Miracle-dz/UniFAR-FTUs huggingface dataset:
Neutrino non-standard interactions: A status report

This report summarizes the present status of neutrino non-standard interactions (NSI). After a brief overview, several aspects of NSIs are discussed, including connection to neutrino mass models, model-building and phenomenology of large NSI with both light and heavy mediators, NSI phenomenology in both short- and long-baseline neutrino oscillation experiments, neutrino cross-sections, complementarity of NSI with other low- and high-energy experiments, fits with neutrino oscillation and scattering data, DUNE sensitivity to NSI, effective field theory of NSI, as well as the relevance of NSI to dark matter and cosmology. We also discuss the open questions and interesting future directions that can be pursued by the community at large. This report is based on talks and discussions during the Neutrino Theory Network NSI workshop held at Washington University in St.~Louis from May 29-31, 2019
Intercore spontaneous Raman scattering impact on quantum key distribution in multicore fiber

We propose a theoretical model to quantitatively estimate the intercore spontaneous Raman scattering (ICSRS) in multicore fiber based on intercore crosstalk. We analyze the properties of ICSRS through numerical simulations. Then, the impact of ICSRS on quantum key distribution (QKD) is evaluated with a classical dense wavelength division multiplexing system in a seven-core fiber. We evaluate the impact in two scenarios, long-distance transmission and metropolitan area transmission. For long-distance transmission, it is revealed that both the forward-ICSRS and backward-ICSRS will reduce the maximum transmission distance of QKD and backward-ICSRS has more impairment to QKD. However, over the range of metropolitan area networks, QKD is not impaired seriously by ICSRS with typical power of classical signal. Finally, we compare the properties of SRS generated in single-core fiber and the properties of ICSRS.
Efficient Cyber Attacks Detection in Industrial Control Systems Using Lightweight Neural Networks

Industrial control systems (ICSs) are widely used and vital to industry and society. Their failure can have severe impact on both economics and human life. Hence, these systems have become an attractive target for attacks, both physical and cyber. A number of attacks detection methods were proposed, however, they are characterized by an insufficient detection rate, a substantial false positives rate, or are system specific. In this paper, we study an attack detection method based on simple and lightweight neural networks, namely, 1D convolutions and autoencoders. We apply these networks to both time and frequency domains of the collected data and discuss pros and cons of each approach. We evaluate the suggested method on three popular public datasets and achieve detection metrics matching or exceeding previously published detection results, while featuring small footprint, short training and detection times, and generality.
A Local Perspective on the Edge Removal Problem

The edge removal problem studies the loss in network coding rates that results when a network communication edge is removed from a given network. It is known, for example, that in networks restricted to linear coding schemes and networks restricted to Abelian group codes, removing an edge e∗ with capacity Re∗ reduces the achievable rate on each source by no more than Re∗. In this work, we seek to uncover larger families of encoding functions for which the edge removal statement holds. We take a local perspective: instead of requiring that all network encoding functions satisfy certain restrictions (e.g., linearity), we limit only the function carried on the removed edge e∗. Our central results give sufficient conditions on the function carried by edge e∗ in the code used to achieve a particular rate vector under which we can demonstrate the achievability of a related rate vector once e∗ is removed.
Understanding flaky tests: the developer’s perspective

Flaky tests are software tests that exhibit a seemingly random outcome (pass or fail) despite exercising unchanged code. In this work, we examine the perceptions of software developers about the nature, relevance, and challenges of flaky tests. We asked 21 professional developers to classify 200 flaky tests they previously fixed, in terms of the nature and the origin of the flakiness, as well as of the fixing effort. We also examined developers' fixing strategies. Subsequently, we conducted an online survey with 121 developers with a median industrial programming experience of five years. Our research shows that: The flakiness is due to several different causes, four of which have never been reported before, despite being the most costly to fix; flakiness is perceived as significant by the vast majority of developers, regardless of their team's size and project's domain, and it can have effects on resource allocation, scheduling, and the perceived reliability of the test suite; and the challenges developers report to face regard mostly the reproduction of the flaky behavior and the identification of the cause for the flakiness. Public preprint [http://arxiv.org/abs/1907.01466], data and materials [https://doi.org/10.5281/zenodo.3265785].
The use of the fractal Brouers-Sotolongo formalism to analyze the kinetics of drug release

