Miracle-dz/UniFAR-FTUs huggingface dataset:
A Distributed Pareto-based Path Planning Algorithm for Autonomous Unmanned Aerial Vehicles (Extended Abstract) ∗

Autonomous Unmanned Aerial Vehicles (UAVs) are in increasing demand thanks to their applicability in a wide range of domains. However, to fully exploit such potential, UAVs should be capable of intelligently planning their collision-free paths as that impacts greatly the execution quality of their applications. While being a problem well-addressed in literature, most presented solutions are either computationally complex centralised approaches or ones not suitable for the multiobjective requirements of most UAV use-cases. This extended abstract introduces ongoing research on a novel distributed Pareto path planning algorithm incorporating a dynamic multi-criteria decision matrix allowing each UAV to plan its collision-free path relying on local knowledge gained via digital stigmergy. The article presents some initial simulations results of a distributed UAV Trafﬁc Management system (UTM) on a weighted multilayer network.
Discussing Feminist Thinking as an Exemplar for Ethical Reflection in the Tech Classroom

In this position paper, we set out to examine the role of ethics in HCI teaching as a source of reflection for students to draw on. We describe examples inspired by experiences from our own teaching where better scaffolding of ethical reflection would be beneficial. Using feminist HCI principles, we exemplify the value of providing students with an ethical framework as a tool for reflection. We further outline three challenges we see with regards to including ethical thinking in teaching for tech education.
A high prevalence of bla

Received 07 September 2020; Accepted 20 December 2020; Published 08 January 2021 Author affiliations: The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK; Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK; Wessex Water, Bath BA2 7WW, UK; Department of Chemistry, University of Bath, Bath BA2 7AY, UK; University of Bristol, School of Cellular and Molecular Medicine, University Walk, Bristol BS8 1TD, UK; Department of R&D, Royal United Hospitals Bath, NHS Foundation Trust, Bath BA1 3NG, UK; Department of Biostatistics, University of Oslo, N-0317, Oslo, Norway; Helsinki Institute for Information Technology, Department of Mathematics and Statistics, University of Helsinki, FIN-00014 Helsinki, Finland. *Correspondence: Edward J. Feil, e. feil@ bath. ac. uk
Relationship between Self-Esteem and Career Choice Behavior among Secondary School Students in Migori Sub-County , Kenya Gor

Previous studies indicate that career choice has become a problem to secondary school students all over the world, Kenya included. Migori sub-county in Kenya is particularly disadvantaged in terms of career development in comparison with neighboring subcounties such as Kisii. The purpose of this study was to establish the relationship between self-esteem and career choice behavior among secondary school students in Migori sub-county. Descriptive Survey Design and Correlation Design were used. The Rosenberg Self-Esteem Scale and Career Decision Scale were used to collect data from students. Test-retest reliability was estimated by Pearson’s r which yielded . 82 for Rosenberg Self-Esteem Scale and . 80 for Career Decision Scale. Quantitative data was analyzed using descriptive statistics that included frequencies, percentages, means and standard deviations. Structural Equation Modeling was used to make statistical inference using model fit and regression estimates. The study revealed that the level of self-esteem and career choice behavior was high. However, career choice behavior for males was generally different from that of females. The relationship between self-esteem and career choice behavior was statistically significant (p<.05). It was concluded that self-esteem does influence career choice behavior. The findings are significant to students, career counselors and policy makers to improve students’ career choice by raising their self-
J un 2 02 1 EVERETTIAN MECHANICS WITH HYPERFINITELY MANY WORLDS

