nils-herrmann/clean_scirepeval huggingface dataset:
On algebraic integers all conjugates of which belong to a given compact subset of the complex plane

The study of Frobenius endomorphism provides numerous information about its corresponding Abelian variety. To understand the action of the Frobenius endomorphism, one may be interested in its eigenvalues. According to Weil's third conjecture ("Riemann hypothesis over finite fields"), they all have absolute value less than or equal to $2g\sqrt{p}$. Thus, the eigenvalues of the Frobenius endomorphism all belong to the same compact subset of the complex plane, and are roots of the same monic polynomial with integer coefficients (the characteristic polynomial of the Frobenius endomorphism). Such complex numbers are called algebraic integers "totally" in a compact subset, which means algebraic integers all conjugates of which belong to a same given compact subset of the complex plane. 
The study of such algebraic integers helps to understand the eigenvalues of the Frobenius endomorphism, especially their distribution. In this paper, we will study the following question : under which conditions a compact subset of the complex plane has a finite or infinite number of algebraic integers "totally" in it ? The problem can be studied in light of the notion of capacity of a compact subset, which comes from potential theory. In this paper, we will present the theory of capacity and some theorems (Fekete, Szego, Robinson) derived from it that partially answer the question: in the case of a union of real segments, when the capacity is smaller (resp. larger) than 1, it contains a finite (resp. infinite) number of algebraic integers totally in it. For instance, for real line segments, the limit length is 4. 
This paper is written as part of a collective project conducted in Ecole Polytechnique (France). It is aimed towards undergraduate audience in mathematics, with basic knowledge in algebra, topology, analysis, and dwells into a modern topic of research.
Championing Research Through Design in HRI

One of the challenges in conducting research on the intersection of the CHI and Human-Robot Interaction (HRI) communities is in addressing the gap of acceptable design research methods between the two. While HRI is focused on interaction with robots and includes design research in its scope, the community is not as accustomed to exploratory design methods as the CHI community. This workshop paper argues for bringing exploratory design, and specifically Research through Design (RtD) methods that have been established in CHI for the past decade to the foreground of HRI. RtD can enable design researchers in the field of HRI to conduct exploratory design work that asks what is the right thing to design and share it within the community.
Challenges of Designing HCI for Negative Emotions

Emotions that are perceived as "negative" are inherent in the human experience. Yet not much work in the field of HCI has looked into the role of these emotions in interaction with technology. As technology is becoming more social, personal and emotional by mediating our relationships and generating new social entities (such as conversational agents and robots), it is valuable to consider how it can support people's negative emotions and behaviors. Research in Psychology shows that interacting with negative emotions correctly can benefit well-being, yet the boundary between helpful and harmful is delicate. This workshop paper looks at the opportunities of designing for negative affect, and the challenge of "causing no harm" that arises in an attempt to do so.
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes

The developments of Rademacher complexity and PAC-Bayesian theory have been largely independent. One exception is the PAC-Bayes theorem of Kakade, Sridharan, and Tewari (2008), which is established via Rademacher complexity theory by viewing Gibbs classifiers as linear operators. The goal of this paper is to extend this bridge between Rademacher complexity and state-of-the-art PAC-Bayesian theory. We first demonstrate that one can match the fast rate of Catoni's PAC-Bayes bounds (Catoni, 2007) using shifted Rademacher processes (Wegkamp, 2003; Lecue and Mitchell, 2012; Zhivotovskiy and Hanneke, 2018). We then derive a new fast-rate PAC-Bayes bound in terms of the "flatness" of the empirical risk surface on which the posterior concentrates. Our analysis establishes a new framework for deriving fast-rate PAC-Bayes bounds and yields new insights on PAC-Bayesian theory.
Developing Creative AI to Generate Sculptural Objects

We explore the intersection of human and machine creativity by generating sculptural objects through machine learning. This research raises questions about both the technical details of automatic art generation and the interaction between AI and people, as both artists and the audience of art. We introduce two algorithms for generating 3D point clouds and then discuss their actualization as sculpture and incorporation into a holistic art installation. Specifically, the Amalgamated DeepDream (ADD) algorithm solves the sparsity problem caused by the naive DeepDream-inspired approach and generates creative and printable point clouds. The Partitioned DeepDream (PDD) algorithm further allows us to explore more diverse 3D object creation by combining point cloud clustering algorithms and ADD.
Siblings, friends and acquaintances: testing galaxy association methods

