Miracle-dz/UniFAR-FTUs huggingface dataset:
“Relationships So Loving and So Hurtful”

Intimate partner violence (IPV) in Asian, Pacific Islander, and Native Hawaiian (APINH) queer women’s and lesbian relationships was examined through interviews with 24 APINH respondents. Seven major themes were uncovered in the dynamics of intimate violence: (a) control, intimidation, and instilling fear; (b) “deep” emotional intimacy; (c) first, early, or rebound relationships; (d) sexual jealousy and possessiveness; (e) shame as a barrier; (f) limited social and potential partner networks; and (g) crossing/intersecting gender in the “butch” as victim. Study implications include expanding research on same-sex IPV focusing on the intersection of ethnicity, gender, and sexual identity.
Concentration bounds for linear Monge mapping estimation and optimal transport domain adaptation

This article investigates the quality of the estimator of the linear Monge mapping between distributions. We provide the first concentration result on the linear mapping operator and prove a sample complexity of $n^{-1/2}$ when using empirical estimates of first and second order moments. This result is then used to derive a generalization bound for domain adaptation with optimal transport. As a consequence, this method approaches the performance of theoretical Bayes predictor under mild conditions on the covariance structure of the problem. We also discuss the computational complexity of the linear mapping estimation and show that when the source and target are stationary the mapping is a convolution that can be estimated very efficiently using fast Fourier transforms. Numerical experiments reproduce the behavior of the proven bounds on simulated and real data for mapping estimation and domain adaptation on images.
Tempus Volat, Hora Fugit - A Survey of Dynamic Network Models in Discrete and Continuous Time

Given the growing number of available tools for modeling dynamic networks, the choice of a suitable model becomes central. It is often difficult to compare the different models with respect to their applicability and interpretation. The goal of this survey is to provide an overview of popular dynamic network models. The survey is focused on introducing binary network models with their corresponding assumptions, advantages, and shortfalls. The models are divided according to generating processes, operating in discrete and continuous time, respectively. First, we introduce the Temporal Exponential Random Graph Model (TERGM) and its extension, the Separable TERGM (STERGM), both being time-discrete models. These models are then contrasted with continuous process models, focusing on the Relational Event Model (REM). We additionally show how the REM can handle time-clustered observations, i.e., continuous time data observed at discrete time points. Besides the discussion of theoretical properties and fitting procedures, we specifically focus on the application of the models using a network that represents international arms transfers. The data allow to demonstrate the applicability and interpretation of the network models.
Single-cell proteomic chip for profiling intracellular signaling pathways in single tumor cells

We describe a microchip designed to quantify the levels of a dozen cytoplasmic and membrane proteins from single cells. We use the platform to assess protein–protein interactions associated with the EGF-receptor-mediated PI3K signaling pathway. Single-cell sensitivity is achieved by isolating a defined number of cells (n = 0–5) in 2 nL volume chambers, each of which is patterned with two copies of a miniature antibody array. The cells are lysed on-chip, and the levels of released proteins are assayed using the antibody arrays. We investigate three isogenic cell lines representing the cancer glioblastoma multiforme, at the basal level, under EGF stimulation, and under erlotinib inhibition plus EGF stimulation. The measured protein abundances are consistent with previous work, and single-cell analysis uniquely reveals single-cell heterogeneity, and different types and strengths of protein–protein interactions. This platform helps provide a comprehensive picture of altered signal transduction networks in tumor cells and provides insight into the effect of targeted therapies on protein signaling networks.
Privacy Risks of Securing Machine Learning Models against Adversarial Examples

The arms race between attacks and defenses for machine learning models has come to a forefront in recent years, in both the security community and the privacy community. However, one big limitation of previous research is that the security domain and the privacy domain have typically been considered separately. It is thus unclear whether the defense methods in one domain will have any unexpected impact on the other domain. In this paper, we take a step towards resolving this limitation by combining the two domains. In particular, we measure the success of membership inference attacks against six state-of-the-art defense methods that mitigate the risk of adversarial examples (i.e., evasion attacks). Membership inference attacks determine whether or not an individual data record has been part of a model's training set. The accuracy of such attacks reflects the information leakage of training algorithms about individual members of the training set. Adversarial defense methods against adversarial examples influence the model's decision boundaries such that model predictions remain unchanged for a small area around each input. However, this objective is optimized on training data. Thus, individual data records in the training set have a significant influence on robust models. This makes the models more vulnerable to inference attacks. To perform the membership inference attacks, we leverage the existing inference methods that exploit model predictions. We also propose two new inference methods that exploit structural properties of robust models on adversarially perturbed data. Our experimental evaluation demonstrates that compared with the natural training (undefended) approach, adversarial defense methods can indeed increase the target model's risk against membership inference attacks. When using adversarial defenses to train the robust models, the membership inference advantage increases by up to 4.5 times compared to the naturally undefended models. Beyond revealing the privacy risks of adversarial defenses, we further investigate the factors, such as model capacity, that influence the membership information leakage.
Relaxation Runge-Kutta Methods: Conservation and Stability for Inner-Product Norms

