Miracle-dz/UniFAR-FTUs huggingface dataset:
System Description for Voice Privacy Challenge 2022

This paper introduces our system submitted to Voice Privacy Challenge 2022. We adopted the following methods to improve the traditional methods. Firstly, the adversarial anonymization method was used, further hiding speaker information. Then, we extracted the embedding from the encoder of the transformer-based ASR systems because ASR has rich speaker information, so we do not have to train an individual speaker recognition/verification system for speaker embedding extraction. Experimental results prove that the proposed methods can be used for speaker anonymization tasks.
Loyola eCommons Loyola eCommons Jackpot Rollover and Lottery Regressivity Jackpot Rollover and Lottery Regressivity

The majority of earlier studies have found the lottery to be a regressive form of taxation that varies by game and whose regressivity declines at higher jackpot size. This paper conducts an in-depth analysis of the effect of consumer spending on lottery regressivity during the Mega Millions rollover sequence and reports the following findings. First, regressivity among six games examined in the paper varies by game and is inversely related to the prize/jackpot size of the game. Second, an increase in the jackpot size reduces the regressivity for the Mega Millions game, but not for the other five games. Third, the impact of household income distribution on lottery sales varies by game, and in the case of Mega Millions, by jackpot size as well. We did not find a significant difference in the demand for the Mega Millions game between below middle-income households and high-income households. However, the demand by middle and upper-middle income households is significantly higher than the demand by high-income households, especially at a higher jackpot size. Lastly, as the jackpot size grows over $100 million and higher, a large cash inflow from states with no Mega Millions flows into the New Jersey lottery market. The majority of the additional cash inflow is spent on the Mega Millions game and there does not appear to be a significant spillover to other New Jersey lottery games.
Modelling Hyperglycaemia in an Epithelial Membrane Model: Biophysical Characterisation

Biomimetic models are valuable platforms to improve our knowledge on the molecular mechanisms governing membrane-driven processes in (patho)physiological conditions, including membrane permeability, transport, and fusion. However, current membrane models are over simplistic and do not include the membrane’s lipid remodelling in response to extracellular stimuli. Our study describes the synthesis of glycated dimyristoyl-phosphatidylethanolamine (DMPE-glyc), which was structurally characterised by mass spectrometry (ESI-MS) and quantified by NMR spectroscopy to be further incorporated in a complex phospholipid (PL) membrane model enriched in cholesterol (Chol) and (glyco)sphingolipids (GSL) designed to mimic epithelial membranes (PL/Chol/GSL) under hyperglycaemia conditions. Characterisation of synthesised DMPE-glyc adducts by tandem mass spectrometry (ESI-MS/MS) show that synthetic DMPE-glyc adducts correspond to Amadori products and quantification by 1H NMR spectroscopy show that the yield of glycation reaction was 8%. The biophysical characterisation of the epithelial membrane model shows that excess glucose alters the thermotropic behaviour and fluidity of epithelial membrane models likely to impact permeability of solutes. The epithelial membrane models developed to mimic normo- and hyperglycaemic scenarios are the basis to investigate (poly)phenol-lipid and drug–membrane interactions crucial in nutrition, pharmaceutics, structural biochemistry, and medicinal chemistry.
Search and competition in expert markets

We develop a model in which consumers sequentially search experts for recommendations and prices to treat a problem, and experts simultaneously compete in these two dimensions. Consumers have either zero or a positive search cost. In equilibrium, experts may “cheat” by recommending an unnecessary treatment with positive probabilities, prices follow distributions that depend on a consumer’s problem type and the treatment, and consumers search with Bayesian belief updating about their problem types. Remarkably, as search cost decreases, both expert cheating and prices can increase stochastically. However, if search cost is suﬃciently small, competition forces all experts to behave honestly.
PARTICLE ACCELERATION AND MAGNETIC FIELD AMPLIFICATION IN THE JETS OF 4C74.26

