Single spin asymmetry measurements for π0\pi^0 inclusive productions in p+p↑→π0+Xp+p_{\uparrow} \to \pi^0 + X and \pi^-+\p_{\uparrow}\to \pi^0+X reactions at 70 and 40 GeV respectively

The inclusive $\pi^0$ asymmetries were measured in reactions $p+p\uparrow \to \pi^0+X$ and $\pi^-+p\uparrow \to \pi^0+X$ at 70 and 40 GeV/c respectively. The measurements were made at the central region (for the first reaction) and asymmetry is compatible with zero in the entire measured $p_T$ region. For the second reaction the asymmetry is zero for small $x_F$ region ($-0.4<x_F<-0.1, 0.5<p_T(GeV/c) <1.5$) and increases with growth of $\mid x_F\mid$. Averaged over the interval $-0.8<x_F<-0.4, 1<p_T(GeV/c)<2$ the asymmetry was $-(13.8\pm 3.8)$.Comment: 4 pages, 2 figures; Presented at SPIN-2004 at Trieste, October 10-16,200

Preserving Differential Privacy in Convolutional Deep Belief Networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing epsilon-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions

Quantum Size Effects on the Chemical Sensing Performance of Two-Dimensional Semiconductors

We investigate the role of quantum confinement on the performance of gas sensors based on two-dimensional InAs membranes. Pd-decorated InAs membranes configured as H2 sensors are shown to exhibit strong thickness dependence, with ~100x enhancement in the sensor response as the thickness is reduced from 48 to 8 nm. Through detailed experiments and modeling, the thickness scaling trend is attributed to the quantization of electrons which favorably alters both the position and the transport properties of charge carriers; thus making them more susceptible to surface phenomena

Ultrathin compound semiconductor on insulator layers for high performance nanoscale transistors

Over the past several years, the inherent scaling limitations of electron devices have fueled the exploration of high carrier mobility semiconductors as a Si replacement to further enhance the device performance. In particular, compound semiconductors heterogeneously integrated on Si substrates have been actively studied, combining the high mobility of III-V semiconductors and the well-established, low cost processing of Si technology. This integration, however, presents significant challenges. Conventionally, heteroepitaxial growth of complex multilayers on Si has been explored. Besides complexity, high defect densities and junction leakage currents present limitations in the approach. Motivated by this challenge, here we utilize an epitaxial transfer method for the integration of ultrathin layers of single-crystalline InAs on Si/SiO2 substrates. As a parallel to silicon-on-insulator (SOI) technology14,we use the abbreviation "XOI" to represent our compound semiconductor-on-insulator platform. Through experiments and simulation, the electrical properties of InAs XOI transistors are explored, elucidating the critical role of quantum confinement in the transport properties of ultrathin XOI layers. Importantly, a high quality InAs/dielectric interface is obtained by the use of a novel thermally grown interfacial InAsOx layer (~1 nm thick). The fabricated FETs exhibit an impressive peak transconductance of ~1.6 mS/{\mu}m at VDS=0.5V with ON/OFF current ratio of greater than 10,000 and a subthreshold swing of 107-150 mV/decade for a channel length of ~0.5 {\mu}m

Effects of pretreatments of Napier Grass with deionized water, sulfuric acid and sodium hydroxide on pyrolysis oil characteristics

The depletion of fossil fuel reserves has led to increasing interest in liquid bio-fuel from renewable biomass. Biomass is a complex organic material consisting of different degrees of cellulose, hemicellulose, lignin, extractives and minerals. Some of the mineral elements tend to retard conversions, yield and selectivity during pyrolysis processing. This study is focused on the extraction of mineral retardants from Napier grass using deionized water, dilute sodium hydroxide and sulfuric acid and subsequent pyrolysis in a fixed bed reactor. The raw biomass was characterized before and after each pretreatment following standard procedure. Pyrolysis study was conducted in a fixed bed reactor at 600 o�C, 30 �C/min and 30 mL/min N2 flow. Pyrolysis oil (bio-oil) collected was analyzed using standard analytic techniques. The bio-oil yield and characteristics from each pretreated sample were compared with oil from the non-pretreated sample. Bio-oil yield from the raw sample was 32.06 wt% compared to 38.71, 33.28 and 29.27 wt% oil yield recorded from the sample pretreated with sulfuric acid, deionized water and sodium hydroxide respectively. GC–MS analysis of the oil samples revealed that the oil from all the pretreated biomass had more value added chemicals and less ketones and aldehydes. Pretreatment with neutral solvent generated valuable leachate, showed significant impact on the ash extraction, pyrolysis oil yield, and its composition and therefore can be regarded as more appropriate for thermochemical conversion of Napier grass