We further develop a simple modification of Runge--Kutta methods that guarantees conservation or stability with respect to any inner-product norm. The modified methods can be explicit and retain the accuracy and stability properties of the unmodified Runge--Kutta method. We study the properties of the modified methods and show their effectiveness through numerical examples, including application to entropy-stability for first-order hyperbolic PDEs.
Novel method for component separation of extended sources in X-ray astronomy

In high-energy astronomy, spectro-imaging instruments such as X-ray detectors allow investigation of the spatial and spectral properties of extended sources including galaxy clusters, galaxies, diffuse interstellar medium, supernova remnants, and pulsar wind nebulae. In these sources, each physical component possesses a different spatial and spectral signature, but the components are entangled. Extracting the intrinsic spatial and spectral information of the individual components from this data is a challenging task. Current analysis methods do not fully exploit the 2D-1D (x, y, E) nature of the data, as spatial information is considered separately from spectral information. Here we investigate the application of a blind source separation (BSS) algorithm that jointly exploits the spectral and spatial signatures of each component in order to disentangle them. We explore the capabilities of a new BSS method (the general morphological component analysis; GMCA), initially developed to extract an image of the cosmic microwave background from Planck data, in an X-ray context. The performance of the GMCA on X-ray data is tested using Monte-Carlo simulations of supernova remnant toy models designed to represent typical science cases. We find that the GMCA is able to separate highly entangled components in X-ray data even in high-contrast scenarios, and can extract the spectrum and map of each physical component with high accuracy. A modification of the algorithm is proposed in order to improve the spectral fidelity in the case of strongly overlapping spatial components, and we investigate a resampling method to derive realistic uncertainties associated to the results of the algorithm. Applying the modified algorithm to the deep Chandra observations of Cassiopeia A, we are able to produce detailed maps of the synchrotron emission at low energies (0.6–2.2 keV), and of the red- and blueshifted distributions of a number of elements including Si and Fe K.
Isoperimetric cones and minimal solutions of partial overdetermined problems

In this paper we consider a partial overdetermined mixed boundary value problem in domains inside a cone as in [18]. We show that in cones having an isoperimetric property the only domains which admit a solution and which minimize a torsional energy functional are spherical sectors centered at the vertex of the cone. We also show that cones close in the $C^{1,1}$-metric to an isoperimetric one are also isoperimetric, generalizing so a result of [1]. This is achieved by using a characterization of constant mean curvature polar graphs in cones which improves a result of [18].
Signature Codes for Weighted Binary Adder Channel and Multimedia Fingerprinting

In this paper, we study binary signature codes for the weighted binary adder channel (WbAC) and collusion-resistant multimedia fingerprinting. Let <inline-formula> <tex-math notation="LaTeX">$A(n, t)$ </tex-math></inline-formula> denote the maximum size of a <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-signature code of length <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$A(n, w, t)$ </tex-math></inline-formula> denote the maximum size of a <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-signature code of length <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> and constant-weight <inline-formula> <tex-math notation="LaTeX">$w$ </tex-math></inline-formula>. First, we derive asymptotic and general upper bounds on <inline-formula> <tex-math notation="LaTeX">$A(n,t)$ </tex-math></inline-formula> by relating signature codes to <inline-formula> <tex-math notation="LaTeX">$B_{t}$ </tex-math></inline-formula> codes and bipartite graphs with large girth respectively, and also show the upper bounds are tight for certain cases. Second, we determine the exact values of <inline-formula> <tex-math notation="LaTeX">$A(n,2,2)$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$A(n,3,2)$ </tex-math></inline-formula> for infinitely many <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> by connecting signature codes with <inline-formula> <tex-math notation="LaTeX">$C_{4}$ </tex-math></inline-formula>-free graphs and union-free families, respectively. Third, we provide two explicit constructions for <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-signature codes which have efficient decoding algorithms and applications to two-level signature codes. Furthermore, we show from a geometric viewpoint that there does not exist any binary code with complete traceability for noisy WbAC and multimedia fingerprinting. A new type of signature codes with a weaker requirement than complete traceability is introduced for the noisy scenario.
Robust Attribution Regularization

