nils-herrmann/clean_scirepeval huggingface dataset:
Mikhail Gerasimenko Intelligent Resource Allocation in 5 G Multi-Radio Heterogeneous Networks

The fast-moving evolution of wireless networks, which started less than three decades ago, has resulted in worldwide connectivity and influenced the development of a global market in all related areas. However, in recent years, the growing user traffic demands have led to the saturation of licensed and unlicensed frequency bands regarding capacity and load-over-time. On the physical layer the used spectrum efficiency is already close to Shannon’s limit; however the traffic demand continues to grow, forcing mobile network operators and equipment manufacturers to evaluate more effective strategies of the wireless medium access. One of these strategies, called cell densification, implies there are a growing number of serving entities, with the appropriate reduction of the per-cell coverage area. However, if implemented blindly, this approach will lead to a significant growth in the average interference level and overhead control signaling, which are both required to allow sufficient user mobility. Furthermore, the interference is also affected by the increasing variety of radio access technologies (RATs) and applications, often deployed without the necessary level of cooperation with technologies that are already in place. To overcome these problems today’s telecommunication standardization groups are trying to collaborate. That is why the recent agenda of the fifth generation wireless networks (5G) includes not only the development schedules for the particular technologies but also implies there should be an expansion of the appropriate interconnection techniques. In this thesis, we describe and evaluate the concept of heterogeneous networks (HetNets), which involve the cooperation between several RATs. In the introductory part, we discuss the set of the problems, related to HetNets, and review the HetNet development process. Moreover, we show the evolution of existing and potential segments of the multi-RAT 5G network, together with the most promising applications, which could be used in future HetNets. Further, in the thesis, we describe the set of key representative scenarios, including three-tier WiFi-LTE multi-RAT deployment, MTC-enabled LTE, and the mmWave-based network. For each of these scenarios, we define a set of unsolved issues and appropriate solutions. For the WiFi-LTE multi-RAT scenario, we develop the framework, enabling intelligent and flexible resource allocation between the involved RATs. For MTC-enabled LTE, we study the effect of massive MTC deployments on the performance of LTE random access procedure and propose some basic methods to improve its efficiency. Finally, for the mmWave scenario, we study the effects of connectivity strategies, human body blockage and antenna array configuration on the overall network performance. Next, we develop a set of validated analytical and simulation-based techniques which allow us to evaluate the performance of proposed solutions. At the end of the introductory part a set of HetNet-related demo activities is demonstrated.
Probabilistic Deep Spiking Neural Systems Enabled by Magnetic Tunnel Junction

Deep spiking neural networks are becoming increasingly powerful tools for cognitive computing platforms. However, most of the existing studies on such computing models are developed with limited insights on the underlying hardware implementation, resulting in area and power expensive designs. Although several neuromimetic devices emulating neural operations have been proposed recently, their functionality has been limited to very simple neural models that may prove to be inefficient at complex recognition tasks. In this paper, we venture into the relatively unexplored area of utilizing the inherent device stochasticity of such neuromimetic devices to model complex neural functionalities in a probabilistic framework in the time domain. We consider the implementation of a deep spiking neural network capable of performing high-accuracy and lowlatency classification tasks, where the neural computing unit is enabled by the stochastic switching behavior of a magnetic tunnel junction. The simulation studies indicate an energy improvement of 20× over a baseline CMOS design in 45-nm technology.
Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint

The paper considers a stylized model of a dynamic assortment optimization problem, where given a limited capacity constraint, we must decide the assortment of products to offer to customers to maximize the profit. Our model is motivated by the problem faced by retailers of stocking products on a shelf with limited capacities and by the problem of placing a limited number of ads on a web page. We assume that each customer chooses to purchase the product (or to click on the ad) that maximizes her utility. We use the multinomial logit choice model to represent demand. However, we do not know the demand for each product. We can learn the demand distribution by offering different product assortments, observing resulting selections, and inferring the demand distribution from past selections and assortment decisions. We present an adaptive policy for joint parameter estimation and assortment optimization. To evaluate our proposed policy, we define a benchmark profit as the maximum expected profit that we can earn if we know the underlying demand distribution in advance. We show that the running average expected profit generated by our policy converges to the benchmark profit and establish its convergence rate. Numerical experiments based on sales data from an online retailer indicate that our policy performs well, generating over 90% of the optimal profit after less than two days of sales. 1.  Companies have realized the importance of offering products that are tailored to the demand of customers in each region. For instance, Wal-mart stocks specific lines of clothes targeted exclusively to certain groups of customers (Zimmerman (2006)). Car manufacturers are well-known for ∗School of Operations Research and Information Engineering, Cornell University, Ithaca, NY 14853, USA. E-mail: paatrus@cornell.edu †Department of Industrial Engineering and Operations Research, University of California–Berkeley, 4129 Etcheverry Hall, Berkeley, CA 94720, USA. E-mail: shen@ieor.berkeley.edu ‡School of Operations Research and Information Engineering and Department of Computer Science, Cornell University, Ithaca, NY 14853, USA. E-mail: shmoys@cs.cornell.edu
The cytosolic thiouridylase CTU2 of Arabidopsis thaliana is essential for posttranscriptional thiolation of tRNAs and influences root development