We model the multi-wavelength emission in the southern hotspot of the radio quasar 4C74.26. The synchrotron radio emission is resolved near the shock with the MERLIN radio-interferometer, and the rapid decay of this emission behind the shock is interpreted as the decay of the amplified downstream magnetic field as expected for small scale turbulence. Electrons are accelerated to only 0.3 TeV, consistent with a diffusion coefficient many orders of magnitude greater than in the Bohm regime. If the same diffusion coefficient applies to the protons, their maximum energy is only ∼100 TeV.
STRUCTURE? SOME EVIDENCE FROM INTERNATIONAL DATA

Rajan thanks the Center for Research on Securities Prices while Zingales thanks the Graduate School of Business for funding. This project was also made possible with a grant from the Center for International Business Research at the University of Chicago. We thank Patricia O'Brien, Douglas Diamond, Eugene Fama, Steve Kaplan, Anil Kashyap, Merton Miller, James Seward and Rob Vishny for helpful discussions, and participants in workshops at the University of Chicago (Finance and Junior Faculty Lunch Group), HEC-University of Montr6al, Indiana University, University of Maryland, NBER Summer Institute, the Stockholm School of Economics and the 1994 WFA meetings for comments. We are indebted to Eduardo Gonzales for excellent research assistance, and Andrew Alford for invaluable help in getting us acquainted with Global Vantage. This paper incorporates part of a C.R. S.P. working paper entitled "Notes on International Capital Structure". This paper is part of NBER's research program in Corporate Finance. Any opinions expressed are those of the authors and not those of the National Bureau of Economic Research.
NeuralStagger: accelerating physics-constrained neural PDE solver with spatial-temporal decomposition

Neural networks have shown great potential in accelerating the solution of partial differential equations (PDEs). Recently, there has been a growing interest in introducing physics constraints into training neural PDE solvers to reduce the use of costly data and improve the generalization ability. However, these physics constraints, based on certain finite dimensional approximations over the function space, must resolve the smallest scaled physics to ensure the accuracy and stability of the simulation, resulting in high computational costs from large input, output, and neural networks. This paper proposes a general acceleration methodology called NeuralStagger by spatially and temporally decomposing the original learning tasks into several coarser-resolution subtasks. We define a coarse-resolution neural solver for each subtask, which requires fewer computational resources, and jointly train them with the vanilla physics-constrained loss by simply arranging their outputs to reconstruct the original solution. Due to the perfect parallelism between them, the solution is achieved as fast as a coarse-resolution neural solver. In addition, the trained solvers bring the flexibility of simulating with multiple levels of resolution. We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations, which leads to an additional $10\sim100\times$ speed-up. Moreover, the experiment also shows that the learned model could be well used for optimal control.
THE EFFECT OF INCOME AND OPERATING EXPENSES ON NET PROFIT WITH CAPITAL EXPENDITURE AS A MODERATION VARIABLE (STUDY ON ENERGY SECTOR COMPANIES LISTED ON THE IDX PERIOD 2018 - 2021)

: Maximizing profit generation and achieving long-term profit growth are the primary goals of company formation. The company's net profit is significant to both internal and external stakeholders. However, even more, significant is the rise in net profit each period, which, in line with the signaling theory, gives the impression or signal that the company is progressing or doing well. This study aims to determine the effect of income and operating expenses on net profit with capital expenditure as a moderation variable in energy sector companies listed on the Indonesia Stock Exchange. The type of research method used is quantitative. The population used in this study are energy sector companies listed on the Indonesia Stock Exchange. The samples used in this study were energy sector companies listed on the Indonesia Stock Exchange for the 2018-2021 period that met the predetermined criteria, namely 41 samples. The data source used in this study is secondary data with documentation data collection techniques. The analytical techniques used in this study include descriptive statistical analysis, outer model, inner model and hypothesis testing with the help of the SmartPLS 3.0 program. The results indicate that income affects net profit, operating expenses do not affect net profit, and capital expenditure does not moderate the effect of income and operating expenses on net profit.
Intel Labs at ActivityNet Challenge 2022: SPELL for Long-Term Active Speaker Detection

In this report, we describe SPELL, a novel spatial-temporal graph learning framework for active speaker detection (ASD). First, each person in a video frame is encoded in a unique node for that frame. The nodes corresponding to each person across frames are connected to encode their temporal dynamics. Nodes within a frame are also connected to encode inter-person relationships. Thus, SPELL reduces ASD to a node classification task. Importantly, SPELL is able to reason over long temporal contexts for all nodes with low computation cost.
M-Quantile and expectile random effects regression for multilevel data