An emerging problem in trustworthy machine learning is to train models that produce robust interpretations for their predictions. We take a step towards solving this problem through the lens of axiomatic attribution of neural networks. Our theory is grounded in the recent work, Integrated Gradients (IG), in axiomatically attributing a neural network's output change to its input change. We propose training objectives in classic robust optimization models to achieve robust IG attributions. Our objectives give principled generalizations of previous objectives designed for robust predictions, and they naturally degenerate to classic soft-margin training for one-layer neural networks. We also generalize previous theory and prove that the objectives for different robust optimization models are closely related. Experiments demonstrate the effectiveness of our method, and also point to intriguing problems which hint at the need for better optimization techniques or better neural network architectures for robust attribution training.
The effective neural drive to muscles is the common synaptic input to motor neurons

Despite the non‐linear property of individual motor neurons, the pool of motor neurons linearizes the relation between their common synaptic input and the neural drive to the muscle, i.e. the ensemble of axonal action potentials reaching the muscle from the spinal cord. In the frequency bandwidth relevant for force generation, the motor neuron pool attenuates the input signals sent independently to each motor neuron and transfers only the common signal components with a pure scaling. The effective neural drive to the muscle tends to exactly replicate, without phase distortion, the common synaptic input to motor neurons for increasing number of active motor neurons. The classic definition and functional meaning of motor unit synchronization are discussed in relation to the role of common input in determining the neural drive to muscle.
Momentum-Based Variance Reduction in Non-Convex SGD

Variance reduction has emerged in recent years as a strong competitor to stochastic gradient descent in non-convex problems, providing the first algorithms to improve upon the converge rate of stochastic gradient descent for finding first-order critical points. However, variance reduction techniques typically require carefully tuned learning rates and willingness to use excessively large "mega-batches" in order to achieve their improved results. We present a new algorithm, STORM, that does not require any batches and makes use of adaptive learning rates, enabling simpler implementation and less hyperparameter tuning. Our technique for removing the batches uses a variant of momentum to achieve variance reduction in non-convex optimization. On smooth losses $F$, STORM finds a point $\boldsymbol{x}$ with $\mathbb{E}[\|\nabla F(\boldsymbol{x})\|]\le O(1/\sqrt{T}+\sigma^{1/3}/T^{1/3})$ in $T$ iterations with $\sigma^2$ variance in the gradients, matching the optimal rate but without requiring knowledge of $\sigma$.
Power up! Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering

Graph convolutional networks (GCNs) are powerful tools for graph-structured data. However, they have been recently shown to be prone to topological attacks. Despite substantial efforts to search for new architectures, it still remains a challenge to improve performance in both benign and adversarial situations simultaneously. In this paper, we re-examine the fundamental building block of GCN---the Laplacian operator---and highlight some basic flaws in the spatial and spectral domains. As an alternative, we propose an operator based on graph powering, and prove that it enjoys a desirable property of "spectral separation. " Based on the operator, we propose a robust learning paradigm, where the network is trained on a family of "'smoothed" graphs that span a spatial and spectral range for generalizability. We also use the new operator in replacement of the classical Laplacian to construct an architecture with improved spectral robustness, expressivity and interpretability. The enhanced performance and robustness are demonstrated in extensive experiments.
On the Star Formation Efficiencies and Evolution of Multiple Stellar Generations in Globular Clusters

By adopting empirical estimates of the helium enhancement (ΔY) between consecutive stellar generations for a sample of Galactic globular clusters (GGCs), we uniquely constraint the star formation efficiency (ϵ) of each stellar generation in these stellar systems. In our approach, the star formation efficiency (ϵ) is the central factor that links stellar generations as it defines both their stellar mass and the remaining mass available for further star formation, fixing also the amount of matter required to contaminate the next stellar generation. In this way, ϵ is here shown to be fully defined by the He enhancement between successive stellar generations in a GC. Our approach also has an impact on the evolution of clusters and thus considers the possible loss of stars through evaporation, tidal interactions and stellar evolution. We focus on the present mass ratio between consecutive stellar generations (M(j−1)G/M(j)G) and the present total mass of GGCs (MGC). Such considerations suffice to determine the relative proportion of stars of consecutive generations that remain today in globular clusters (α(j−1)G/α(j)G). The latter is also shown to directly depend on the values of ΔY and thus the He enhancement between consecutive stellar generations in GGC places major constraints on models of star formation and evolution of GC.
Monitoring dynamic networks: A simulation‐based strategy for comparing monitoring methods and a comparative study