A large number of post-transcriptional modifications of transfer RNAs (tRNAs) have been described in prokaryotes and eukaryotes. They are known to influence their stability, turnover, and chemical/physical properties. A specific subset of tRNAs contains a thiolated uridine residue at the wobble position to improve the codon-anticodon interaction and translational accuracy. The proteins involved in tRNA thiolation are reminiscent of prokaryotic sulfur transfer reactions and of the ubiquitylation process in eukaryotes. In plants, some of the proteins involved in this process have been identified and show a high degree of homology to their non-plant equivalents. For other proteins, the identification of the plant homologs is much less clear, due to the low conservation in protein sequence. This manuscript describes the identification of CTU2, the second CYTOPLASMIC THIOURIDYLASE protein of Arabidopsis thaliana. CTU2 is essential for tRNA thiolation and interacts with ROL5, the previously identified CTU1 homolog of Arabidopsis. CTU2 is ubiquitously expressed, yet its activity seems to be particularly important in root tissue. A ctu2 knock-out mutant shows an alteration in root development. The analysis of CTU2 adds a new component to the so far characterized protein network involved in tRNA thiolation in Arabidopsis. CTU2 is essential for tRNA thiolation as a ctu2 mutant fails to perform this tRNA modification. The identified Arabidopsis CTU2 is the first CTU2-type protein from plants to be experimentally verified, which is important considering the limited conservation of these proteins between plant and non-plant species. Based on the Arabidopsis protein sequence, CTU2-type proteins of other plant species can now be readily identified.
Hybrid Mobile Edge Computing: Unleashing the Full Potential of Edge Computing in Mobile Device Use Cases

Many different technologies fostering and supporting distributed and decentralized computing scenarios emerged recently. Edge computing provides the necessary on-demand computing power for Internet-of-Things (IoT) devices where it is needed. Computing power is moved closer to the consumer, with the effect of reducing latency and increasing fail-safety due to absent centralized structures. This is an enabler for applications requiring high-bandwidth uplinks and low latencies to computing units. In this paper, a new use case for edge computing is identified. Mobile devices can overcome their battery limitations and performance constraints by dynamically using the edge-computing-provided computational power. We call this new technology Hybrid Mobile Edge Computing. We present a general architecture and framework, which targets the mobile device use case of hybrid mobile edge computing, not only considering the improvement of performance and energy consumption, but also providing means to protect user privacy, sensitive data and computations. The achieved results are backed by the results of our analysis, targeting the energy saving potentials and possible performance improvements.
Neural Sign Language Translation

Sign Language Recognition (SLR) has been an active research field for the last two decades. However, most research to date has considered SLR as a naive gesture recognition problem. SLR seeks to recognize a sequence of continuous signs but neglects the underlying rich grammatical and linguistic structures of sign language that differ from spoken language. In contrast, we introduce the Sign Language Translation (SLT) problem. Here, the objective is to generate spoken language translations from sign language videos, taking into account the different word orders and grammar. We formalize SLT in the framework of Neural Machine Translation (NMT) for both end-to-end and pretrained settings (using expert knowledge). This allows us to jointly learn the spatial representations, the underlying language model, and the mapping between sign and spoken language. To evaluate the performance of Neural SLT, we collected the first publicly available Continuous SLT dataset, RWTH-PHOENIX-Weather 2014T1. It provides spoken language translations and gloss level annotations for German Sign Language videos of weather broadcasts. Our dataset contains over .95M frames with >67K signs from a sign vocabulary of >1K and >99K words from a German vocabulary of >2.8K. We report quantitative and qualitative results for various SLT setups to underpin future research in this newly established field. The upper bound for translation performance is calculated at 19.26 BLEU-4, while our end-to-end frame-level and gloss-level tokenization networks were able to achieve 9.58 and 18.13 respectively.
Trimethoprim-associated hyponatremia.

