Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel method reveals that solvent water favors polyproline II over β-strand conformation in peptides and unfolded proteins : Conditional Hydrophobic Accessible Surface Area ( CHASA ) *

In aqueous solution, the ensemble of conformations sampled by peptides and unfolded proteins is largely determined by their interaction with water. It has been a long-standing goal to capture these solute-water energetics accurately and efficiently in calculations. Historically, accessible surface area (ASA) has been used to estimate these energies, but this method breaks down when applied to amphipathic peptides and proteins. Here we introduce a novel method in which hydrophobic ASA is determined after first positioning water oxygens in hydrogen-bonded orientations proximate to all accessible peptide/protein backbone N and O atoms. This conditional hydrophobic accessible surface area is termed CHASA. The CHASA method was validated by predicting the P II and β-strand conformational preferences of non-proline residues in the coil library (i.e. non-α-helix, non-β-strand, non-β-turn library derived from X-ray elucidated structures). Further, the method successfully rationalizes the previously unexplained solvation energies in polyalanyl peptides and compares favorably with published experimentally-determined P II residue propensities. Of the many factors that determine the conformation of polypeptides, the interaction with water is one of the most important. Intrapeptide backbone interactions limit available conformational space (Pappu and Rose 2002) and sidechain interactions specify conformational preferences isolation. Rather, such effects act in concert with solvation preferences, which are measured by the free energy of interaction between peptides and water (Luo and Baldwin 1999; Thomas et al. 2001). It is often assumed, usually implicitly, that protein backbone solvation is uniform in the unfolded state. However, computational studies indicate that the solvation energetics differ among Indeed, this method has even been used as a potential function in protein simulations (Ferrara et al. 2002; Rathore et al. 2003). Unfortunately, the ASA approximation breaks down eventually (Avbelj et al. 2000; Gallicchio et al. 2000; Mezei et al. 2004) because typical ASA calculations assume the entire surface in question is available for solvation simultaneously, and in an undifferentiated way, with no distinction made between one accessible site and another. This is a particularly poor assumption for a chemically heterogeneous moiety like a peptide, which has both polar and apolar sites in close proximity. Given that interactions between water and polar sites are much stronger than the corresponding interactions between water and apolar sites, the Boltzmann-weighted distribution Fleming et al, page 4 of water-peptide interactions favors solvation of N-H and C=O groups over apolar groups. The residence times of water molecules at polar solvation sites are known to be 2 to …
Fashion IQ: A New Dataset towards Retrieving Images by Natural Language Feedback

We contribute a new dataset and a novel method for natural language based fashion image retrieval. Unlike previous fashion datasets, we provide natural language annotations to facilitate the training of interactive image retrieval systems, as well as the commonly used attribute based labels. We propose a novel approach and empirically demonstrate that combining natural language feedback with visual attribute information results in superior user feedback modeling and retrieval performance relative to using either of these modalities. We believe that our dataset can encourage further work on developing more natural and real-world applicable conversational shopping assistants.
1 3 D Imaging of Nanomaterials by Discrete Tomography

The field of discrete tomography focuses on the reconstruction of samples that consist of only a few different materials. Ideally, a 3D reconstruction of such a sample should contain only one grey level for each of the compositions in the sample. By exploiting this property in the reconstruction algorithm, either the quality of the reconstruction can be improved significantly, or the number of required projection images can be reduced. The discrete reconstruction typically contains fewer artifacts and does not have to be segmented, as it already contains one grey level for each composition. Recently, a new algorithm, called DART, has been proposed that can be used effectively on experimental electron tomography datasets. In this paper, we propose discrete tomography as a general reconstruction method for electron tomography in materials science. We describe the basic principles of DART and show that it can be applied successfully to three different types of samples, consisting of embedded ErSi2 nanocrystals, a carbon nanotube grown from a catalyst particle, and a single gold nanoparticle, respectively.
Regional to Global Biogenic Isoprene Emission Responses to Changes in Vegetation From 2000 to 2015

