Miracle-dz/UniFAR-FTUs huggingface dataset:
Report of the Topical Group on Neutrino Properties for Snowmass 2021

Neutrinos are the most elusive among the known elementary particles, because of their feeble interactions with ordinary matter. They are also the most mysterious, because of their tiny masses that suggest a novel mass generating mechanism, their unknown Dirac or Majorana nature, and their big quantum mixing leading to large-amplitude flavor oscillations. This Topical Group focuses on neutrino properties that are not directly investigated in other Topical Groups of the Neutrino Frontier: in particular, the absolute value of the neutrino masses, the Dirac or Majorana nature of neutrinos, their electromagnetic properties, their lifetime, and hypothetical exotic properties. carlo.giunti@to.infn. it (NF05 convener and editor) jgruszko@unc.edu (NF05 convener and editor) ben.jones@uta.edu (NF05 convener and editor) ljkauf@slac.stanford. edu (NF05 convener and editor) dparno@cmu.edu (NF05 convener and editor) pocar@umass.edu, (co-editor and RF04 convener) 1 ar X iv :2 20 9. 03 34 0v 1 [ he pph ] 7 S ep 2 02 2
Predicting the entire glassy dynamics from static structure by machine learning relative motion

The recent developments of machine learning have enabled accurate predictions of glassy dynamics. However, existing machine-learning models are designed so that they learn a single dynamic quantity and related it to the structural features of glassy liquids. In this Letter, we propose a graph neural network model which learns relative motion between neighboring pairs of particles, in addition to self motion of particles. By relating the structural features to these two di ﬀ erent dynamical variables, the model autonomously acquires the ability to discern how two di ﬀ erent types of dynamic processes, strain ﬂuctuations and particle rearrangements, a ﬀ ect self motion of particles undergoing slow relaxation. Our model a ﬀ ords a methodology to treat these two at the same time and realizes highly precise predictions of glassy structural relaxation over the entire temporal range.
A Time-Domain Real-Valued Generalized Wiener Filter for Multi-Channel Neural Separation Systems

Frequency-domain beamformers have been successful in a wide range of multi-channel neural separation systems in the past years. However, the operations in conventional frequency-domain beamformers are typically independently-defined and complex-valued, which result in two drawbacks: the former does not fully utilize the advantage of end-to-end optimization, and the latter may introduce numerical instability during the training phase. Motivated by the recent success in end-to-end neural separation systems, in this paper we propose time-domain real-valued generalized Wiener filter (TD-GWF), a linear filter defined on a 2-D learnable real-valued signal transform. TD-GWF splits the transformed representation into groups and performs an minimum mean-square error (MMSE) estimation on all available channels on each of the groups. We show how TD-GWF can be connected to conventional filter-and-sum beamformers when certain signal transform and the number of groups are specified. Moreover, given the recent success in the sequential neural beamforming frameworks, we show how TD-GWF can be applied in such frameworks to perform iterative beamforming and separation to obtain an overall performance gain. Comprehensive experiment results show that TD-GWF performs consistently better than conventional frequency-domain beamformers in the sequential neural beamforming pipeline with various neural network architectures, microphone array scenarios, and task configurations.
Characterizing Spontaneous Ideation Contest on Social Media: Case Study on the Name Change of Facebook to Meta

Collecting good ideas is vital for organizations, especially companies, to retain their competitiveness. Social media is gathering attention as a place to extract ideas efficiently; however, the characteristics of ideas and the posters of ideas on social media are underexamined. Thus, this study aims to characterize spontaneous ideation contests among social media users by taking an event of Facebook’s name change to Meta as a case study. As a dataset, we comprehensively collect tweets containing new acronyms of Big Tech companies, which we treat as an "idea" in this work. In the analysis, we especially focus on the diversity of ideas, which would be the main reason for enlisting social media for idea generation. As the main results, we discovered that social media users offered a wider range of ideas than those in mainstream media. The follow-follower network of the users suggested that the users’ position on the network is related to the preferred ideas. Additionally, we discovered a link between the amount of user interaction on social media and the diversity of ideas. This study would promote the use of social media as a part of open innovation and co-creation processes in the industry.
FPHammer: A Device Identification Framework based on DRAM Fingerprinting