In order to constrain the limitations of association methods applied to galaxy surveys, we analysed the catalogue of haloes at z = 0 of a cosmological simulation, trying to reproduce the limitations dealt with by an observational survey. We focused on the percolation method, usually called the friends of friends method, commonly used in the literature. The analysis was carried out on the dark matter cosmological simulation MDPL2, from the MultiDark project. The results point to a large fraction of contaminants for massive haloes in high-density environments. Thresholds in the association parameters and the subsequent analysis of observational properties can mitigate the occurrence of fake positives. The use of tests for substructures can also be efficient in particular cases.
Quest for new physics using astrophysical neutrino flavour in IceCube

We have detected astrophysical neutrinos in IceCube that can be used to probe astrophysical sources at ultra high scales. Here we report a search for anomalous space time effects using astrophysical neutrino flavor data in IceCube. New effective operators are introduced to drive non-standard neutrino flavor mixing which modify the flavor ratios compared to standard cases. Using the High Energy Starting Events sample (HESE) 7.5-year data for this analysis, we found no evidence of such flavor anomalies. However, we are expecting to set limits from this new approach which goes far beyond any known techniques. Importantly, we achieve the necessary precision to probe new physics using neutrino flavor expected by Planck scale theories.
Piecewise visual, linearly connected metrics on boundaries of relatively hyperbolic groups

Suppose a finitely generated group  see text] is hyperbolic relative to  see text] a set of proper finitely generated subgroups of  see text]. Established results in the literature imply that a “visual” metric on  see text] is “linearly connected” if and only if the boundary  see text] has no cut point. Our goal is to produce linearly connected metrics on  see text] that are “piecewise” visual when  see text] contains cut points. Our main theorem is connected to graph of groups decompositions of relatively hyperbolic groups  see text] by work of B. Bowditch. We describe piecewise visual linearly connected metrics on connected boundaries of relatively hyperbolic groups. Our metric on  see text] agrees with the visual metric on limit sets of vertex groups and is in this sense piecewise visual.
Automatic and Simultaneous Adjustment of Learning Rate and Momentum for Stochastic Gradient Descent

Stochastic Gradient Descent (SGD) methods are prominent for training machine learning and deep learning models. The performance of these techniques depends on their hyperparameter tuning over time and varies for different models and problems. Manual adjustment of hyperparameters is very costly and time-consuming, and even if done correctly, it lacks theoretical justification which inevitably leads to "rule of thumb" settings. In this paper, we propose a generic approach that utilizes the statistics of an unbiased gradient estimator to automatically and simultaneously adjust two paramount hyperparameters: the learning rate and momentum. We deploy the proposed general technique for various SGD methods to train Convolutional Neural Networks (CNN's). The results match the performance of the best settings obtained through an exhaustive search and therefore, removes the need for a tedious manual tuning.
Implications of Quantum Computing for Artificial Intelligence alignment research

We explain some key features of quantum computing via three heuristics and apply them to argue that a deep understanding of quantum computing is unlikely to be helpful to address current bottlenecks in Artificial Intelligence Alignment. Our argument relies on the claims that Quantum Computing leads to compute overhang instead of algorithmic overhang, and that the difficulties associated with the measurement of quantum states do not invalidate any major assumptions of current Artificial Intelligence Alignment research agendas. We also discuss tripwiring, adversarial blinding, informed oversight and side effects as possible exceptions.
Detecting Gas Vapor Leaks Using Uncalibrated Sensors

Chemical and infra-red sensors generate distinct responses under similar conditions because of sensor drift, noise or resolution errors. In this paper, we develop novel machine learning methods for detecting and identifying VOC and Ammonia vapor from time-series data obtained by uncalibrated chemical and infrared sensors. We process time-series sensor signals using deep neural networks (DNN). Three neural network algorithms are utilized for this purpose. Additive neural networks (termed AddNet) are based on a multiplication-devoid operator and consequently exhibit energy efficiency compared to regular neural networks. The second algorithm uses generative adversarial neural networks so as to expose the classifying neural network to more realistic data points in order to help the classifier network to deliver improved generalization. Finally, we use conventional convolutional neural networks as a baseline method. Our findings indicate that using raw time-series data obtained from uncalibrated sensors and processing them using deep-learning-based methods yield better results than using hand-crafted feature parameters.
Prospects of Earth-skimming neutrino detection with HAWC