The present paper shows how onemight model Everettian quantum mechanics using hyperfinitely many worlds. A hyperfinite model allows one to consider idealized measurements of observables with continuous-valued spectra where different outcomes are associated with possibly infinitesimal probabilities. One can also prove hyperfinite formulations of Everett’s limiting relative-frequency and randomness properties, theorems he considered central to his formulation of quantummechanics. This approach also provides a more general framework in which to consider no-collapse formulations of quantum mechanics more generally. 1. Everettian quantum mechanics and many worlds Hugh Everett III (1956) (1957) presented pure wave mechanics as a solution to the quantum measurement problem encountered by the standard collapse theory. He characterized pure wave mechanics as the standard von NeumannDirac formulation of quantummechanics butwithout any collapse of the quantummechanical state on measurement or any other time. All physical systems always evolve in a deterministic, linear way described by the standard quantum dynamics. On Everett’s interpretation of the quantum-mechanical state, the linear dynamics describes the universe as constantly splitting into branches or worlds corresponding to different measurement outcomes. How many worlds there are and the state of each world depends on how one understands the global state. Everett’s description of the principle of the relativity of states allows for arbitrary decompositions of the state, and he explicitly allowed for decompositions in terms of eigenstates of observables with continuous-valued spectra. His discussion of relative states in the context of von Neumann’s account of a position measurement, for example, involves a superposition of a continuous number of branches, in each ofwhich the object systemhas a different determinate position Goldbring was partially supported by NSF grant DMS-2054477. Barrett (2019, 42–53 and 105–17) for a detailed description of the standard collapse formulation of quantum mechanics and the measurement problem and Barrett (2018) for an overview of Everett’s project.
Extending Amartya Sen’s Paretian liberal paradox to a firm’s hierarchy

Amartya Sen’s impossibility of a Paretian liberal underscores the trade-off between two widely applied social principles—liberalism and Pareto-optimality: “If someone does have certain liberal values, then he [or she] may have to eschew his [or her] adherence to Pareto optimality” (Sen 1970a:157). This paper extends the Paretian liberal paradox to the firm, understood as a socio-economic institution with a hierarchical nature and aimed at enhancing the efficiency of transactions in a world where the costs of market transactions are positive and relevant (Coase 1937). The paper shows that, even if the firm’s hierarchical nature ensures Pareto optimality, it may conflict with liberal values.
Cross-Section of Option Returns and the Volatility Risk Premium

This paper presents a robust new finding that delta-hedged equity option returns include a volatility risk premium. To separate volatility risk premia from confounding effects, we estimate conditional quantile curves of implied volatilities using machine learning. We find that a zero-cost trading strategy that is long (short) in the portfolio with low (high) implied volatility -- conditional on the options' moneyness and realized volatility -- produces an economically and statistically significant average monthly return. Using conditional quantile curves not only helps in distinguishing volatility risk premia from other effects, most notably realized volatility, it also leads to returns that are higher than those reported in previous work on similar volatility strategies.
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect

The"cold posterior effect"(CPE) in Bayesian deep learning describes the uncomforting observation that the predictive performance of Bayesian neural networks can be significantly improved if the Bayes posterior is artificially sharpened using a temperature parameter T<1. The CPE is problematic in theory and practice and since the effect was identified many researchers have proposed hypotheses to explain the phenomenon. However, despite this intensive research effort the effect remains poorly understood. In this work we provide novel and nuanced evidence relevant to existing explanations for the cold posterior effect, disentangling three hypotheses: 1. The dataset curation hypothesis of Aitchison (2020): we show empirically that the CPE does not arise in a real curated data set but can be produced in a controlled experiment with varying curation strength. 2. The data augmentation hypothesis of Izmailov et al. (2021) and Fortuin et al. (2021): we show empirically that data augmentation is sufficient but not necessary for the CPE to be present. 3. The bad prior hypothesis of Wenzel et al. (2020): we use a simple experiment evaluating the relative importance of the prior and the likelihood, strongly linking the CPE to the prior. Our results demonstrate how the CPE can arise in isolation from synthetic curation, data augmentation, and bad priors. Cold posteriors observed"in the wild"are therefore unlikely to arise from a single simple cause; as a result, we do not expect a simple"fix"for cold posteriors.
Energy-efficient Knowledge Distillation for Spiking Neural Networks