Hyponatremia associated with diuretic use can be clinically difficult to differentiate from the syndrome of inappropriate antidiuretic hormone secretion (SIADH). We report a case of a 28-year-old man with HIV (human immunodeficiency virus) and Pneumocystis pneumonia who developed hyponatremia while receiving trimethoprim-sulfamethoxazole (TMP/SMX). Serum sodium level on admission was 135 mEq/L (with a history of hyponatremia) and decreased to 117 mEq/L by day 7 of TMP/SMX treatment. In the setting of suspected euvolemia and Pneumocystis pneumonia, he was treated initially for SIADH with fluid restriction and tolvaptan without improvement in serum sodium level. A diagnosis of hyponatremia secondary to the diuretic effect of TMP subsequently was confirmed, with clinical hypovolemia and high renin, aldosterone, and urinary sodium levels. Subsequent therapy with sodium chloride stabilized serum sodium levels in the 126- to 129-mEq/L range. After discontinuation of TMP/SMX treatment, serum sodium, renin, and aldosterone levels normalized. TMP/SMX-related hyponatremia likely is underdiagnosed and often mistaken for SIADH. It should be considered for patients on high-dose TMP/SMX treatment and can be differentiated from SIADH by clinical hypovolemia (confirmed by high renin and aldosterone levels). TMP-associated hyponatremia can be treated with sodium supplementation to offset ongoing urinary losses if the TMP/SMX therapy cannot be discontinued. In this Acid-Base and Electrolyte Teaching Case, a less common cause of hyponatremia is presented, and a stepwise approach to the diagnosis is illustrated.
Molecular dynamics of the diatom thylakoid membrane under different light conditions

During the last years significant progress was achieved in unraveling molecular characteristics of the thylakoid membrane of different diatoms. With the present review it is intended to summarize the current knowledge about the structural and functional changes within the thylakoid membrane of diatoms acclimated to different light conditions. This aspect is addressed on the level of the organization and regulation of light-harvesting proteins, the dissipation of excessively absorbed light energy by the process of non-photochemical quenching, and the lipid composition of diatom thylakoid membranes. Finally, a working hypothesis of the domain formation of the diatom thylakoid membrane is presented to highlight the most prominent differences of heterokontic thylakoids in comparison to vascular plants and green algae during the acclimation to low and high light conditions.
A Theory-Driven Design Framework for Social Recommender Systems

Ofer Arazy University of Alberta ofer.arazy@ualberta.ca Nanda Kumar City University of New York Nanda.Kumar@baruch.cuny.edu Bracha Shapira Ben-Gurion University of the Negev bshapira@bgumail.bgu.ac.il Social recommender systems utilize data regarding users’ social relationships in filtering relevant information to users. To date, results show that incorporating social relationship data – beyond consumption profile similarity – is beneficial only in a very limited set of cases. The main conjecture of this study is that the inconclusive results are, at least to some extent, due to an under-specification of the nature of the social relations. To date, there exist no clear guidelines for using behavioral theory to guide systems design. Our primary objective is to propose a methodology for theory-driven design. We enhance Walls et al.’s (1992) IS Design Theory by introducing the notion of “applied behavioral theory,” as a means of better linking theory and system design. Our second objective is to apply our theory-driven design methodology to social recommender systems, with the aim of improving prediction accuracy. A behavioral study found that some social relationships (e.g., competence, benevolence) are most likely to affect a recipient’s advice-taking decision. We designed, developed, and tested a recommender system based on these principles, and found that the same types of relationships yield the best recommendation accuracy. This striking correspondence highlights the importance of behavioral theory in guiding system design. We discuss implications for design science and for research on recommender systems.
An FPGA-based queue management system for high speed networking devices

One of the main bottlenecks when designing a network system is very often its memory subsystem. This is mainly due to the extremely high speed of the state-of-the-art network links and to the fact that in order to support advanced Quality of Service (QoS), a large number of independent queues is desirable. In this paper we describe the architecture and performance of a memory manager, the QMS that is tailored to FPGA technology and can provide up to 6.2 Gbps of aggregate throughput, while handling 32 K independent queues. The presented system supports a complete instruction set and thus we believe it can be used as a hardware component in any suitable networking system. It also supports a large number of different interfaces, and it is designed in a very scalable way. The QMS uses a double data rate DRAM for data storage and a typical SRAM for keeping data structures-pointers, therefore minimizing the system’s cost. In order to deal with the problems of refreshing and bank conflicts in the DRAM, several optimization techniques have been employed. In this paper we also present the architecture of a network-processing device that fully utilizes the advanced feature of the QMS. The QMS consists of 8500 Slices in a XILINX FPGA and works at 125 MHz. q 2004 Elsevier B.V. All rights reserved.
Wavelet-based Machine Learning Techniques for ECG Signal Analysis