The device fingerprinting technique extracts fingerprints based on the hardware characteristics of the device to identify the device. The primary goal of device fingerprinting is to accurately and uniquely identify a device, which requires the generated device fingerprints to have good stability to achieve long-term tracking of the target device. However, the fingerprints generated by some existing fingerprinting technologies are not stable enough or change frequently, making it impossible to track the target device for a long time. In this paper, we present FPHammer, a novel DRAM-based fingerprinting technique. The device fingerprint generated by our technique has high stability and can be used to track the device for a long time. We leverage the Rowhammer technique to repeatedly and quickly access a row in DRAM to get bit flips in its adjacent row. We then construct a physical fingerprint of the device based on the locations of the collected bit flips. The evaluation results of the uniqueness and reliability of the physical fingerprint show that it can be used to distinguish devices with the same hardware and software configuration. The experimental results on device identification demonstrate that the physical fingerprints engendered by our innovative technique are inherently linked to the entirety of the device rather than just the DRAM module. Even if the device modifies software-level parameters such as MAC address and IP address or even reinstalls the operating system, we can accurately identify the target device. This demonstrates that FPHammer can generate stable fingerprints that are not affected by software layer parameters.
Semiparametric discrete data regression with Monte Carlo inference and prediction

Discrete data are abundant and often arise as counts or rounded data. These data commonly exhibit complex distributional features such as zero-inflation, over-/under-dispersion, boundedness, and heaping, which render many parametric models inadequate. Yet even for parametric regression models, approximations such as MCMC typically are needed for posterior inference. This paper introduces a Bayesian modeling and algorithmic framework that enables semiparametric regression analysis for discrete data with Monte Carlo (not MCMC) sampling. The proposed approach pairs a nonparametric marginal model with a latent linear regression model to encourage both flexibility and interpretability, and delivers posterior consistency even under model misspecification. For a parametric or large-sample approximation of this model, we identify a class of conjugate priors with (pseudo) closed-form posteriors. All posterior and predictive distributions are available analytically or via direct Monte Carlo sampling. These tools are broadly useful for linear regression, nonlinear models via basis expansions, and variable selection with discrete data. Simulation studies demonstrate significant advantages in computing, prediction, estimation, and selection relative to existing alternatives. This novel approach is applied successfully to self-reported mental health data that exhibit zero-inflation, overdispersion, boundedness, and heaping.
Supervised Hebbian learning

In neural network's literature, Hebbian learning traditionally refers to the procedure by which the Hopfield model and its generalizations store archetypes (i.e., definite patterns that are experienced just once to form the synaptic matrix). However, the term learning in machine learning refers to the ability of the machine to extract features from the supplied dataset (e.g., made of blurred examples of these archetypes), in order to make its own representation of the unavailable archetypes. Here, given a sample of examples, we define a supervised learning protocol based on Hebb's rule and by which the Hopfield network can infer the archetypes. By an analytical inspection, we detect the correct control parameters (including size and quality of the dataset) that tune the system performance and we depict its phase diagram. We also prove that, for structureless datasets, the Hopfield model equipped with this supervised learning rule is equivalent to a restricted Boltzmann machine and this suggests an optimal and interpretable training routine. Finally, this approach is generalized to structured datasets: we highlight an ultrametric-like organization (reminiscent of replica-symmetry-breaking) in the analyzed datasets and, consequently, we introduce an additional broken-replica hidden layer for its (partial) disentanglement, which is shown to improve MNIST classification from to , and to offer a new perspective on deep architectures.
An Investigation into the Spectral Imaging of Hall Thruster Plumes

Abstract : An exploratory investigation of measuring xenon spectral emission lines in a Hall thruster plume through optical imaging techniques was performed on the SPT-100 Hall thruster. A 16 bit CCD camera was used with optical interference bandwidth filters to separately photograph neutral (823 nm) and ion (542 nm) xenon emission. The resulting images allowed for examination of the near-plume structure in terms of excited ions and neutral particles for various thruster operating conditions. Abel inversion analysis was applied to the photographs to examine the emission as a function of radius with the assumption of axial symmetry and optically thin plasma. This experimental technique may provide results useful for comparison with numerical near-plume predictions that incorporate collisional radiative models.
Ricci-Rank 1 Lorentz manifoldlarında statik tam çözümler