Spiking neural networks (SNNs) have been gaining interest as energy-efficient alternatives of conventional artificial neural networks (ANNs) due to their event-driven computation. Considering the future deployment of SNN models to constrained neuromorphic devices, many studies have applied techniques originally used for ANN model compression, such as network quantization, pruning, and knowledge distillation, to SNNs. Among them, existing works on knowledge distillation reported accuracy improvements of student SNN model. However, analysis on energy efficiency, which is also an important feature of SNN, was absent. In this paper, we thoroughly analyze the performance of the distilled SNN model in terms of accuracy and energy efficiency. In the process, we observe a substantial increase in the number of spikes, leading to energy inefficiency, when using the conventional knowledge distillation methods. Based on this analysis, to achieve energy efficiency, we propose a novel knowledge distillation method with heterogeneous temperature parameters. We evaluate our method on two different datasets and show that the resulting SNN student satisfies both accuracy improvement and reduction of the number of spikes. On MNIST dataset, our proposed student SNN achieves up to 0.09% higher accuracy and produces 65% less spikes compared to the student SNN trained with conventional knowledge distillation method. We also compare the results with other SNN compression techniques and training methods.
Cognitive-aware Short-text Understanding for Inferring Professions

Leveraging short-text contents to estimate the occupation of microblog authors has significant gains in many applications. Yet challenges abound. Firstly brief textual contents come with excessive lexical noise that makes the inference problem challenging. Secondly, cognitive-semantics are not evident, and important linguistic features are latent in short-text contents. Thirdly, it is hard to measure the correlation between the cognitive short-text semantics and the features pertaining various occupations. We argue that the multi-aspect cognitive features are needed to correctly associate short-text contents to a particular job and discover suitable people for the careers. To this end, we devise a novel framework that on the one hand, can infer short-text contents and exploit cognitive features, and on the other hand, fuses various adopted novel algorithms, such as curve fitting, support vector, and boosting modules to better predict the occupation of the authors. The final estimation module manufactures the $R^w$-tree via coherence weight to tune the best outcome in the inferring process. We conduct comprehensive experiments on real-life Twitter data. The experimental results show that compared to other rivals, our cognitive multi-aspect model can achieve a higher performance in the career estimation procedure, where it is inevitable to neglect the contextual semantics of users.
An energy decomposition theorem for matrices and related questions

Abstract Given $A\subseteq GL_2(\mathbb {F}_q)$ , we prove that there exist disjoint subsets $B, C\subseteq A$ such that $A = B \sqcup C$ and their additive and multiplicative energies satisfying $$\begin{align*}\max\{\,E_{+}(B),\, E_{\times}(C)\,\}\ll \frac{|A|^3}{M(|A|)}, \end{align*}$$ where $$ \begin{align*} M(|A|) = \min\Bigg\{\,\frac{q^{4/3}}{|A|^{1/3}(\log|A|)^{2/3}},\, \frac{|A|^{4/5}}{q^{13/5}(\log|A|)^{27/10}}\,\Bigg\}. \end{align*} $$ We also study some related questions on moderate expanders over matrix rings, namely, for $A, B, C\subseteq GL_2(\mathbb {F}_q)$ , we have $$\begin{align*}|AB+C|, ~|(A+B)C|\gg q^4,\end{align*}$$ whenever $|A||B||C|\gg q^{10 + 1/2}$ . These improve earlier results due to Karabulut, Koh, Pham, Shen, and Vinh ([2019], Expanding phenomena over matrix rings, $Forum Math.$ , 31, 951–970).
JUGE: An infrastructure for benchmarking Java unit test generators

