Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of High-k Dielectric Materials on Different Structures of 3D FinFET

In this research work, we have examined the electrical characteristics of Double Gate FinFET (DG FinFET), Tri-Gate FinFET (TG FinFET) and Gate All Around FinFET (GAA FinFET) with 10nm channel lengths for different high-k dielectric materials as gate insulators. The Silvaco TCAD software is used for designing and simulation. The performance parameters taken into consideration for analyzing the FinFETs are threshold voltage, subthreshold swing (SS) and $\mathrm{I}_{\text{on}}/\mathrm{I}_{\text{off}}$ ratio. From the simulation results, it is evident that gate-all-around FinFET exhibits better device performance among all three structures due to its enhanced electrostatic control over the channel. The high-k dielectric Hafnium oxide (HfO2) demonstrates better performance among all materials for gate insulators as it shows better electrical performance. This paper also investigates the electrical characteristics of GAA FinFET for different materials like SiGe and 3C-SiC in the channel region. Comparative analysis from the simulation results showed that 3C-SiC provides 1.3183 times more $\mathrm{I}_{\text{on}}/\mathrm{I}_{\text{off}}$ ratio and 0.9923 times less subthreshold swing than the Si as channel materials.
Human Cytomegalovirus Encoded Homologs of Cytokines, Chemokines and their Receptors: Roles in Immunomodulation

Human cytomegalovirus (HCMV), the largest human herpesvirus, infects a majority of the world’s population. Like all herpesviruses, following primary productive infection, HCMV establishes a life-long latent infection, from which it can reactivate years later to produce new, infectious virus. Despite the presence of a massive and sustained anti-HCMV immune response, productively infected individuals can shed virus for extended periods of time, and once latent infection is established, it is never cleared from the host. It has been proposed that HCMV must therefore encode functions which help to evade immune mediated clearance during productive virus replication and latency. Molecular mimicry is a strategy used by many viruses to subvert and regulate anti-viral immunity and HCMV has hijacked/developed a range of functions that imitate host encoded immunomodulatory proteins. This review will focus on the HCMV encoded homologs of cellular cytokines/chemokines and their receptors, with an emphasis on how these virus encoded homologs may facilitate viral evasion of immune clearance.
Assessment of Bovine Leukemia Virus Transcripts In Vivo

ABSTRACT Reverse transcriptase PCR (RT-PCR) consistently detected bovine leukemia virus transcripts in fresh cells, and competitive RT-PCR enumerated these transcripts. The detection of transcripts in limited numbers of tumor cells indicated that expression occurs in a minority of cells. The data suggest that individual cells contain hundreds of copies of the tax/rex transcript in vivo.
FaST: Fine-grained and Scalable TCP for Cloud Data Center Networks

With the increasing usage of cloud applications such as MapReduce and social networking, the amount of data traffic in data center networks continues to grow. Moreover, these appli-cations follow the incast traffic pattern, where a large burst of traffic sent by a number of senders, accumulates simultaneously at the shallow-buffered data center switches. This causes severe packet losses. The currently deployed TCP is custom-tailored for the wide-area Internet. This causes cloud applications to suffer long completion times towing to the packet losses, and hence, results in a poor quality of service. An Explicit Congestion Notification (ECN)-based approach is an attractive solution that conservatively adjusts to the network congestion in advance. This legacy approach, however, lacks scalability in terms of the number of flows. In this paper, we reveal the primary cause of the scalability issue through analysis, and propose a new congestion-control algorithm called FaST. FaST employs a novel, virtual congestion window to conduct fine-grained congestion control that results in improved scalability. Fur-thermore, FaST is easy to deploy since it requires only a few software modifications at the server-side. Through ns-3 simulations, we show that FaST improves the scalability of data center networks compared with the existing approaches.
Autoimmune Encephalomyelitis Established Relapsing Experimental Is Superior to Myelin in Suppressing Oral Administration of Myelin Basic Protein

2 * Oral administration of a myelin component, myelin basic protein (MBP), induces immunological unresponsiveness to CNS Ags and ameliorates murine relapsing experimental autoimmune encephalomyelitis (REAE). However, a recent clinical trial in which multiple sclerosis patients were treated with repeated doses of oral myelin was unsuccessful in reducing disease exacerbations. Therefore, we directly compared the tolerizing capacity of myelin vs MBP during REAE in B10.PL mice. Oral administration of high doses of myelin, either before disease induction or during REAE, did not provide protection from disease or decrease in vitro T cell responses. In contrast, repeated oral administration of high doses of MBP suppressed established disease and MBP-speciﬁc T cell proliferation and cytokine responses. The frequency of IL-2-, IFN- g -, and IL-5-secreting MBP-speciﬁc T cells declined with MBP feeding, implicating anergy and/or deletion as the mechanism(s) of oral tolerance after high Ag doses. We have previously shown that the dosage and timing of Ag administration are critical parameters in oral tolerance induction. Studies presented here demonstrate that Ag homogeneity is also important, i.e., homogeneous Ag (MBP) is more effective at inducing oral tolerance than heterogeneous Ag (myelin). The Journal of Immunology, 1999, 162: 6247–6254. clinical, and immunological similarities between MS and an experimentally relapsing experimental
Learning to Recommend Accurate and Diverse Items

