New Threats to Privacy-preserving Text Representations

The users’ privacy concerns mandate data publishers to protect privacy by anonymizing the data before sharing it with data consumers. Thus, the ultimate goal of privacy-preserving representation learning is to protect user privacy while ensuring the utility, e.g., the accuracy of the published data, for future tasks and usages. Privacy-preserving embeddings are usually functions that are encoded to low-dimensional vectors to protect privacy while preserving important semantic information about an input text. We demonstrate that these embeddings still leak private information, even though the low dimensional embeddings encode generic semantics. We develop two classes of attacks, i.e., adversarial classification attack and adversarial generation attack, to study the threats for these embeddings. In particular, the threats are (1) these embeddings may reveal sensitive attributes letting alone if they explicitly exist in the input text, and (2) the embedding vectors can be partially recovered via generation models. Besides, our experimental results show that our approach can produce higher-performing adversary models than other adversary baselines

SOT-MRAM-Enabled Probabilistic Binary Neural Networks for Noise-Tolerant and Fast Training

We report the use of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) to implement a probabilistic binary neural network (PBNN) for resource-saving applications. The in-plane magnetized SOT (i-SOT) MRAM not only enables field-free magnetization switching with high endurance (> 10^11), but also hosts multiple stable probabilistic states with a low device-to-device variation (< 6.35%). Accordingly, the proposed PBNN outperforms other neural networks by achieving an 18* increase in training speed, while maintaining an accuracy above 97% under the write and read noise perturbations. Furthermore, by applying the binarization process with an additional SOT-MRAM dummy module, we demonstrate an on-chip MNIST inference performance close to the ideal baseline using our SOT-PBNN hardware

Molecular Engineering of Potent Sensitizers for Very Efficient Light Harvesting in Thin-Film Solid-State Dye-Sensitized Solar Cells

Dye-sensitized solar cells (DSSCs) have shown significant potential for indoor arid building integrated photovoltaic applications. Herein we present three new D-A-pi-A organic sensitizers, XY1, XY2, and XY3, that exhibit high molar extinction coefficients and a broad absorption range. Molecular modifications of these dyes, featuring a benzothiadiazole (BTZ) auxiliary acceptor, were achieved by introducing a thiophene heterocycle as well as by shifting the, position of BTZ on the conjugated bridge. The ensuing high molar absorption coefficients enabled the fabrication of highly efficient thin-film solid-state DSSCs with only 1.3 mu m mesoporous TiO2 layer. XY2 with a molar extinction coefficient of 6.66 X 10(4) M-1 cm(-1) at 578 nm led to the best photovoltaic performance of 7.51%

Disrupted Resting Frontal–Parietal Attention Network Topology Is Associated With a Clinical Measure in Children With Attention-Deficit/Hyperactivity Disorder

Purpose: Although alterations in resting-state functional connectivity between brain regions have been reported in children with attention-deficit/hyperactivity disorder (ADHD), the spatial organization of these changes remains largely unknown. Here, we studied frontal–parietal attention network topology in children with ADHD, and related topology to a clinical measure of disease progression.Methods: Resting-state fMRI scans were obtained from New York University Child Study Center, including 119 children with ADHD (male n = 89; female n = 30) and 69 typically developing controls (male n = 33; female n = 36). We characterized frontal–parietal functional networks using standard graph analysis (clustering coefficient and shortest path length) and the construction of a minimum spanning tree, a novel approach that allows a unique and unbiased characterization of brain networks.Results: Clustering coefficient and path length in the frontal–parietal attention network were similar in children with ADHD and typically developing controls; however, diameter was greater and leaf number, tree hierarchy, and kappa were lower in children with ADHD, and were significantly correlated with ADHD symptom score. There were significant alterations in nodal eccentricity in children with ADHD, involving prefrontal and occipital cortex regions, which are compatible with the results of previous ADHD studies.Conclusions: Our results indicate the tendency to deviate from a more centralized organization (star-like topology) towards a more decentralized organization (line-like topology) in the frontal–parietal attention network of children with ADHD. This represents a more random network that is associated with impaired global efficiency and network decentralization. These changes appear to reflect clinically relevant phenomena and hold promise as markers of disease progression