Recently, there has been a lot of interest in monitoring and identifying changes in dynamic networks, which has led to the development of a variety of monitoring methods. New methods are often designed for a specialized use‐case and rarely compared to competing methods in a systematic fashion. In light of this, the use of simulation is proposed to compare the performance of network monitoring methods over a variety of dynamic network changes. Using the family of simulated dynamic networks, the performance of several state‐of‐the‐art social network monitoring methods from the literature are compared. Their performance over a variety of types of change is compared; both increases in communication levels as well as changes in community structure are considered. It is shown that there does not exist one method that is uniformly superior to the others; the best method depends on the context and the type of change one wishes to detect. As such, it is concluded that a variety of methods are needed for network monitoring and that it is important to understand in which scenarios a given method is appropriate.
Fermented Dairy Foods: Impact on Intestinal Microbiota and Health-Linked Biomarkers

The intake of fermented foods is gaining increasing interest due to their health-promoting benefits. Among them, fermented dairy foods have been associated with obesity prevention, and reduction of the risk of metabolic disorders and immune-related pathologies. Fermented foods could lead to these health benefits by providing the consumer with both easily metabolizable nutrients and beneficial microorganisms. Our aim was to evaluate the relationship between the consumption of fermented dairy products and the intestinal microbiota, serum lipid profile, and the pro-oxidant/inflammatory status. 130 healthy adults were evaluated. Dietary fermented food intake was assessed by an annual food frequency questionnaire (FFQ), including 26 fermented dairy products. Levels of the major phylogenetic types of the intestinal microbiota were determined by qPCR, and concentration of fecal short chain fatty acids were assessed by gas chromatography. Serum glucose and lipid profile, as well as serum malondialdehyde (MDA), C-reactive protein (CRP), and leptin levels were determined by standardized protocols. Among fermented dairy foods, natural yogurt, sweetened yogurt and matured/semi-matured cheese were the most consumed. While natural yogurt consumers showed increased fecal levels of Akkermansia with respect to non-consumers, sweetened yogurt intake was associated to lower levels of Bacteroides. Serum levels of CRP were also significantly reduced in yogurt consumers. Our results underline the interest in exploring the potential effects of the different yogurt types and the role the microbiota may play in such effects.
Hardness of Distributed Optimization

This paper studies lower bounds for fundamental optimization problems in the CONGEST model. We show that solving problems exactly in this model can be a hard task, by providing tildeΩmega (n2) lower bounds for cornerstone problems, such as minimum dominating set (MDS), Hamiltonian path, Steiner tree and max-cut. These are almost tight, since all of these problems can be solved optimally in O(n2) rounds. Moreover, we show that even in bounded-degree graphs and even in simple graphs with maximum degree 5 and logarithmic diameter, it holds that various tasks, such as finding a maximum independent set (MaxIS) or a minimum vertex cover, are still difficult, requiring a near-tight number of tildeΩ (n) rounds. Furthermore, we show that in some cases even approximations are difficult, by providing an tildeΩ (n2) lower bound for a (7/8+ε)-approximation for MaxIS, and a nearly-linear lower bound for an O(log n )-approximation for the k-MDS problem for any constant k geq≥ 2, as well as for several variants of the Steiner tree problem. Our lower bounds are based on a rich variety of constructions that leverage novel observations, and reductions among problems that are specialized for the CONGEST model. However, for several additional approximation problems, as well as for exact computation of some central problems in P, such as maximum matching and max flow, we show that such constructions cannot be designed, by which we exemplify some limitations of this framework.
The Degree of Horizontal Violence in RNs Practicing in New York State

This study examined the degree of horizontal violence (HV) among RNs practicing in New York State (NYS). The objective was to determine the knowledge of HV and the degree of being a victim among practicing RNs. A descriptive survey using Briles’ Sabotage Savvy Questionnaire, measuring occurrences of HV and nurses’ knowledge of HV, was conducted among 2659 RNs in NYS hospitals. Findings demonstrate a significantly greater (P < .05) degree of HV in union versus nonunion cultures and less HV in Magnet® organizations. Gender and RN education significantly impacted HV. Developing organizational, leadership, and individual interventions to address HV helps create a culture of safety.