The analysis of hierarchically structured data is usually carried out by using random effects models. The primary goal of random effects regression is to model the expected value of the conditional distribution of an outcome variable given a set of explanatory variables while accounting for the dependence structure of hierarchical data. The expected value, however, may not offer a complete picture of this conditional distribution. In this paper we propose using linear M-quantile regression, to model other parts of the conditional distribution of the outcome variable given the covariates. The proposed random effects regression model extends M-quantile regression and can be viewed as an alternative to the quantile random effects model. Inference for estimators of the fixed and random effects parameters is discussed. The performance of the proposed methods is evaluated in a series of simulation studies. Finally, we present a case study where M-quantile and expectile random effects regression is employed for analyzing repeated measures data collected from a rotary pursuit tracking experiment.
Gaussian discriminators between Λ CDM and wCDM cosmologies using expansion data

The Gaussian linear model provides a unique way to obtain the posterior probability distribution as well as the Bayesian evidence analytically. Considering the expansion rate data, the Gaussian linear model can be applied for Λ CDM, wCDM and a non-ﬂat Λ CDM. In this paper, we simulate the expansion data with various precision and obtain the Bayesian evidence, then it has been used to discriminate the models. The data uncertainty is in range σ ∈ ( 0 . 5 , 10 ) % and two different sampling rates have been considered. Our results indicate that considering σ = 0 . 5% uncertainty, it is possible to discriminate 2% deviation in equation of state from w = − 1. On the other hand, we investigate how precision of the expansion rate data affects discriminating the Λ CDM from a non-ﬂat Λ CDM model. Finally, we perform a parameters inference in both the MCMC and Gaussian linear model, using current available expansion rate data and compare the results.
CD46 targeted 212 Pb alpha particle radioimmunotherapy for prostate cancer treatment

We recently identified CD46 as a novel prostate cancer cell surface antigen that shows lineage independent expression in both adenocarcinoma and small cell neuroendocrine subtypes of metastatic castration resistant prostate cancer (mCRPC), discovered an internalizing human monoclonal antibody YS5 that binds to a tumor selective CD46 epitope, and developed a microtubule inhibitor-based antibody drug conjugate that is in a multi-center phase I trial for mCRPC (NCT03575819). Here we report the development of a novel CD46-targeted alpha therapy based on YS5. We conjugated 212 Pb, an in vivo generator of alpha-emitting 212 Bi and 212 Po, to YS5 through the chelator TCMC to create the radioimmunoconjugate, 212 Pb-TCMC-YS5. We characterized 212 Pb-TCMC-YS5 in vitro and established a safe dose in vivo . We next studied therapeutic efficacy of a single dose of 212 Pb-TCMC-YS5 using three prostate cancer small animal models: a subcutaneous mCRPC cell line-derived xenograft (CDX) model (subcu-CDX), an orthotopically grafted mCRPC CDX model (ortho-CDX), and a prostate cancer patient-derived xenograft model (PDX). In all three models, a single dose of 20 µCi 212 Pb-TCMC-YS5 was well tolerated and caused potent and sustained inhibition of established tumors, with significant increases of survival in treated animals. A lower dose (10 µCi 212 Pb-TCMC-YS5) was also studied on the PDX model, which also showed a significant effect on tumor growth inhibition and prolongation of animal survival. These results demonstrate that 212 Pb-TCMC-YS5 has an excellent therapeutic window in preclinical models including PDXs, opening a direct path for clinical translation of this novel CD46-targeted alpha radioimmunotherapy for mCRPC treatment. Significance : This study reports a novel CD46 targeted 212 Pb alpha particle radioimmunotherapy, 212 Pb-TCMC-YS5, that is well tolerated and shows potent anti-tumor activity (tumor growth inhibition and increase of animal survival) in vivo in three prostate cancer small animal models, i.e., a subcutaneous and an intraprostate orthotopic mCRPC cell line-derived xenograft models, and a prostate cancer patient-derived xenograft model. Given that YS5 is a clinical stage human antibody, this YS5-based 212 Pb alpha particle therapy has potential of translation to the clinic for treatment of mCRPC patients.
Loyola eCommons Loyola eCommons Reliability and Validity of the Japanese Version of the Ways of Reliability and Validity of the Japanese Version of the Ways of Savoring Checklist (WOSCJ) Savoring Checklist (WOSCJ)