Whole-Exome Sequencing Identifies Rare and Low-Frequency Coding Variants Associated with LDL Cholesterol

Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98th or <2nd percentile). Follow-up analyses included sequencing of 1,302 additional individuals and genotype-based analysis of 52,221 individuals. We observed significant evidence of association between LDL-C and the burden of rare or low-frequency variants in PNPLA5, encoding a phospholipase-domain-containing protein, and both known and previously unidentified variants in PCSK9, LDLR and APOB, three known lipid-related genes. The effect sizes for the burden of rare variants for each associated gene were substantially higher than those observed for individual SNPs identified from GWASs. We replicated the PNPLA5 signal in an independent large-scale sequencing study of 2,084 individuals. In conclusion, this large whole-exome-sequencing study for LDL-C identified a gene not known to be implicated in LDL-C and provides unique insight into the design and analysis of similar experiments

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Interval Computing and Information Technology ∗

Interval computing has become a powerful tool in applied research. In this paper, we briefly introduce interval computing, available software, and resent applications especially in information technology. It is a survey with points to technical details. 1 Interval computing R. E. Moore introduced interval computing [23, 24] in the late 1950s. Ever since, it has become an active research branch of scientific computation. In this section, we briefly review what is interval computing. 1.1 Mathematical and machine intervals A nonempty mathematical interval [a, b] is the set {x ∈ℜ|a ≤ x ≤ b} where a ≤ b. To perform interval computing on computers, a mathematical interval should be represented by a machine interval whose endpoints are machine representable numbers. We say that [a ∗,b ∗ ] is a machine representation of [a, b] if[a ∗,b ∗ ] ⊇ [a, b] i.e. a ∗ ≤ a and b ≤ b ∗. We say that the machine interval [a ∗,b ∗]isatight representation of a mathematical interval [a, b] if and only if a ∗ is the greatest machine representable number which is less than or equal to a, and b ∗ is the least machine representable number which is greater than or equal to b. 1.2 Interval arithmetic Interval arithmetic on mathematical intervals is defined as follows. Let a and b be two mathematical intervals. Let op be one of the arithmetic operations +, −, ×, ÷. Then a op b ≡{a op b: a ∈ a,b ∈ b}, provided that 0 � ∈ b if op represents ÷. ∗ This work is partially supported by NSF grants 0202042 1 In other words, the result of adding two intervals a and b is the interval containing the sums of all pairs of numbers from a and b. Table 1 gives explicit implementations of these four basic interval arithmetic operations and other operations on mathematical intervals. We use the notation a =[a, a] and b =[b, b]. Operation Addition a + b [a + b, a + b] Subtraction a − b [a − b, a − b] Multiplication a ∗ b [min{ab,ab, ab, ab}, max{ab,ab, ab, ab}

Parallel Reliable Computing with Interval Arithmetic

Reliability of computational results is crucial in computational science and engineering. In this paper, we report some current research results on parallel reliable computing with interval arithmetic. In section 1, a brief introduction to interval arithmetic is provided. In section 2, an interval algorithm to reliably solving largescale sparse nonlinear systems of equations is presented. In section 3, polynomial interpolation with interval arithmetic is studied. We conclude this paper with section 4. I. Introduction Interval arithmetic, first introduced by Moore [24] in the 1960&apos;s, has become an active research area in scientific computing. Here is the definition of interval arithmetic. Definition 1.1: Let x and y be two real intervals 1 , and op be one of the arithmetic operations +; \Gamma; \Theta, \Xi. Then, x op y = fx op y : x 2 x; y 2 yg, provided that 0 62 y if op represents \Xi. For example, [1; 2] + [\Gamma1; 0] = [0; 2] and [2; 4] \Xi [1; 2] = [1; 4]. Some reasons for..