High and low levels of an NTRK2-driven genetic profile affect motor- and cognition-associated frontal gray matter in prodromal Huntington’s disease

This study assessed how BDNF (brain-derived neurotrophic factor) and other genes involved in its signaling influence brain structure and clinical functioning in pre-diagnosis Huntington’s disease (HD). Parallel independent component analysis (pICA), a multivariate method for identifying correlated patterns in multimodal datasets, was applied to gray matter concentration (GMC) and genomic data from a sizeable PREDICT-HD prodromal cohort (N = 715). pICA identified a genetic component highlighting NTRK2, which encodes BDNF’s TrkB receptor, that correlated with a GMC component including supplementary motor, precentral/premotor cortex, and other frontal areas (p < 0.001); this association appeared to be driven by participants with high or low levels of the genetic profile. The frontal GMC profile correlated with cognitive and motor variables (Trail Making Test A (p = 0.03); Stroop Color (p = 0.017); Stroop Interference (p = 0.04); Symbol Digit Modalities Test (p = 0.031); Total Motor Score (p = 0.01)). A top-weighted NTRK2 variant (rs2277193) was protectively associated with Trail Making Test B (p = 0.007); greater minor allele numbers were linked to a better performance. These results support the idea of a protective role of NTRK2 in prodromal HD, particularly in individuals with certain genotypes, and suggest that this gene may influence the preservation of frontal gray matter that is important for clinical functioning.This project was supported by 1U01NS082074 (V.C. and J.T., co-principal investigators) from the National Institutes of Health, National Institute of Neurological Disorders and Stroke. The PREDICT-HD study was supported by NIH/NINDS grant 5R01NS040068 awarded to J.P.; CHDI Foundation, Inc., A3917 and 6266 awarded to J.P.; Cognitive and Functional Brain Changes in Preclinical Huntington’s Disease (HD) 5R01NS054893 awarded to J.P.; 4D Shape Analysis for Modeling Spatiotemporal Change Trajectories in Huntington’s 1U01NS082086; Functional Connectivity in Premanifest Huntington’s Disease 1U01NS082083; and Basal Ganglia Shape Analysis and Circuitry in Huntington’s Disease 1U01NS082085 awarded to Christopher A. Ross

Development of an app for processing data on wildlife density in the field

It is essential to provide tools to wildlife professionals and researchers in order to facilitate data collection on wildlife density estimation following standardized protocols in the field. This is relevant for efficient harmonized data management systems, from the field to final reporting. Our main objective was to facilitate the collection of information in the field using established density estimation protocols. The specific objectives were (i) to evaluate and use already existing data registration IT tools for collecting and storing the data in the field; (ii) to make these data available in real time (cloud-based solution), and (iii) being flexible enough to incorporate new protocols and species, as methods (such as camera trap-based) and needs continuously evolves. We improved an already existing tool, Spatial Monitoring and Reporting Tool (SMART; https://smartconservationtools.org/). It is an open source software, which allows easily collect, visualize, store, analyze, report and act on a wide range of field data relevant for wildlife monitoring. The integration of SMART tools on EOW was successfully done for (i) distance sampling, (ii) hunting data and (iii) camera trap protocols. ENETWILD, therefore, made now available new IT functionalities to wildlife professionals and researchers to facilitate and harmonize wildlife data collection systems.EFSA-Q-2022-00044Peer reviewe