This study represents an important first step in understanding the ways in which Japanese adults savor positive experiences. Our initial empirical evaluation of the Japanese translation of the WOSC produced promising evidence of its utility in measuring Japanese savoring strategies. Future researchers can use this measurement tool with confidence to investigate savoring in Japan and in work comparing savoring across cultures.
Understanding the HMI Pseudocontinuum in White-light Solar Flares

We analyze observations of the X9.3 solar flare (SOL2017-09-06T11:53) observed by SDO/HMI and Hinode/Solar Optical Telescope. Our aim is to learn about the nature of the HMI pseudocontinuum Ic used as a proxy for the white-light continuum. From model atmospheres retrieved by an inversion code applied to the Stokes profiles observed by the Hinode satellite, we synthesize profiles of the Fe i 617.3 nm line and compare them to HMI observations. Based on a pixel-by-pixel comparison, we show that the value of Ic represents the continuum level well in quiet-Sun regions only. In magnetized regions, it suffers from a simplistic algorithm that is applied to a complex line shape. During this flare, both instruments also registered emission profiles in the flare ribbons. Such emission profiles are poorly represented by the six spectral points of HMI and the MDI-like algorithm does not account for emission profiles in general; thus, the derived pseudocontinuum intensity does not approximate the continuum value properly.
S UPPRESSION HELPS : L ATERAL I NHIBITION - INSPIRED C ONVOLUTIONAL N EURAL N ETWORK FOR I MAGE C LASSIFICATION

Convolutional neural networks (CNNs) have become powerful and popular tools since deep learning emerged for image classification in the computer vision field. For better recognition, the dimensions of depth and width have been explored, leading to convolutional neural networks with more layers and more channels. In addition to these factors, neurobiology also suggests the widely existing lateral inhibition (e.g., Mach band effect), which increases the contrast of nearby neuron excitation in the lateral direction, to help recognition. However, such an important mechanism has not been well explored in modern convolutional neural networks. In this paper, we explicitly explore the filter dimension in the lateral direction and propose our lateral inhibition-inspired (LI) design. Our naive design incorporates the low-pass filter, while eliminating the central weight to mimic the inhibition strength decay. The inhibition value is computed from the filtering result of the input, with a simple learnable weight parameter per channel for multiplication to decide the strength. Then the inhibition value is subtracted from the input as suppression, which could increase the contrast to help recognition. We also suggest an alternative using depthwise convolution, as a general form. Our design could work on both the plain convolution and the convolutional block with residual connection, while being compatible with existing modules. Without any channel attention along the channel dimension, the preliminary results demonstrate an absolute improvement of 3.68% and 0.69% over AlexNet and ResNet-18, respectively, in the ImageNet data set, with little increase in parameters, indicating the merits of our design to help feature learning for image classification.
New Low-Temperature Phase of Molecular Deuterium at Ultrahigh Pressure

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Towards ab Initio Thermodynamics of the Electron Gas at Strong Degeneracy

Recently a number of theoretical studies of the uniform electron gas (UEG) at finite temperature have appeared that are of relevance for dense plasmas, warm dense matter and laser excited solids and thermodynamic density functional theory simulations. In particular, restricted path integral Monte Carlo (RPIMC) results became available which, however, due to the Fermion sign problem, are confined to moderate quantum degeneracy, i.e. low to moderate densities. We have recently developed an alternative approach–configuration PIMC [T. Schoof et al., Contrib. Plasma Phys. 51, 687 (2011)] that allows one to study the so far not accessible high degeneracy regime. Here we present the first step towards UEG simulations using CPIMC by studying implementation and performance of the method for the model case of N = 4 particles. We also provide benchmark data for the total energy. (© 2015 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Renormalization of symmetry restricted spin foam models with curvature in the asymptotic regime