Machine learning of ECG is a core component in any of the ECG-based healthcare informatics system. Since the ECG is a nonlinear signal, the subtle changes in its amplitude and duration are not well manifested in time and frequency domains. Therefore, in this chapter, we introduce a machine-learning approach to screen arrhythmia from normal sinus rhythm from the ECG. The methodology consists of R-point detection using the Pan-Tompkins algorithm, discrete wavelet transform (DWT) decomposition, sub-band principal component analysis (PCA), statistical validation of features, and subsequent pattern classification. The k-fold cross validation is used in order to reduce the bias in choosing training and testing sets for classification. The average accuracy of classification is used as a benchmark for comparison. Different classifiers used are Gaussian mixture model (GMM), error back propagation neural network (EBPNN), and support vector machine (SVM). The DWT basis functions used are Daubechies-4, Daubechies-6, Daubechies-8, Symlet-2, Symlet-4, Symlet-6, Symlet-8, Coiflet-2, and Coiflet-5. An attempt is made to exploit the energy compaction in the wavelet sub-bands to yield higher classification accuracy. Results indicate that the Symlet2 wavelet basis function provides the highest accuracy in classification. Among the classifiers, SVM yields the highest classification accuracy, whereas EBPNN yields a higher accuracy than GMM. The use of other time frequency representations using different time frequency kernels as a future direction is also observed. The developed machine-learning approach can be used in a web-based telemedicine system, which can be used in remote monitoring of patients in many healthcare informatics systems. R. J. Martis (&) C. Chakraborty School of Medical Science and Technology, IIT, Kharagpur, India e-mail: roshaniitsmst@gmail.com A. K. Ray Department of Electronics and Electrical Communication Engineering, IIT, Kharagpur, India S. Dua et al. (eds.), Machine Learning in Healthcare Informatics, Intelligent Systems Reference Library 56, 9_2,
O-GlcNAc transferase inhibitors: current tools and future challenges.

The O-linked N-acetylglucosamine (O-GlcNAc) post-translational modification (O-GlcNAcylation) is the dynamic and reversible attachment of N-acetylglucosamine to serine and threonine residues of nucleocytoplasmic target proteins. It is abundant in metazoa, involving hundreds of proteins linked to a plethora of biological functions with implications in human diseases. The process is catalysed by two enzymes: O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA) that add and remove sugar moieties respectively. OGT knockout is embryonic lethal in a range of animal models, hampering the study of the biological role of O-GlcNAc and the dissection of catalytic compared with non-catalytic roles of OGT. Therefore, selective and potent chemical tools are necessary to inhibit OGT activity in the context of biological systems. The present review focuses on the available OGT inhibitors and summarizes advantages, limitations and future challenges.
Rational development of guidelines for management of neonatal sepsis in developing countries.

This review discusses the rational development of guidelines for the management of neonatal sepsis in developing countries.



Diagnosis of neonatal sepsis with high specificity remains challenging in developing countries. Aetiology data, particularly from rural, community-based studies, are very limited, but molecular tests to improve diagnostics are being tested in a community-based study in South Asia. Antibiotic susceptibility data are limited, but suggest reducing susceptibility to first-and second-line antibiotics in both hospital and community-acquired neonatal sepsis. Results of clinical trials in South Asia and sub-Saharan Africa assessing feasibility of simplified antibiotic regimens are awaited.



Effective management of neonatal sepsis in developing countries is essential to reduce neonatal mortality and morbidity. Simplified antibiotic regimens are currently being examined in clinical trials, but reduced antimicrobial susceptibility threatens current empiric treatment strategies. Improved clinical and microbiological surveillance is essential, to inform current practice, treatment guidelines, and monitor implementation of policy changes.
Can we steal your vocal identity from the Internet?: Initial investigation of cloning Obama's voice using GAN, WaveNet and low-quality found data

Thanks to the growing availability of spoofing databases and rapid advances in using them, systems for detecting voice spoofing attacks are becoming more and more capable, and error rates close to zero are being reached for the ASVspoof2015 database. However, speech synthesis and voice conversion paradigms that are not considered in the ASVspoof2015 database are appearing. Such examples include direct waveform modelling and generative adversarial networks. We also need to investigate the feasibility of training spoofing systems using only low-quality found data. For that purpose, we developed a generative adversarial networkbased speech enhancement system that improves the quality of speech data found in publicly available sources. Using the enhanced data, we trained state-of-the-art text-to-speech and voice conversion models and evaluated them in terms of perceptual speech quality and speaker similarity. The results show that the enhancement models significantly improved the SNR of low-quality degraded data found in publicly available sources and that they significantly improved the perceptual cleanliness of the source speech without significantly degrading the naturalness of the voice. However, the results also show limitations when generating speech with the low-quality found data.