Isoprene, a dominant biogenic volatile organic compound that is mainly emitted by trees, has a significant impact on the atmospheric chemistry. Regional to global changes in biogenic isoprene emission associated with vegetation variations between 2000 and 2015 were estimated using the MEGAN model with satellite land cover data for inputs in this study. The satellite data estimates of land cover changes were compared to results from previous investigators that have either conducted regional studies or have used lower resolution land cover data. The analysis indicates that tree coverage increases of >5% occurred in 13% of locations including in central China and Europe. In contrast, a decrease of >5% was observed in about 5% of locations, especially in tropical regions. The trends in global tree coverage from 2000 to 2015 resulted in a global isoprene emission decrease of only 1.5%, but there were significant regional variations. Obvious decreases in tree coverage in some tropical areas (e.g., Amazon Basin, Western Africa, Southeast Asia) resulted in a ~10% reduction of regional isoprene emission due to agricultural expansion. Distinct increments of isoprene emission (5–10%) were mainly found in Northeast China and India and were associated with afforestation efforts. Deforestation and afforestation associated with managed plantations does not only affect the total forest coverage but also impacts average isoprene emission capacity, which can result in accelerated isoprene emission variations. Consequently, isoprene variation assessments are needed that not only account for changes in vegetation fractions but also consider the changes in plant species compositions of forests and other landscapes.
GSDA: A Generative Adversarial Network-based Semi-Supervised Data Augmentation Method

Background : Medical Ultrasound (US) is one of the most widely used imaging modalities in clinical practice. However, its use presents unique challenges such as variable imaging quality. The deep learning (DL) model can be used as an advanced medical US image analysis tool, while the scarcity of big datasets greatly limits its performance. To solve the common data shortage, we develop a Generative Adversarial Network (GAN)-based semi-supervised data augmentation method GSDA. Methods : The GSDA is composed of the GAN and Convolutional Neural Network (CNN), in which GAN synthesizes and pseudo-labeled the US images with high resolution and high quality, and both real and synthesized images are employed to train CNN. To overcome the training difﬁculty for GAN and CNN under the small data regime, we employ the transfer learning technique for both of them. We also pro-pose a novel evaluation standard to balance the classiﬁcation accuracy and the time consumption. We evaluate our method on the BUSI dataset and GSDA outperforms existing state-of-the-art methods. Results : With high-resolution and high-quality images synthesized, GSDA obtain a 97.9% accuracy using merely 780 images. With the promising results, we believe GSDA can be regarded as a potential auxiliary tool for medical US analysis.
Drivers of yield and nitrogen-loss tradeoffs: Cropping system evaluations with process-based modeling

The Midwest is one of the most productive agricultural regions, but mitigating loss of nitrogen (N) from cropland is needed to improve environmental quality. Tradeoffs between crop yield and N loss have been linked largely to the inefficient use of N fertilizers, but the contributions of more systemic factors such as soil characteristics, crop sequences and genotypes have not been thoroughly studied. This dissertation examines and quantifies the impact of various genetic, environmental and management drivers of crop yield and N-loss tradeoffs in the maize and soybean cropping systems of the US Midwest, and identifies potential management strategies to lessen these tradeoffs. To this end, a system analysis framework was employed, which used field data from small plots, long-term experiments, publicly available databases, and process-based modeling. The approach allowed for full exploration of the soil-plant-atmosphere continuum and extrapolation of the behavior of cropping systems across a wide range of weather, soil, and management. Findings from these studies indicate the prominent role of crop sequences and residue dynamics in driving tradeoffs. In maize-soybean systems, it was estimated that a majority (55%) of N losses originated from the release of native soil N into the environment due to asynchrony between soil mineralization and crop uptake. Including a rye cover crop in rotations was shown to be an effective way of improving soil N retention and reducing losses, while seldom resulting in yield tradeoffs. However, the most effective strategies also required simultaneously choosing appropriate genotypes, timely planting, and optimizing N inputs to better match crop requirements. Research also aimed to advance knowledge and modeling of various crop-soil processes
CNSBench: A Cloud Native Storage Benchmark