Researchers and practitioners have designed and implemented various automated test case generators to support effective software testing. Such generators exist for various languages (e.g., Java, C#, or Python) and various platforms (e.g., desktop, web, or mobile applications). The generators exhibit varying effectiveness and efficiency, depending on the testing goals they aim to satisfy (e.g., unit‐testing of libraries versus system‐testing of entire applications) and the underlying techniques they implement. In this context, practitioners need to be able to compare different generators to identify the most suited one for their requirements, while researchers seek to identify future research directions. This can be achieved by systematically executing large‐scale evaluations of different generators. However, executing such empirical evaluations is not trivial and requires substantial effort to select appropriate benchmarks, setup the evaluation infrastructure, and collect and analyse the results. In this Software Note, we present our JUnit Generation Benchmarking Infrastructure (JUGE) supporting generators (search‐based, random‐based, symbolic execution, etc.) seeking to automate the production of unit tests for various purposes (validation, regression testing, fault localization, etc.). The primary goal is to reduce the overall benchmarking effort, ease the comparison of several generators, and enhance the knowledge transfer between academia and industry by standardizing the evaluation and comparison process. Since 2013, several editions of a unit testing tool competition, co‐located with the Search‐Based Software Testing Workshop, have taken place where JUGE was used and evolved. As a result, an increasing amount of tools (over 10) from academia and industry have been evaluated on JUGE, matured over the years, and allowed the identification of future research directions. Based on the experience gained from the competitions, we discuss the expected impact of JUGE in improving the knowledge transfer on tools and approaches for test generation between academia and industry. Indeed, the JUGE infrastructure demonstrated an implementation design that is flexible enough to enable the integration of additional unit test generation tools, which is practical for developers and allows researchers to experiment with new and advanced unit testing tools and approaches.
Smooth rigidity and Remez inequalities via Topology of level sets

A smooth rigidity inequalitiy provides an explicit lower bound for the (d+1)st derivatives of a smooth function f , which holds, if f exhibits certain patterns, forbidden for polynomials of degree d. The main goal of the present paper is twofold: first, we provide an overview of some recent results and questions related to smooth rigidity, which recently were obtained in Singularity Theory, in Approximation Theory, and in Whitney smooth extensions. Second, we prove some new results, specifically, a new Remez-type inequality, and on this base we obtain a new rigidity inequality. In both parts of the paper we stress the topology of the level sets, as the input information. Here are the main new results of the paper: Let B be the unit n-dimensional ball. For a given integer d let Z ⊂ B be a smooth compact hypersurface with N = (d − 1) + 1 connected components Zj . Let μj be the n-volume of the interior of Zj, and put μ = minμj , j = 1, . , N . Then for each polynomial P of degree d on R we have maxBn |P | max Z |P | ≤ ( 4n μ ). As a consequence, we provide an explicit lower bound for the (d+1)-st derivatives of any smooth function f , which vanishes on Z, while being of order 1 on B (smooth rigidity): ||f || ≥ 1 (d+ 1)! ( 4n μ ). We also provide an interpretation, in terms of smooth rigidity, of one of the simplest versions of the results in [8].
Online Sub-Sampling for Reinforcement Learning with General Function Approximation

Designing provably efﬁcient algorithms with general function approximation is an important open problem in reinforcement learning. Recently, Wang et al. [2020c] establish a value-based algorithm with general function approximation that enjoys (cid:101) O (poly( dH ) √ K ) 1 regret bound, where d depends on the complexity of the function class, H is the planning horizon, and K is the total number of episodes. However, their algorithm requires Ω( K ) computation time per round, rendering the algorithm inefﬁcient for practical use. In this paper, by applying online sub-sampling techniques, we develop an algorithm that takes (cid:101) O (poly( dH )) computation time per round on average, and enjoys nearly the same regret bound. Furthermore, the algorithm achieves low switching cost, i.e., it changes the policy only (cid:101) O (poly( dH )) times during its execution, making it appealing to be implemented in real-life scenarios. Moreover, by using an upper-conﬁdence based exploration-driven reward function, the algorithm provably explores the environment in the reward-free setting. In particular, after (cid:101) O (poly( dH )) /(cid:15) 2 rounds of exploration, the algorithm outputs an (cid:15) -optimal policy for any given reward function.
Federated Learning With Spiking Neural Networks

