations in the recommendation process. During training, we perform multi-task learning to achieve the contribution of each cause; during testing, we perform counterfactual inference to remove the effect of item popularity. Remarkably, our solution amends the learning process of recommendation which is agnostic to a wide range of models --- it can be easily implemented in existing methods. We demonstrate it on Matrix Factorization (MF) and LightGCN [20], which are representative of the conventional and SOTA model for collaborative filtering. Experiments on five real-world datasets demonstrate the effectiveness of our method.
Classification Accuracy and Parameter Estimation in Multilevel Contexts: A Study of Conditional Nonparametric Multilevel Latent Class Analysis

The current research has two aims. First, to demonstrate the utility conditional nonparametric multilevel latent class analysis (NP-MLCA) for multi-site program evaluation using an empirical dataset. Second, to investigate how classification accuracy and parameter estimation of a conditional NP-MLCA are affected by six study factors: the quality of latent class indicators, the number of latent class indicators, level-1 covariate effects, cross-level covariate effects, the number of level-2 units, and the size of level-2 units. A total of 96 conditions was examined using a simulation study. The resulting classification accuracy rates, the power and type-I error of cross-level covariate effects and contextual effects suggest that the nonparametric multilevel latent class model can be applied broadly in multilevel contexts.
The Performance Analysis of Generalized Margin Maximizer (GMM) on Separable Data

Logistic models are commonly used for binary classification tasks. The success of such models has often been attributed to their connection to maximum-likelihood estimators. It has been shown that gradient descent algorithm, when applied on the logistic loss, converges to the max-margin classifier (a.k.a. hard-margin SVM). The performance of the max-margin classifier has been recently analyzed. Inspired by these results, in this paper, we present and study a more general setting, where the underlying parameters of the logistic model possess certain structures (sparse, block-sparse, low-rank, etc.) and introduce a more general framework (which is referred to as "Generalized Margin Maximizer", GMM). While classical max-margin classifiers minimize the 2-norm of the parameter vector subject to linearly separating the data, GMM minimizes any arbitrary convex function of the parameter vector. We provide a precise analysis of the performance of GMM via the solution of a system of nonlinear equations. We also provide a detailed study for three special cases: (1) l₂-GMM that is the max-margin classifier, (2) l₁-GMM which encourages sparsity, and (3) l_∞-GMM which is often used when the parameter vector has binary entries. Our theoretical results are validated by extensive simulation results across a range of parameter values, problem instances, and model structures.
Learning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment

Edge intelligence is an emerging network architecture that integrates sensing, communication, computing components, and supports various machine learning applications, where a fundamental communication question is: how to allocate the limited wireless resources (such as time, energy) to the simultaneous model training of heterogeneous learning tasks? Existing methods ignore two important facts: 1) different models have heterogeneous demands on training data; 2) there is a mismatch between the simulated environment and the real-world environment. As a result, they could lead to low learning performance in practice. This paper proposes the learning centric wireless resource allocation (LCWRA) scheme that maximizes the worst learning performance of multiple tasks. Analysis shows that the optimal transmission time has an inverse power relationship with respect to the generalization error. Finally, both simulation and experimental results are provided to verify the performance of the proposed LCWRA scheme and its robustness in real implementation.
Semi-Grant-Free NOMA: Ergodic Rates Analysis With Random Deployed Users

Semi-grant-free (Semi-GF) non-orthogonal multiple access (NOMA) enables grant-free (GF) and grant-based (GB) users to share the same resource blocks, thereby balancing the connectivity and stability of communications. This letter analyzes ergodic rates of Semi-GF NOMA systems. First, this letter exploits a Semi-GF protocol, denoted as dynamic protocol, for selecting GF users into the occupied GB channels via the GB user’s instantaneous received power. Under this protocol, the closed-form analytical and approximated expressions for ergodic rates are derived. The numerical results illustrate that the GF user (weak NOMA user) has a performance upper limit, while the ergodic rate of the GB user (strong NOMA user) increases linearly versus the transmit signal-to-noise ratio.
Null controllability and finite-time stabilization in minimal time of one-dimensional first-order 2 × 2 linear hyperbolic systems

The goal of this article is to present the minimal time needed for the null controllability and finite-time stabilization of one-dimensional first-order 2 × 2 linear hyperbolic systems. The main technical point is to show that we cannot obtain a better time. The proof combines the backstepping method with the Titchmarsh convolution theorem.
A Local Search Framework for Experimental Design

We present a local search framework to design and analyze both combinatorial algorithms and rounding algorithms for experimental design problems. This framework provides a unifying approach to match and improve all known results in D/A/E-design and to obtain new results in previously unknown settings. For combinatorial algorithms, we provide a new analysis of the classical Fedorov's exchange method. We prove that this simple local search algorithm works well as long as there exists an almost optimal solution with good condition number. Moreover, we design a new combinatorial local search algorithm for E-design using the regret minimization framework. For rounding algorithms, we provide a unified randomized exchange algorithm to match and improve previous results for D/A/E-design. Furthermore, the algorithm works in the more general setting to approximately satisfy multiple knapsack constraints, which can be used for weighted experimental design and for incorporating fairness constraints into experimental design.
Convergence of Constrained Anderson Acceleration

We prove non asymptotic linear convergence rates for the constrained Anderson acceleration extrapolation scheme. These guarantees come from new upper bounds on the constrained Chebyshev problem, which consists in minimizing the maximum absolute value of a polynomial on a bounded real interval with $l_1$ constraints on its coefficients vector. Constrained Anderson Acceleration has a numerical cost comparable to that of the original scheme.
Distribution of moments of trace of Frobenius in arithmetic progressions and holomorphic projection