Modern hybrid cloud infrastructures require software to be easily portable between heterogeneous clusters. Application containerization is a proven technology to provide this portability for the functionalities of an application. However, to ensure performance portability, dependable verification of a cluster’s performance under realistic workloads is required. Such verification is usually achieved through benchmarking the target environment and its storage in particular, as I/O is often the slowest component in an application. Alas, existing storage benchmarks are not suitable to generate cloud native workloads as they do not generate any storage control operations (e.g., volume or snapshot creation), cannot easily orchestrate a high number of simultaneously running distinct workloads, and are limited in their ability to dynamically change workload characteristics during a run. In this paper, we present the design and prototype for the first-ever Cloud Native Storage Benchmark—CNSBench. CNSBench treats control operations as first-class citizens and allows to easily combine traditional storage benchmark workloads with user-defined control operation workloads. As CNSBench is a cloud native application itself, it natively supports orchestration of different control and I/O workload combinations at scale. We built a prototype of CNSBench for Kubernetes, leveraging several existing containerized storage benchmarks for data and metadata I/O generation. We demonstrate CNSBench’s usefulness with case studies of Ceph and OpenEBS, two popular storage providers for Kubernetes, uncovering and analyzing previously unknown performance characteristics.
Improving Permafrost Modeling by Assimilating Remotely Sensed Soil Moisture

Knowledge of soil moisture conditions is important for modeling soil temperatures, as soil moisture influences the thermal dynamics in multiple ways. However, in permafrost regions, soil moisture is highly heterogeneous and difficult to model. Satellite soil moisture data may fill this gap, but the degree to which they can improve permafrost modeling is unknown. To explore their added value for modeling soil temperatures, we assimilate fine‐scale satellite surface soil moisture into the CryoGrid‐3 permafrost model, which accounts for the soil moisture's influence on the soil thermal properties and the surface energy balance. At our study site in the Canadian Arctic, the assimilation improves the estimates of deeper (>10 cm) soil temperatures during summer but not consistently those of the near‐surface temperatures. The improvements in the deeper temperatures are strongly contingent on soil type: They are largest for porous organic soils (30%), smaller for thin organic soil covers (20%), and they essentially vanish for mineral soils (only synthetic data available). That the improvements are greatest over organic soils reflects the strong coupling between soil moisture and deeper temperatures. The coupling arises largely from the diminishing soil thermal conductivity with increasing desiccation thanks to which the deeper soil is kept cool. It is this association of dry organic soils being cool at depth that lets the assimilation revise the simulated soil temperatures toward the actually measured ones. In the future, the increasing availability of satellite soil moisture data holds promise for the operational monitoring of soil temperatures, hydrology, and biogeochemistry.
Adaptive Signal Processing: Next Generation Solutions