In this study, we investigate diversified recommendation problem by supervised learning, seeking significant improvement in diversity while maintaining accuracy. In particular, we regard each user as a training instance, and heuristically choose a subset of accurate and diverse items as ground-truth for each user. We then represent each user or item as a vector resulted from the factorization of the user-item rating matrix. In our paper, we try to discover a factorization for matching the following supervised learning task. In doing this, we define two coupled optimization problems, parameterized matrix factorization and structural learning, to formulate our task. And we propose a diversified collaborative filtering algorithm (DCF) to solve the coupled problems. We also introduce a new pairwise accuracy metric and a normalized topic coverage diversity metric to measure the performance of accuracy and diversity respectively. Extensive experiments on benchmark datasets show the performance gains of DCF in comparison with the state-of-the-art algorithms.
Inventory-Based Versus Prior-Based Options Trading Agents

Options are a basic, widely-traded form of financial derivative that offer payouts based on the future price of an underlying asset. The finance literature gives us option-trading algorithms that take into consideration information about how prices move over time but do not explicitly involve the trades the agent made in the past. In contrast, the prediction market literature gives us automated market-making agents (like the popular LMSR) that are event-independent and price trades based only on the inventories the agent holds. We simulate the performance of five trading agents inspired by these literatures on a large database of recent historical option prices. We find that a combination of the two approaches produced the best results in our experiments: a trading agent that keeps track of previously-made trades combined with a good prior distribution on how prices move over time. The experimental success of this synthesized trader has implications for agent design in both financial and prediction markets.
A Framework for Building Conversational Agents Based on a Multi-Expert Model

This paper presents a novel framework for building symbol-level control modules of animated agents and robots having a spoken dialogue interface. It features distributed modules called experts each of which is specialized to perform certain kinds of tasks. A common interface that all experts must support is specified, and any kind of expert can be incorporated if it has the interface. Several modules running in parallel coordinate the experts by accessing them through the interface, so that the whole system can achieve flexible control, such as interruption handling and parallel task execution.
Incremental Truncated LSTD

Balancing between computational efficiency and sample efficiency is an important goal in reinforcement learning. Temporal difference (TD) learning algorithms stochastically update the value function, with a linear time complexity in the number of features, whereas least-squares temporal difference (LSTD) algorithms are sample efficient but can be quadratic in the number of features. In this work, we develop an efficient incremental low-rank LSTD({\lambda}) algorithm that progresses towards the goal of better balancing computation and sample efficiency. The algorithm reduces the computation and storage complexity to the number of features times the chosen rank parameter while summarizing past samples efficiently to nearly obtain the sample complexity of LSTD. We derive a simulation bound on the solution given by truncated low-rank approximation, illustrating a bias- variance trade-off dependent on the choice of rank. We demonstrate that the algorithm effectively balances computational complexity and sample efficiency for policy evaluation in a benchmark task and a high-dimensional energy allocation domain.
A theoretical framework for mitigating delay in 3D wireless data center networks

Recently, a novel 3D wireless mechanism based on 60 GHz band has been proposed to mitigate the job completion time (JCT) in Data Center Networks (DCNs), where signals bounce off DC ceilings to establish wireless connections. The 3D scheme could alleviate hotspots in DCNs with flexible multigigabit wireless links, which bypasses the line-of-sight limitation of 60 GHz wireless links. However, the novel wireless transmission mechanism incurs significant change in the traditional interference model for wireless networks, and the theoretical analysis tool for such hybrid networks is still unavailable. This paper presents a theoretical framework for such 3D DCNs, where the entire network is first transformed from 3D to 2D by remodeling the 3D interference effects. The transformed graph is then processed with a multi-dimensional conflict graph methodology, where the wired and wireless sub-graphs induced by the DCN topology are jointly analyzed. The last but not least, the processed graph is modeled as a minimum job completion time (MJCT) problem, where the optimal traffic engineering, channel allocation and scheduling schemes in the original DCN can be obtained. Simulation results are presented to demonstrate the delay performance of our proposed approach.
Research on Transformer Multimodal Named Entity Recognition Based on Co-Occurrence Entities and Knowledge Enhancement