We study the renormalization group flow of the Euclidean Engle-Pereira-Rovelli-Livine and Freidel-Krasnov (EPRL-FK) spin foam model in the large-$j$ limit. The vertex amplitude is deformed to include a cosmological constant term. The state sum is reduced to describe a foliated spacetime whose spatial slices are flat, isotropic, and homogeneous. The model admits a nonvanishing extrinsic curvature, whereas the scale factor can expand or contract at successive time steps. The reduction of degrees of freedom allows a numerical evaluation of certain geometric observables on coarser and finer discretizations. Their comparison defines the renormalization group (RG) flow of the model in the parameters $(\ensuremath{\alpha},\mathrm{\ensuremath{\Lambda}},G)$. We first consider the projection of the RG flow along the $\ensuremath{\alpha}$ direction, which shows a UV-attractive fixed point. Then, we extend our analysis to two- and three-dimensional parameter spaces. Most notably, we find the indications of a fixed point in the $(\ensuremath{\alpha},\mathrm{\ensuremath{\Lambda}},G)$ space, showing one repulsive and two attractive directions.
Yield, Dry Production, And Revenue Cost Ratios Of Pennisetum Purpureum Of Cv. Taiwan In Ultisol Soil With Biotechnology Of Bisozyme Mixed Arbuscular Mycorrizal Fungi

This study investigates the effects of Bisozyme mixed with Arbuscular Mycorrhizal Fungi (AMF) CV. Glomus manihotis on yield, production, and revenue cost ratio (RCR) of Pennisetum purpureum of CV. Taiwan in ultisol soil for the first harvest in West Sumatra, Indonesia. The N, P, and K fertilizers for AMF CV. Glomus manihotis inoculation is used at recommended doses of 100%, 75%, 50%, and 25%. Whereas, AMF is inoculated with a dose of 10 grams/grass plant clump. This research employed Bisozyme DT 1000 type and Bisozyme MK 1000 type. This research was conducted using a randomized group design, four groups, and five variables. The groups were P0 (Manure + Fertilizer N, P, and K), P1 (Bisozyme), P2 (AMF CV. Glomus manihotis), P3 (Bisozyme + AMF CV. Glomus manihotis), and P4 (Bisozyme + AMF CV. Glomus manihotis + N, P, and K). The variables were yield, dry production, and RCR. The results of the statistical analysis have shown that the treatments are not significant (P < 0.05) for yield and dry production. Meanwhile, the RCR of grass is significantly different (P > 0.05). The yield, dry production, and RCR range from 57.50 (P1) to 66.53 (P4) ton/ha/year, from 12.18 (P1) to 14.93 (P4) ton/ha/harvesting, and from 1.54 (P2) to 6.71 (P0). These results conclude that Bisozyme mixed with FMA could be used as organic fertilizers to substitute N, P, and K fertilizers.
Statistical Plasma Properties of the Planar and Nonplanar ICME Magnetic Clouds during Solar Cycles 23 and 24

Various remote and in situ observations, along with several models, simulations, and kinetic studies, have been proposed in recent years, suggesting that the morphology of an interplanetary coronal mass ejection (ICME) magnetic cloud can vary from cylindrical, elliptical, toroidal, flattened, pancaked, etc. Recently, Raghav et al. proposed for the first time a unique morphological characteristic of an ICME magnetic cloud at 1 au that showed characteristics of a planar magnetic structure, using in situ data from the ACE spacecraft. In this study, we statistically investigate the plasma properties of planar and nonplanar ICMEs from 1998–2017 at 1 au. The detailed study of 469 ICMEs suggests that 136 (∼29%) ICMEs are planar, whereas 333 (∼71%) are nonplanar. Furthermore, total interplanetary magnetic field strength, average plasma parameters, i.e., plasma density, beta, thermal pressure, and magnetic pressure in planar ICME, are significantly higher than in the nonplanar ICME. Also, we noticed that the thickness of planar ICMEs is less compared to nonplanar ICMEs. This analysis demonstrates that planar ICMEs are formed due to the high compression of ICME. Moreover, we also observed the southward/northward magnetic field component’s double strength during planar ICMEs compared to nonplanar ICMEs. It implies that planar ICMEs are more geoeffective than nonplanar ICMEs.