Preface. Contributors. Chapter 1 Complex-Valued Adaptive Signal Processing. 1.1 Introduction. 1.2 Preliminaries. 1.3 Optimization in the Complex Domain. 1.4 Widely Linear Adaptive Filtering. 1.5 Nonlinear Adaptive Filtering with Multilayer Perceptrons. 1.6 Complex Independent Component Analysis. 1.7 Summary. 1.8 Acknowledgment. 1.9 Problems. References. Chapter 2 Robust Estimation Techniques for Complex-Valued Random Vectors. 2.1 Introduction. 2.2 Statistical Characterization of Complex Random Vectors. 2.3 Complex Elliptically Symmetric (CES) Distributions. 2.4 Tools to Compare Estimators. 2.5 Scatter and Pseudo-Scatter Matrices. 2.6 Array Processing Examples. 2.7 MVDR Beamformers Based on M -Estimators. 2.8 Robust ICA. 2.9 Conclusion. 2.10 Problems. References. Chapter 3 Turbo Equalization. 3.1 Introduction. 3.2 Context. 3.3 Communication Chain. 3.4 Turbo Decoder: Overview. 3.5 Forward-Backward Algorithm. 3.6 Simplified Algorithm: Interference Canceler. 3.7 Capacity Analysis. 3.8 Blind Turbo Equalization. 3.9 Convergence. 3.10 Multichannel and Multiuser Settings. 3.11 Concluding Remarks. 3.12 Problems. References. Chapter 4 Subspace Tracking for Signal Processing. 4.1 Introduction. 4.2 Linear Algebra Review. 4.3 Observation Model and Problem Statement. 4.4 Preliminary Example: Oja s Neuron. 4.5 Subspace Tracking. 4.6 Eigenvectors Tracking. 4.7 Convergence and Performance Analysis Issues. 4.8 Illustrative Examples. 4.9 Concluding Remarks. 4.10 Problems. References. Chapter 5 Particle Filtering. 5.1 Introduction. 5.2 Motivation for Use of Particle Filtering. 5.3 The Basic Idea. 5.4 The Choice of Proposal Distribution and Resampling. 5.5 Some Particle Filtering Methods. 5.6 Handling Constant Parameters. 5.7 Rao Blackwellization. 5.8 Prediction. 5.9 Smoothing. 5.10 Convergence Issues. 5.11 Computational Issues and Hardware Implementation. 5.12 Acknowledgments. 5.13 Exercises. References. Chapter 6 Nonlinear Sequential State Estimation for Solving Pattern-Classification Problems. 6.1 Introduction. 6.2 Back-Propagation and Support Vector Machine-Learning Algorithms: Review. 6.3 Supervised Training Framework of MLPs Using Nonlinear Sequential State Estimation. 6.4 The Extended Kalman Filter. 6.5 Experimental Comparison of the Extended Kalman Filtering Algorithm with the Back-Propagation and Support Vector Machine Learning Algorithms. 6.6 Concluding Remarks. 6.7 Problems. References. Chapter 7 Bandwidth Extension of Telephony Speech. 7.1 Introduction. 7.2 Organization of the Chapter. 7.3 Nonmodel-Based Algorithms for Bandwidth Extension. 7.4 Basics. 7.5 Model-Based Algorithms for Bandwidth Extension. 7.6 Evaluation of Bandwidth Extension Algorithms. 7.7 Conclusion. 7.8 Problems. References. Index.
Deep In-Memory Architectures for Machine Learning-Accuracy

—In-memory architectures, in particular, the deep in-memory architecture (DIMA) has emerged as an attractive alternative to the traditional von Neumann (digital) architecture for realizing energy and latency-efﬁcient machine learning systems in silicon. Multiple DIMA integrated circuit (IC) prototypes have demonstrated energy-delay product (EDP) gains of up to 100 × over a digital architecture. These EDP gains were achieved minimal or sometimes no loss in decision-making accuracy which is surprising given its intrinsic analog mixed-signal nature. This paper establishes models and methods to understand the fundamental energy-delay and accuracy trade-offs underlying DIMA by: 1) presenting silicon-validated energy, delay, and accuracy models; and 2) employing these to quantify DIMA’s decision-level accuracy and to identify the most effective design parameters to maximize its EDP gains at a given level of accuracy. For example, it is shown that: 1) DIMA has the potential to realize between 21 × -to- 1365 × gains; 2) its energy-per-decision is approximately 10 × lower at the same decision-making accuracy under most conditions; 3) its accuracy can always be improved by increasing the input vector dimension and/or by increasing the bitline swing; and 4) unlike the digital architecture, there are quantiﬁable conditions under which DIMA’s accuracy is fundamentally limited due to noise.
of the Association for Information Systems

: In this paper, we examine the evolution of the institutional and intellectual structures of the IS field. We argue that, though the field’s institutional structures—academic programs, journals, conferences, and professional associations— have developed admirably, the state of the field’s intellectual structure is less clear. We employ a co-citation lens to analyze the development and evolution of subfields across three periods. We rely on Culnan’s (1987) second co-citation study as a point of departure for our analysis. We then extend her work through two additional studies that individually assess the state of subfield development at distinct periods during the field’s history. Over the three periods, we note that the field has experienced change in subfield diversity and cohesion. Culnan’s study exhibits low levels of cohesion and diversity among topics. Our first study shows continued isolation but growth in subfield diversity. This period is indicative of a fragmented adhocracy. Our second study suggests increasing levels of integration despite only a slight reduction in subfield diversity. While we largely only describe the field’s evolution, any assessment of whether this evolution represents a positive or negative trajectory for the field will be subject to interpretation and debate.
Phylogenetic Inference using RevBayes Substitution Models