Einstein alan denklemlerinin cozumlerinde kullanilan ve kotanjant demetinin isiksal yerel tabani icin hareketli cati yaklasimina esdeger olan, Newman-Penrose (NP) formalizmi kullanilarak, durumu icin Ricci tensorunun rankinin 1 olma kosulu elde edildi. Daha sonra genel bir B tipi warped carpim metrik icin NP sistemi verilerek, bu sistemde manifoldun ikinci bileseninin sabit egrilige sahip, R 3' e gomulmus, kompakt, baglantili ve reguler bir manifold olmasi durumunda uzay-zamanin kuresel simetrik, Ricci tensoru matrisinin rankinin 1 ve Enerji-momentum tensoru matrisinin tekil olmamasi durumunda, fiziksel kaynagin kutlesiz bir skaler alan olmasi gerektigi ispatlandi. Genel B tipi warped carpim metrikten, kuresel simetrik metrige gecilerek, NP buyuklukleri hesaplandi. Einstein alan denklemleri, Ricci tensoru icin rank 1 kosulu kullanilarak elde edildi. Ikinci olarak, (3+1) boyutta Einstein alan denklemleri, kuresel simetrik metrige kutlesiz skaler alanin kuple edilmesiyle calisildi. Baslangicta "over determined" olan orjinal sistem, ve degiskenleri Choptuik (Choptuik, M.W., (1993). "Universality and scaling in gravitational collapse of massless scalar field", Physical Review Letters, 70, 9)'de oldugu gibi kullanilarak, μ = m/r ve y = α/ra fonksiyonlari icin efektif olarak zamana gore ikinci dereceden normal bir sisteme indirgendi. μ ve y icin, zamandan bagimsiz (statik) duruma, skaler alanin veya olmasi durumunda gecildi ve bunlar sirasiyla pozitif ve negatif dal olarak adlandirildi. Pozitif dal olarak adlandirdigimiz statik durum icin hem tam cozum bulundu, hem de faz duzlemi analizi ile asikar cozumunun bolgesinde bir genel cekim noktasi oldugu kanitlandi. Negatif dal olarak adlandirdigimiz statik durum, dinamik bir sistem olarak incelendi ve tum cozum egrileri karakterize edilerek noktasinin bolgesinde genel bir cekim noktasi oldugu ispat edildi. Anahtar Kelimeler: Choptuik uzay zamani, kuresel simetrik statik cozumler, kutlesiz skaler alan.
Adaptive Algorithms for Relatively Lipschitz Continuous Convex Optimization Problems

Recently, there were proposed some innovative convex optimization concepts, namely, relative smoothness [3] and relative strong convexity [6, 8]. These approaches have signiﬁcantly expanded the class of applicability of gradient-type methods with optimal estimates of the convergence rate. Later Yu. Nesterov and H. Lu [6, 8] introduced some modiﬁcations of the Mirror Descent method for convex minimization problems with the corresponding analogue of the Lipschitz condition (the so-called relative continuity or Lipschitz continuity). In this paper, we cover both the concept of relative smoothness and relative Lipschitz continuity and introduce some adaptive and universal methods which have optimal estimates of the convergence rate for the corresponding class of problems. We consider the relative boundedness condition for the variational inequality problem and propose some adaptive optimal methods for this class of problems. Some results of the conducted numerical experiments are presented, which demonstrate the eﬀectiveness of the proposed methods.
Influence of anisotropic fiber properties on tensile elastic modulus estimation for carbon fiber reinforced thermoplastics

The influence of anisotropic fiber properties on estimation of the elastic modulus of carbon fiber-reinforced polyamide 6 (CFRPA6) was studied using two kinds of micromechanics models; Nairn et al. and Hashin (N–H) models, and Mori–Tanaka (M–T) model. First, these models’ accuracy was verified by comparison with an elastic modulus measured for injection molded dumbbell specimens of CFRPA6. Although there was a slight difference between the elastic moduli estimated from the N–H models and the M–T model, the difference was discussed in terms of model properties. Next, estimation errors caused by assuming carbon fiber (CF) as isotropic were calculated under several conditions. The maximum error occurred in the condition that the tensile direction was perpendicular to the fiber axis. Despite CF had large anisotropy, it was approximately 10% for both models. We infer that the small error was caused by a specific stress field.
Exploring the gap between academic geography and school geography: knowledge transformation of the competencies-based curriculum making in China

ABSTRACT The gap between academic geography and school geography is of ongoing interest in geography education. To achieve a comprehensive understanding of the gap, this article describes how a framework of knowledge transformation was applied and validated in China. A total of 182 individuals involved in geography education took part in this study; these included geographers, pre- and in-service geography teachers, and geography teacher educators. Though geographical knowledge is seen as important at the societal level, this research found substantial discrepancies between academic and school geography at the institutional and classroom levels of knowledge transformation. Insufﬁcient engagement in disciplinary development and a lack of support from the academic community are two major reasons for the gap. Finally, future progress in addressing the gap in order to generate powerful geographical knowledge in the competencies-based curricula is highlighted.
Accelerating molecular dynamics simulations by a hybrid molecular dynamics-continuum mechanical approach

ABSTRACT In this contribution, molecular dynamics (MD) simulations in combination with continuum mechanical (CM) approaches are performed to investigate particle movements under uniaxial deformations of an amorphous polymer at particle resolution. A coarse-grained (CG) model of atactic polystyrene is used as an exemplary model system. We propose a hybrid molecular dynamics-continuum mechanical (MD-CM) approach to simulate the deformation behavior of the polymer. As a reference, purely molecular dynamics systems are used. The methods are compared with regard to the local displacement of the particles and the global stress-strain behavior of the overall system. The good reproducibility of the system’s mechanical behavior with the hybrid molecular dynamics-continuum mechanical method is shown. Furthermore, it is demonstrated that CPU time can be significantly reduced with the hybrid calculation model.
Performance Evaluation and Validation of Air Samplers To Detect Aerosolized Coxiella burnetii