In this paper, we study moments of the trace of Frobenius of elliptic curves if the trace is restricted to a fixed arithmetic progression. In particular, we fix the arithmetic progression and consider the ratio of the $2k$-th moment to the zeroeth moment as one varies the size of the finite field $\F_{p^r}$. We obtain asymptotic formulas for these ratios for cases for which the prime $p$ goes to infinity for fixed $r$ and cases where the power $r$ goes to infinity with fixed $p$. These results follow from similar asymptotic formulas relating sums and moments of Hurwitz class numbers where the sums are restricted to certain arithmetic progressions which we investigate in this paper.
Excitons in bulk black phosphorus evidenced by photoluminescence at low temperature

Atomic layers of black phosphorus (BP) present unique opto-electronic properties dominated by a direct tunable bandgap in a wide spectral range from visible to mid-infrared (IR). In this work, we investigate the IR photoluminescence (PL) of BP single crystals at very low temperature. Near-band-edge recombinations are observed at 2 K, including dominant excitonic transitions at 0.276 eV and a weaker one at 0.278 eV. The free-exciton binding energy is calculated with an anisotropic Wannier–Mott model and found equal to 9.1 meV. On the contrary, the PL intensity quenching of the 0.276 eV peak at high temperature is found with a much smaller activation energy, attributed to the localization of free excitons on a shallow impurity. This analysis leads us to attribute respectively the 0.276 eV and 0.278 eV PL lines to bound excitons and free excitons in BP. As a result, the value of bulk BP bandgap is refined to 0.287 eV at 2 K.
Named Entity Recognition for Social Media Texts with Semantic Augmentation

Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts, especially user-generated social media content. Semantic augmentation is a potential way to alleviate this problem. Given that rich semantic information is implicitly preserved in pre-trained word embeddings, they are potential ideal resources for semantic augmentation. In this paper, we propose a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account. In particular, we obtain the augmented semantic information from a large-scale corpus, and propose an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively. Extensive experiments are performed on three benchmark datasets collected from English and Chinese social media platforms, where the results demonstrate the superiority of our approach to previous studies across all three datasets.
GANs & Reels: Creating Irish Music using a Generative Adversarial Network

In this paper we present a method for algorithmic melody generation using a generative adversarial network without recurrent components. Music generation has been successfully done using recurrent neural networks, where the model learns sequence information that can help create authentic sounding melodies. Here, we use DC-GAN architecture with dilated convolutions and towers to capture sequential information as spatial image information, and learn long-range dependencies in fixed-length melody forms such as Irish traditional reel.
Exploring the hot gaseous halo around an extremely massive and relativistic jet launching spiral galaxy with XMM−Newton

We present a deep XMM-Newton observation of the extremely massive, rapidly rotating, relativistic-jet-launching spiral galaxy 2MASX J23453268-0449256. Diffuse X-ray emission from the hot gaseous halo around the galaxy is robustly detected out to a radius of 160 kpc, corresponding roughly to 35 per cent of the virial radius ($\approx 450$ kpc). We fit the X-ray emission with the standard isothermal $\beta$ model, and it is found that the enclosed gas mass within 160 kpc is $1. 15_{-0.24}^{+0.22} \times 10^{11} \, \rm{M}_{\odot}$. Extrapolating the gas mass profile out to the virial radius, the estimated gas mass is $8. 25_{-1.77}^{+1.62} \times 10^{11} \, \rm{M}_{\odot}$, which makes up roughly 65 per cent of the total baryon mass content of the galaxy. When the stellar mass is considered and accounting for the statistical and systematic uncertainties, the baryon mass fraction within the virial radius is $0.121_{-0.043}^{+0.043}$, in agreement with the universal baryon fraction. The baryon mass fraction is consistent with all baryons falling within $r_{200}$, or with only half of the baryons falling within $r_{200}$. Similar to the massive spiral galaxies NGC 1961 and NGC 6753, we find a low value for the metal abundance of $\approx 0.1 {\rm{Z}}_{\odot}$, which appears uniform with radius. We also detect diffuse X-ray emission associated with the northern and southern lobes, possibly attributed to inverse Compton scattering of cosmic microwave background photons. The estimated energy densities of the electrons and magnetic field in these radio lobes suggest that they are electron-dominated by a factor of 10$-$200, depending on the choice of the lower cut-off energy of the electron spectrum.
Survivor Bias: Divergent Fates of the Solar System’s Ejected versus Persisting Planetesimals

The orbital architecture of the solar system is thought to have been sculpted by a dynamical instability among the giant planets. During the instability a primordial outer disk of planetesimals was destabilized and ended up on planet-crossing orbits. Most planetesimals were ejected into interstellar space, but a fraction were trapped on stable orbits in the Kuiper Belt and Oort cloud. We use a suite of N-body simulations to map out the diversity of planetesimals’ dynamical pathways. We focus on two processes: tidal disruption from very close encounters with a giant planet, and loss of surface volatiles from repeated passages close to the Sun. We show that the rate of tidal disruption is more than a factor of 2 higher for ejected planetesimals than for surviving objects in the Kuiper Belt or Oort cloud. Ejected planetesimals are preferentially disrupted by Jupiter and surviving ones by Neptune. Given that the gas giants contracted significantly as they cooled but the ice giants did not, taking into account the thermal evolution of the giant planets decreases the disruption rate of ejected planetesimals. The frequency of volatile loss and extinction is far higher for ejected planetesimals than for surviving ones and is not affected by the giant planets’ contraction. Even if all interstellar objects were ejected from solar system–like systems, our analysis suggests that their physical properties should be more diverse than those of solar system small bodies as a result of their divergent dynamical histories. This is consistent with the characteristics of the two currently known interstellar objects.