1 Overview This tutorial demonstrates some general principles of Bayesian model comparison, which is based on estimating the marginal likelihood of competing models and then comparing their relative fit to the data using Bayes factors. We consider the specific case of calculating Bayes factors to select among different substitution models. 1.1 Requirements We assume that you have read and hopefully completed the following tutorials:
A Compact Microstrip Patch Antenna for LTE Applications

A compact multiband antennas for Long Term Evolution (LTE) applications is a challenge. Both the frequencies of new wireless technologies and new frequency bands must be covered. The lower end of the 0.7- 3.5 GHz band is especially difficult to handle for miniaturized terminal devices. A single layer, line-feed rectangular microstrip patch antenna is small enough for the LTE handsets. Our project proposes size reduction and bandwidth enhancement through adapted feeding techniques. By means of slits the return loss and gain can be optimized with the aid of HFSS (High Frequency Structure Simulator).
Management of Medical Website Quality Labels via Web Mining

The World Wide Web is an important channel of information exchange in many domains, including the medical one. The ever increasing amount of freely available healthcare-related information generates, on the one hand, excellent conditions for self-education of patients as well as physicians, but on the other hand, entails substantial risks if such information is trusted irrespective of low
Event-based Gesture Recognition with Dynamic Background Suppression using Smartphone Computational Capabilities

This paper introduces a framework of gesture recognition operating on the output of an event based camera using the computational resources of a mobile phone. We will introduce a new development around the concept of time-surfaces modified and adapted to run on the limited computational resources of a mobile platform. We also introduce a new method to remove dynamically backgrounds that makes full use of the high temporal resolution of event-based cameras. We assess the performances of the framework by operating on several dynamic scenarios in uncontrolled lighting conditions indoors and outdoors. We also introduce a new publicly available eventbased dataset for gesture recognition selected through a clinical process to allow human-machine interactions for the visually-impaired and the elderly. We finally report comparisons with prior works that tackled event-based gesture recognition reporting comparable if not superior results if taking into account the limited computational and memory constraints of the used hardware.
This paper is included in the Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation.

Internet Service Providers use routers from multiple vendors that support standardized routing protocols. Network operators deploy new services by tuning these protocols. Unfortunately, while standardization is necessary for interoperability, this is a slow process. As a consequence, new features appear very slowly in routing protocols. We propose a new implementation model for BGP, called x BGP, that enables ISPs to innovate by easily deploying BGP extensions in their multivendor network. We define a vendor-neutral x BGP API which can be supported by any BGP implementation and an eBPF Virtual Machine that allows executing extension code within these BGP implementations. We demonstrate the feasibility of our approach by extending both FRRouting and BIRD. We demonstrate seven different use cases showing the benefits that network operators can obtain using x BGP programs. We propose a verification toolchain that enables operators to compile and verify the safety properties of x BGP programs before deploying them. Our testbed measurements show that the performance impact of x BGP is reasonable compared to native code.
SParseval: Evaluation Metrics for Parsing Speech

While both spoken and written language processing stand to beneﬁt from parsing, the standard Parseval metrics (Black et al., 1991) and their canonical implementation (Sekine and Collins, 1997) are only useful for text. The Parseval metrics are undeﬁned when the words input to the parser do not match the words in the gold standard parse tree exactly, and word errors are unavoidable with automatic speech recognition (ASR) systems. To ﬁll this gap, we have developed a publicly available tool for scoring parses that implements a variety of metrics which can handle mismatches in words and segmentations, including: alignment-based bracket evaluation, alignment-based dependency evaluation, and a dependency evaluation that does not require alignment. We describe the different metrics, how to use the tool, and the outcome of an extensive set of experiments on the sensitivity of the metrics.