As neural networks get widespread adoption in resource-constrained embedded devices, there is a growing need for low-power neural systems. Spiking Neural Networks (SNNs) are emerging to be an energy-efficient alternative to the traditional Artificial Neural Networks (ANNs) which are known to be computationally intensive. From an application perspective, as federated learning involves multiple energy-constrained devices, there is a huge scope to leverage energy efficiency provided by SNNs. Despite its importance, there has been little attention on training SNNs on a large-scale distributed system like federated learning. In this paper, we bring SNNs to a more realistic federated learning scenario. Specifically, we design a federated learning method for training decentralized and privacy preserving SNNs. To validate the proposed method, we experimentally evaluate the advantages of SNNs on various aspects of federated learning with CIFAR10 and CIFAR100 benchmarks. We observe that SNNs outperform ANNs in terms of overall accuracy by over 15% when the data is distributed across a large number of clients in the federation while providing up to $4.3\times$ energy efficiency. In addition to efficiency, we also analyze the sensitivity of the proposed federated SNN framework to data distribution among the clients, stragglers, and gradient noise and perform a comprehensive comparison with ANNs. The source code is available at https://github.com/Intelligent-Computing-Lab-Yale/FedSNN.
A Wasserstein Minimax Framework for Mixed Linear Regression

Multi-modal distributions are commonly used to model clustered data in statistical learning tasks. In this paper, we consider the Mixed Linear Regression (MLR) problem. We propose an optimal transport-based framework for MLR problems, Wasserstein Mixed Linear Regression (WMLR), which minimizes the Wasserstein distance between the learned and target mixture regression models. Through a model-based duality analysis, WMLR reduces the underlying MLR task to a nonconvex-concave minimax optimization problem, which can be provably solved to find a minimax stationary point by the Gradient Descent Ascent (GDA) algorithm. In the special case of mixtures of two linear regression models, we show that WMLR enjoys global convergence and generalization guarantees. We prove that WMLR's sample complexity grows linearly with the dimension of data. Finally, we discuss the application of WMLR to the federated learning task where the training samples are collected by multiple agents in a network. Unlike the Expectation Maximization algorithm, WMLR directly extends to the distributed, federated learning setting. We support our theoretical results through several numerical experiments, which highlight our framework's ability to handle the federated learning setting with mixture models.
Understanding Deflation Process in Over-parametrized Tensor Decomposition

In this paper we study the training dynamics for gradient flow on over-parametrized tensor decomposition problems. Empirically, such training process often first fits larger components and then discovers smaller components, which is similar to a tensor deflation process that is commonly used in tensor decomposition algorithms. We prove that for orthogonally decomposable tensor, a slightly modified version of gradient flow would follow a tensor deflation process and recover all the tensor components. Our proof suggests that for orthogonal tensors, gradient flow dynamics works similarly as greedy low-rank learning in the matrix setting, which is a first step towards understanding the implicit regularization effect of over-parametrized models for low-rank tensors.
Posthoc Verification and the Fallibility of the Ground Truth

Classifiers commonly make use of pre-annotated datasets, wherein a model is evaluated by pre-defined metrics on a held-out test set typically made of human-annotated labels. Metrics used in these evaluations are tied to the availability of well-defined ground truth labels, and these metrics typically do not allow for inexact matches. These noisy ground truth labels and strict evaluation metrics may compromise the validity and realism of evaluation results. In the present work, we conduct a systematic label verification experiment on the entity linking (EL) task. Specifically, we ask annotators to verify the correctness of annotations after the fact (, posthoc). Compared to pre-annotation evaluation, state-of-the-art EL models performed extremely well according to the posthoc evaluation methodology. Surprisingly, we find predictions from EL models had a similar or higher verification rate than the ground truth. We conclude with a discussion on these findings and recommendations for future evaluations. The source code, raw results, and evaluation scripts are publicly available via the MIT license at https://github.com/yifding/e2e_EL_evaluate