Searches for Earth-skimming neutrinos using volcanoes have not yet been achieved, but it is a promising technique for the detection of neutrinos above 1 PeV. The HAWC observatory is located in the vicinity of the highest mountain in Mexico, the Pico de Orizaba volcano, which shields the detector from quasi-horizontal very high-energy (VHE) muons. The large amount of shielding, up to 8 km of rock, enables the suppression of the large VHE muon background and makes neutrino detection possible. In this work we present the first steps towards the implementation of the Earth-skimming technique for the indirect measurement of tau neutrinos with HAWC. The results include the description of the charged lepton tracking reconstruction algorithm developed for this study and the initial analysis of the background of VHE muons using half a year of data.
Optimal Investment with Correlated Stochastic Volatility Factors

The problem of portfolio allocation in the context of stocks evolving in random environments, that is with volatility and returns depending on random factors, has attracted a lot of attention. The problem of maximizing a power utility at a terminal time with only one random factor can be linearized thanks to a classical distortion transformation. In the present paper, we address the problem with several factors using a perturbation technique around the case where these factors are perfectly correlated reducing the problem to the case with a single factor. We illustrate our result with a particular model for which we have explicit formulas. A rigorous accuracy result is also derived using a verification result for the HJB equation involved. In order to keep the notations as explicit as possible, we treat the case with one stock and two factors and we describe an extension to the case with two stocks and two factors.
Control of molecular ultracold plasma relaxation dynamics by mm-wave Rydberg–Rydberg transitions

Resonant mm-wave fields drive transitions in a state-selected Rydberg gas of NO. This transformation produces a clear signature in the selected field ionisation spectrum and dramatically increases the intensity of corresponding features in the spectrum of the Rydberg series observed in UV–UV double resonant transitions via the state. Here, refers to the principal quantum number of an f Rydberg state converging to the rotational state of NO. Enhancement owing to transitions from (f) to (g) appears both in the electron signal detected at early time by the field ionisation of Rydberg molecules and, later, as the late-peak signal of plasma in a state of arrested relaxation. Similar stabilisation and enhanced intensity also occurs for shorter-lived interloping complex resonances, and . We conclude from these observations that avalanche alone does not guarantee a plasma state of arrested relaxation. But rather, the formation of an arrested phase requires both avalanche-produced NO ions and a persistent population of long-lived Rydberg molecules.
P2L: Predicting Transfer Learning for Images and Semantic Relations

We describe an efficient method to accurately estimate the effectiveness of a previously trained deep learning model for use in a new learning task. We use this method, "Predict To Learn" (P2L), to predict the most likely "source" dataset to produce effective transfer for training on a "target" dataset. We validate our approach extensively across 21 tasks, including image classification tasks and semantic relationship prediction tasks in the linguistic domain. The P2L approach selects the best transfer learning model on 62% of the tasks, compared with a baseline of 48% of cases when using a heuristic of selecting the largest source dataset and 52% of cases when using a distance measure between source and target datasets. Further, our work results in an 8% reduction in error rate. Finally, we also show that a model trained from merging multiple source model datasets does not necessarily result in improved transfer learning. This suggests that performance of the target model depends upon the relative composition of the source dataset as well as their absolute scale, as measured by our novel method we term ‘P2L’.
How to gamble with non-stationary $\mathcal{X}$-armed bandits and have no regrets

In $\mathcal{X}$-armed bandit problem an agent sequentially interacts with environment which yields a reward based on the vector input the agent provides. The agent's goal is to maximise the sum of these rewards across some number of time steps. The problem and its variations have been a subject of numerous studies, suggesting sub-linear and some times optimal strategies. The given paper introduces a novel variation of the problem. We consider an environment, which can abruptly change its behaviour an unknown number of times. To that end we propose a novel strategy and prove it attains sub-linear cumulative regret. Moreover, in case of highly smooth relation between an action and the corresponding reward, the method is nearly optimal. The theoretical result are supported by experimental study.
Risk-Efficient Bayesian Data Synthesis for Privacy Protection

Statistical agencies utilize models to synthesize respondent-level data for release to the public for privacy protection. In this study, we efficiently induce privacy protection into any Bayesian synthesis model by employing a pseudo-likelihood that exponentiates each likelihood contribution by an observation record-indexed weight ∈[0,1], defined to be inversely proportional to the identification risk for that record. We start with the marginal probability of identification risk for a record, which is composed as the probability that the identity of the record may be disclosed. Our application to the Consumer Expenditure Surveys (CE) of the U.S. Bureau of Labor Statistics demonstrates that the marginally risk-weighted synthesizer provides an overall improved privacy protection. However, the identification risks actually increase for some moderate-risk records after risk-weighted pseudo-posterior estimation synthesis owing to increased isolation after weighting, a phenomenon we label “whack-a-mole.” We proceed to construct a weight for each record from a collection of pairwise identification risk probabilities with other records, where each pairwise probability measures the joint probability of reidentification of the pair of records, which mitigates the whack-a-mole issue and produces a more efficient set of synthetic data with lower risk and higher utility for the CE data.
On Object Symmetries and 6D Pose Estimation from Images