Coxiella burnetii causes Q fever in humans and coxiellosis in animals. It is important to know if C. burnetii is present in the air around putative sources as it is transmitted via inhalation. ABSTRACT Coxiella burnetii, the etiological agent of Q fever, is an intracellular zoonotic pathogen transmitted via the respiratory route. Once released from infected animals, C. burnetii can travel long distances through air before infecting another host. As such, the ability to detect the presence of C. burnetii in air is important. In this study, three air samplers, AirPort MD8, BioSampler, and the Coriolis Micro, were assessed against a set of predetermined criteria in the presence of three different aerosolized C. burnetii concentrations. Two liquid collection media, phosphate-buffered saline (PBS) and alkaline polyethylene glycol (Alk PEG), were tested with devices requiring a collection liquid. Samples were tested by quantitative polymerase chain reaction assay (qPCR) targeting the single-copy com1 gene or multicopy insertion element IS1111. All air samplers performed well at detecting airborne C. burnetii across the range of concentrations tested. At high nebulized concentrations, AirPort MD8 showed higher, but variable, recovery probabilities. While the BioSampler and Coriolis Micro recovered C. burnetii at lower concentrations, the replicates were far more repeatable. At low and intermediate nebulized concentrations, results were comparable in the trials between air samplers, although the AirPort MD8 had consistently higher recovery probabilities. In this first study validating air samplers for their ability to detect aerosolized C. burnetii, we found that while all samplers performed well, not all samplers were equal. It is important that these results are further validated under field conditions. These findings will further inform efforts to detect airborne C. burnetii around known point sources of infection. IMPORTANCE Coxiella burnetii causes Q fever in humans and coxiellosis in animals. It is important to know if C. burnetii is present in the air around putative sources as it is transmitted via inhalation. This study assessed air samplers (AirPort MD8, BioSampler, and Coriolis Micro) for their efficacy in detecting C. burnetii. Our results show that all three devices could detect aerosolized bacteria effectively; however, at high concentrations the AirPort performed better than the other two devices, showing higher percent recovery. At intermediate and low concentrations AirPort detected at a level higher than or similar to that of other samplers. Quantification of samples was hindered by the limit of quantitation of the qPCR assay. Compared with the other two devices, the AirPort was easier to handle and clean in the field. Testing air around likely sources (e.g., farms, abattoirs, and livestock saleyards) using validated sampling devices will help better estimate the risk of Q fever to nearby communities.
Physicochemical and sensory characterization of meat from Santa Ines sheep fed with cactus forage (Opuntia ficus indica mill)

Forty non-castrated male lambs were randomly assigned to groups fed with cactus forage (0,17.6, 35.3, 53.2 or 71.1%) in place of buffelgrass hay to evaluate the effects onphysicochemical and sensory properties of sheep meat. There was no influence of weightloss by cooking, shear force, moisture, lipids and ash (P>0.05). Effects were observed forprotein (P<0.05), initial lightness (P<0.01) and final color (P<0.05) when increasing the levelof cactus forage in the diet. Reduction in saturated fatty acids was found, mainly myristicacids, and increased levels of monounsaturated fatty acids and oleic acids, due tobiohydrogenation. The use of cactus forage in the diet as a replacement for buffelgrasssignificantly influences the content of fatty acids in Santa Ines sheep meat, improving the lipidprofile, without influencing sensory attributes.
The topology of four-dimensional manifolds

0. Introduction Manifold topology enjoyed a golden age in the late 1950's and 1960's. Of the mysteries still remaining after that period of great success the most compelling seemed to lie in dimensions three and four. Although experience suggested that manifold theory at these dimensions has a distinct character, the dream remained since my graduate school days that some key principle from the high dimensional theory would extend, at least to dimension four, and bring with it the beautiful adherence of topology to algebra familiar in dimensions greater than or equal to five. There is such a principle. It is a homotopy theoretic criterion for imbedding (relatively) a topological 2-handle in a smooth four-dimensional manifold with boundary. The main impact, as outlined in §1, is to the classification of 1-connected 4-manifolds and topological end recognition. However, certain applications to nonsimply connected problems such as knot concordance are also obtained. The discovery of this principle was made in three stages. From 1973 to 1975 Andrew Casson developed his theory of "flexible handles". These are certain pairs having the proper homotopy type of the common place open 2-handle H = (D X D, dD X D) but "flexible" in the sense that finding imbeddings is rather easy; in fact imbedding is implied by a homotopy theoretic criterion. It was clear to Casson that: (1) no known invariant—link theoretic