We have applied the Brouers-Sotolongo fractal kinetic equation (BSf(t,n,{\alpha})), improving notably the precision, to nine cases reported recently in the literature on drug release. The reason of using this equation is that it contains as approximations some of the mostly used empirical formula used in that field. Moreover, this equation is now successfully employed for the investigation of sorption of contaminants in aqueous media. An important extension of the BSf(t,n,{\alpha}) has been the introduction of variation of the fractal time coefficient ({\alpha}(t^{\nu} )). This improvement can lead to a greater precision of the fits and deduce some hint on the nature of the drug release process which can give precious information to propose microscopic molecular ad hoc models. We, therefore, suggest the use of the BSf(t,n,{\alpha}(t^{\nu})) formula, as a first step, in any detailed investigation and practical application of drug release data both in vitro and in vivo studies starting with the Weibull and Hill approximations to follow properly the physical solution.
Weighted Lorentz gradient and point-wise estimates for solutions to quasilinear divergence form elliptic equations with an application

In this paper, our work is aimed to show the fractional maximal gradient estimates and point-wise gradient estimates for quasilinear divergence form elliptic equations with general Dirichlet boundary data: \begin{align*} \begin{cases} \mathrm{div}(A(x,\nabla u)) &= \ \mathrm{div}(f) \quad \text{in} \ \ \Omega, \\ \hspace{1.2cm} u &= \ g \quad \qquad \text{on} \ \ \partial \Omega, \end{cases} \end{align*} in terms of the Riesz potentials, where $\Omega$ is a Reifenberg flat domain of $\mathbb{R}^n$ ($n \ge 2$), the nonlinearity $A$ is a monotone Caratheodory vector valued function, $g$ belongs to some $W^{1,p}(\Omega;\mathbb{R})$ for $p>1$ and $f \in L^{\frac{p}{p-1}}(\Omega; \mathbb{R}^n)$. Our proofs of gradient regularity results are established in the weighted Lorentz spaces. Here, we generalize our earlier results concerning the ``good-$\lambda$'' technique and the study of so-called cut-off fractional maximal functions. Moreover, as an application of point-wise gradient estimates, we also prove the existence of solutions for a generalized quasilinear elliptic equation containing the Riesz potential of gradient term.
Operationalizing Individual Fairness with Pairwise Fair Representations

We revisit the notion of individual fairness proposed by Dwork et al. A central challenge in operationalizing their approach is the difficulty in eliciting a human specification of a similarity metric. In this paper, we propose an operationalization of individual fairness that does not rely on a human specification of a distance metric. Instead, we propose novel approaches to elicit and leverage side-information on equally deserving individuals to counter subordination between social groups. We model this knowledge as a fairness graph, and learn a unified Pairwise Fair Representation (PFR) of the data that captures both data-driven similarity between individuals and the pairwise side-information in fairness graph. We elicit fairness judgments from a variety of sources, including human judgments for two real-world datasets on recidivism prediction (COMPAS) and violent neighborhood prediction (Crime & Communities). Our experiments show that the PFR model for operationalizing individual fairness is practically viable.
Properties and Algorithms of the KCube Interconnection Networks

The KCube interconnection network was first introduced in 2010 in order to exploit the good characteristics of two well-known interconnection networks, the hypercube and the Kautz graph. KCube links up multiple processors in a communication network with high density for a fixed degree. Since the KCube network is newly proposed, much study is required to demonstrate its potential properties and algorithms that can be designed to solve parallel computation problems. In this thesis we introduce a new methodology to construct the KCube graph. Also, with regard to this new approach, we will prove its Hamiltonicity in the generalKC(m, k). Moreover, we will find its connectivity followed by an optimal broadcasting scheme in which a source node containing a message is to communicate it with all other processors. In addition to KCube networks, we have studied a version of the routing problem in the traditional hypercube, investigating this problem: whether there exists a shortest path in a Qn between two nodes 0 and 1, when the network is experiencing failed components. We first conditionally discuss this problem when there is a constraint on the number of faulty nodes, and subsequently introduce an algorithm to tackle the problem without restrictions on the number of nodes.
Landmark Assisted CycleGAN for Cartoon Face Generation

In this paper, we are interested in generating an cartoon face of a person by using unpaired training data between real faces and cartoon ones. A major challenge of this task is that the structures of real and cartoon faces are in two different domains, whose appearance differs greatly from each other. Without explicit correspondence, it is difficult to generate a high quality cartoon face that captures the essential facial features of a person. In order to solve this problem, we propose landmark assisted CycleGAN, which utilizes face landmarks to define landmark consistency loss and to guide the training of local discriminator in CycleGAN. To enforce structural consistency in landmarks, we utilize the conditional generator and discriminator. Our approach is capable to generate high-quality cartoon faces even indistinguishable from those drawn by artists and largely improves state-of-the-art.
Application of a Topological Descriptor for Protein Interface Identification and Protein Binding Prediction