Objects with symmetries are common in our daily life and in industrial contexts, but are often ignored in the recent literature on 6D pose estimation from images. In this paper, we study in an analytical way the link between the symmetries of a 3D object and its appearance in images. We explain why symmetrical objects can be a challenge when training machine learning algorithms that aim at estimating their 6D pose from images. We propose an efficient and simple solution that relies on the normalization of the pose rotation. Our approach is general and can be used with any 6D pose estimation algorithm. Moreover, our method is also beneficial for objects that are 'almost symmetrical', i.e. objects for which only a detail breaks the symmetry. We validate our approach within a Faster-RCNN framework on a synthetic dataset made with objects from the T-Less dataset, which exhibit various types of symmetries, as well as real sequences from T-Less.
AdaCliP: Adaptive Clipping for Private SGD

Privacy preserving machine learning algorithms are crucial for learning models over user data to protect sensitive information. Motivated by this, differentially private stochastic gradient descent (SGD) algorithms for training machine learning models have been proposed. At each step, these algorithms modify the gradients and add noise proportional to the sensitivity of the modified gradients. Under this framework, we propose AdaCliP, a theoretically motivated differentially private SGD algorithm that provably adds less noise compared to the previous methods, by using coordinate-wise adaptive clipping of the gradient. We empirically demonstrate that AdaCliP reduces the amount of added noise and produces models with better accuracy.
An Expert System Approach for determine the stage of UiTM Perlis Palapes Cadet Performance and Ranking Selection

The Palapes cadets are one of the uniform organizations in UiTM Perlis for extra-curricular activities. The Palapes cadets arrange their organization in a hierarchy according to grade. Senior Uniform Officer (SUO) is the highest rank, followed by Junior Uniform Officer (JUO), sergeant, corporal, lance corporal, and lastly, cadet officer, which is the lowest rank. The Palapes organization has several methods to measure performance toward promotion to higher rank, whether individual performance or in a group. Cadets are selected for promotion based on demonstrated leadership abilities, acquired skills, physical fitness, and comprehension of information as measured through standardized testing. However, this method is too complicated when manually assessed by a trainer or coach. Therefore, this study will propose an expert system, which is one of the artificial intelligence techniques that can recognize the readiness and progression of a Palapes cadet. Readiness is the degree to which a cadet is capable of fulfilling duties involving leadership skill and progression is the rate or manner to which a cadet is assigned new leadership or managerial duties. This study also will ensure that all eligible Palapes cadets will receive a full and equitable opportunity to compete for promotion and for the trainers to actively monitor their cadets' performance.
Understanding and Partitioning Mobile Traffic using Internet Activity Records Data - A Spatiotemporal Approach

The internet activity records (IARs) of a mobile cellular network posses significant information which can be exploited to identify the network's efficacy and the mobile users' behavior. In this work, we extract useful information from the IAR data and identify a healthy predictability of spatio-temporal pattern within the network traffic. The information extracted is helpful for network operators to plan effective network configuration and perform management and optimization of network's resources. We report experimentation on spatiotemporal analysis of IAR data of the Telecom Italia. Based on this, we present mobile traffic partitioning scheme. Experimental results of the proposed model is helpful in modelling and partitioning of network traffic patterns.
Myopic robust index tracking with Bregman divergence

Index tracking is a popular form of asset management. Typically, a quadratic function is used to define the tracking error of a portfolio and the look back approach is applied to solve the index tracking problem. We argue that a forward looking approach is more suitable, whereby the tracking error is expressed as an expectation of a function of the difference between the returns of the index and of the portfolio. We also assume that there is model uncertainty in the distribution of the assets, hence a robust version of the optimization problem needs to be adopted. We use Bregman divergence in describing the deviation between the nominal and actual (true) distribution of the components of the index. In this scenario, we derive the optimal robust index tracking portfolio in a semi-analytical form as a solution of a system of nonlinear equations. Several numerical results are presented that allow us to compare the performance of this robust portfolio with the optimal non-robust portfolio. We show that, especially during market downturns, the robust portfolio can be very advantageous.