APPLICATION OF A TOPOLOGICAL DESCRIPTOR FOR PROTEIN INTERFACE IDENTIFICATION AND PROTEIN BINDING PREDICTION Olivia J. Peters, PhD George Mason University, 2010 Dissertation Director: Dr. Iosif Vaisman The identification of proteins which interact or form complexes is a critical step in advancing several aspects of computational biology, including intelligent protein design and functional prediction. Previous methods have focused primarily on sequence alignment or threading methods to accomplish this, requiring large libraries of sequences. This work is an attempt to advance the current field of protein prediction through the use of a structural geometry methodology proven successful for many other aspects of proteomic analyses. The method is extended in two ways; first, a classification approach is created to identify protein residues involved in the binding interface, with the intent of using this information to aid the prediction of protein complex formation. Results are promising, with better than eighty percent correct classification, comparable to the best techniques currently in use. Second, a methodology was created to score potential docking conformations. Of the 54 proteins in the test data set, 43 had a near-native structure in the top 100 positions, and a median ratio of successfully identified residue contacts of 0.57. The structural geometry method has been successfully applied to these two problems to advance the state of the field of proteomics.
Directional Wideband Channel Measurements at 28 GHz in an Industrial Environment

With the expected adoption of millimeter wave technologies for industrial communication, it is fundamentally important to properly understand the radio channel characteristics of such environments. This paper presents the setup, scenario and results of a measurement campaign at 28 GHz in a machine hall. The radio channel was measured with a bandwidth of 2 GHz and both large scale parameters and directional information were extracted. Evaluation of the power delay profiles shows that the channel contains dense multipath components. A path loss model is parameterized and blockage losses, RMS delay and angle spreads are evaluated. Comparison with the 3GPP TR 38.901 channel model shows that none of the currently defined scenarios is a fit for industrial settings. This emphasizes the need for a newly defined scenario with an industry specific parameter set.
Experimental Evidence of Hydrodynamic Instantons: The Universal Route to Rogue Waves

A statistical theory of rogue waves is proposed and tested against experimental data collected in a long water tank where random waves with different degrees of nonlinearity are mechanically generated and free to propagate along the flume. Strong evidence is given that the rogue waves observed in the tank are hydrodynamic instantons, that is, saddle point configurations of the action associated with the stochastic model of the wave system. As shown here, these hydrodynamic instantons are complex spatio-temporal wave field configurations, which can be defined using the mathematical framework of Large Deviation Theory and calculated via tailored numerical methods. These results indicate that the instantons describe equally well rogue waves that originate from a simple linear superposition mechanism (in weakly nonlinear conditions) or from a nonlinear focusing one (in strongly nonlinear conditions), paving the way for the development of a unified explanation to rogue wave formation.
On the Applicability of κ-distributions

The standard (nonrelativistic) κ-distribution is widely used to fit data and to describe macroscopic thermodynamical behavior, e.g., the pressure (temperature) as the second moment of the distribution function. By contrast to a Maxwellian distribution, for small relevant values κ < 2 there exists a significant, but unphysical contribution to the pressure from unrealistic, superluminal particles with speeds exceeding the speed of light. Similar concerns exist for the entropy. We demonstrate here that by using the recently introduced regularized κ-distribution one can avoid such unphysical behavior.
On the Reliability of Magnetic Energy and Helicity Computations Based on Nonlinear Force-free Coronal Magnetic Field Models

We demonstrate the sensitivity of magnetic energy and helicity computations regarding the quality of the underlying coronal magnetic field model. We apply the method of Wiegelmann & Inhester to a series of Solar Dynamics Observatory/Helioseismic and Magnetic Imager vector magnetograms, and discuss nonlinear force-free (NLFF) solutions based on two different sets of the free model parameters. The two time series differ from each other concerning their force-free and solenoidal quality. Both force- and divergence-freeness are required for a consistent NLFF solution. Full satisfaction of the solenoidal property is inherent in the definition of relative magnetic helicity in order to ensure gauge independence. We apply two different magnetic helicity computation methods to both NLFF time series and find that the output is highly dependent on the level to which the NLFF magnetic fields satisfy the divergence-free condition, with the computed magnetic energy being less sensitive than the relative helicity. Proxies for the nonpotentiality and eruptivity derived from both quantities are also shown to depend strongly on the solenoidal property of the NLFF fields. As a reference for future applications, we provide quantitative thresholds for the force- and divergence-freeness, for the assurance of reliable computation of magnetic energy and helicity, and of their related eruptivity proxies.
Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation

Video object segmentation (VOS) aims at pixel-level object tracking given only the annotations in the first frame. Due to the large visual variations of objects in video and the lack of training samples, it remains a difficult task despite the upsurging development of deep learning. Toward solving the VOS problem, we bring in several new insights by the proposed unified framework consisting of object proposal, tracking and segmentation components. The object proposal network transfers objectness information as generic knowledge into VOS; the tracking network identifies the target object from the proposals; and the segmentation network is performed based on the tracking results with a novel dynamic-reference based model adaptation scheme. Extensive experiments have been conducted on the DAVIS'17 dataset and the YouTube-VOS dataset, our method achieves the state-of-the-art performance on several video object segmentation benchmarks. We make the code publicly available at this https URL.
A hybrid algorithm framework for small quantum computers with application to finding Hamiltonian cycles

Recent works have shown that quantum computers can polynomially speed up certain SAT-solving algorithms even when the number of available qubits is significantly smaller than the number of variables. Here we generalise this approach. We present a framework for hybrid quantum-classical algorithms which utilise quantum computers significantly smaller than the problem size. Given an arbitrarily small ratio of the quantum computer to the instance size, we achieve polynomial speedups for classical divide-and-conquer algorithms, provided that certain criteria on the time- and space-efficiency are met. We demonstrate how this approach can be used to enhance Eppstein's algorithm for the cubic Hamiltonian cycle problem, and achieve a polynomial speedup for any ratio of the number of qubits to the size of the graph.
Comparative Causal Mediation and Relaxing the Assumption of No Mediator–Outcome Confounding: An Application to International Law and Audience Costs

Experiments often include multiple treatments, with the primary goal to compare the causal effects of those treatments. This study focuses on comparing the causal anatomies of multiple treatments through the use of causal mediation analysis. It proposes a novel set of comparative causal mediation (CCM) estimands that compare the mediation effects of different treatments via a common mediator. Further, it derives the properties of a set of estimators for the CCM estimands and shows these estimators to be consistent (or conservative) under assumptions that do not require the absence of unobserved confounding of the mediator–outcome relationship, which is a strong and nonrefutable assumption that must typically be made for consistent estimation of individual causal mediation effects. To illustrate the method, the study presents an original application investigating whether and how the international legal status of a foreign policy commitment can increase the domestic political “audience costs” that democratic governments suffer for violating such a commitment. The results provide novel evidence that international legalization can enhance audience costs via multiple causal channels, including by amplifying the perceived immorality of violating the commitment.
Optimal lower bounds for Donaldson's $\mathcal{J}$-functional

In this paper we study optimal lower bounds for Donaldson's J-functional, and give an explicit formula for precisely how far it is from being proper. As a main application this leads to new sufficient conditions for existence of constant scalar curvature Kahler metrics in terms of Tian's alpha invariant. Using the above formula we also discuss Calabi dream manifolds and an analogous notion for the J-equation, and show that for surfaces the optimal bound is an explicitly computable rational function which typically tends to minus infinity as the underlying class approaches the boundary of the Kahler cone, even when the underlying Kahler classes admit Kahler metrics of constant scalar curvature. As a final application, we show that if the Lejmi-Szekelyhidi conjecture holds, then the optimal bound coincides with its algebraic counterpart, the set of J-semistable classes equals the closure of the set of uniformly J-stable classes in the Kahler cone, and there exists an optimal degeneration for uniform J-stability.
Results of mobile channel sounding measurements in the 4.9 GHz public safety band

This paper describes a channel sounding system and a corresponding data processing method for the characterization of wireless channels in the 4.9 GHz public safety band. The equipment and subsequent processing enable high-resolution estimation of propagation delay, Doppler frequency shift, and direction-of-arrival (azimuth and elevation) associated with multipath signal components. Results are shown that verify the proper functioning of the equipment and parameter estimation method.
Evaluating the Effect of Clothing and Environment on the perceived Personality of Virtual Avatars

Virtual avatars gain more and more importance in our everyday lives, especially in the field of human machine interaction and affective interfaces. The avatar can be used as a virtual surrogate to answer questions in online costumer support applications, they can virtually tutor students in e-learning scenarios, or they can mimic a real person's behavior in specific situations for training purposes in for example medical consulting sessions or accidents. It is easy to imagine that, just as with real people, appropriate clothing style and environment might be very important to sustain the intended reality and maintain reliability, believability, and seriousness of the virtual avatar. As clothing style is known to convey personal attributes of another person like sociability, orderliness, or openness it is highly recommended to carefully adjust the avatar's clothes to its usage, purpose, and intended goal. It is also known that environment influences how we feel; we can feel relaxed in calm and quite surroundings (like at home or in the nature) or stressed in loud and noisy settings (like a train station or an airport). While designing a human-machine-interface the environment can also have an effect on our own feelings and thereby influence how we perceive a virtual avatar. This paper would like to investigate the role of clothing and environment on the perception of a virtual character. With the help of two experiments a virtual character's perceived personality is measured using personality traits based on the OCEAN dimensions in several different clothing styles and different environments.