Transformers are important electrical equipment that plays an important role in the three stages of power generation, transmission, and distribution in the power system. Currently, research on transformers is mainly focused on single-modal fields such as images and audio, while research on multimodal named entity recognition is still in its infancy. This paper proposes a transformer multimodal named entity recognition model based on co-occurrence entities and knowledge enhancement to address the issues of image text mismatch and low efficiency of modal fusion in existing methods for multimodal named entity recognition (i.e. Co-occurrence Entities and Knowledge Enhancement, CEKE-MNER). The model adopts cooccurrence entities to solve the problems of image text mismatch and high noise and uses knowledge augmentation to reduce domain knowledge differences to solve the problem of entity semantic divergence. The experimental results on transformer data show that the CEKE-MNER model achieves an F1 value of 82.40% in multimodal data, which is about 2% higher than that in single-modal data; The experimental results on the publicly available datasets Twitter 15, Twitter 17, and Wu Kong CMNER show that the F1 values of the CEKE-MNER model are 72.76%, 85.49%, and 81.96%, respectively, which are higher than the current mainstream multimodal named entity recognition models and have good generalization performance.
Research on Output Voltage Tracking Technique Based on SWDPT System

Due to the complexity of the oil drilling environment, the parameters of the wireless energy transmission system drift, causing the output voltage to drop, which affects the instability of the backstage circuit. In view of the above problems, this paper designs an energy and telecommunication co-transmission system based on the frequency tracking technology of output voltage stabilization, simulates the parameter changes of the coupling mechanism due to the environmental changes, establishes the model of the coupling mechanism by MAXWELL, and builds the simulation model of the system in MATLAB. The experimental results show that the output voltage of the system has been stabilized at the set value unchanged under the change of system parameters, which proves the feasibility of this design.
Smith-Waterman Protein Search with OpenCL on an FPGA

The well-known Smith-Waterman (SW) algorithm is a high-sensitivity method for local alignments. Unfortunately, SW is expensive in terms of both execution time and memory usage, which makes it impractical in many scenarios. Previous research has shown that massively parallel architectures such as GPUs and FPGAs are able to mitigate the computational problems and achieve impressive speedups. In this paper we explore SW acceleration on an FPGA with OpenCL. We efficiently exploit data and thread-level parallelism on an Altera Stratix V FPGA, obtaining up to 39 GCUPS with less than 25 watt of power consumption.
An Improved Vehicle-to-Vehicle Wireless Power Transfer System for Electric Vehicle Applications Using Reconfigurable Two-Phase Transmitter

Vehicle-to-vehicle (V2V) energy transfer is a necessary topology in the Electric Vehicle (EV) charging field. The connectivity between EVs using rigid cables adds difficulties while making the connectivity due to its weight. The recent advancements in Wireless power transfer (WPT) technology give comfortable connectivity between EVs for energy sharing. In WPT, the coupling between the transmitter pad (TP) and the receiver pad (RP) plays a noticeable role in the power transfer efficiency (PTE) and transmission distance. In this article, a reconfigurable two-phase circular transmitter pad improves the coupling factor (K). The impact of the proposed two-phase coil on the coupling factor is verified with the ANSYS-MAXWELL simulation tool. The cross-coupling effects due to the dual transmitter are minimized by phase-shifted excitation of the relay coil and capacitor. An improvement of 4.7% in PTE has been achieved in the implemented charging system with two-phase coil-based WPT by performing the energy sharing between two 24V, 28Ah batteries.
Proximity-aware directory-based coherence for multi-core processor architectures

As the number of cores increases on chip multiprocessors, coherence is fast becoming a central issue for multi-core performance. This is exacerbated by the fact that interconnection speeds are not scaling well with technology. This paper describes mechanisms to accelerate coherence for a multi-core architecture that has multiple private L2 caches and a scalable point-to-point interconnect between cores. These techniques exploit the differences in geometry between chip multiprocessors and traditional multiprocessor architectures. Directory-based protocols have been proposed as a scalable alternative to snoop-based protocols. In this paper, we discuss implementations of coherence for CMPs and propose and evaluate a novel directory-based coherence scheme to improve the performance of parallel programs on such processors. Proximity-aware coherence accelerates read and write misses by initiating cache-to-cache transfers from the spatially closest sharer. This has the dual benefit of eliminating unnecessary accesses to off-chip memory, and minimizing the distance over which communicated data moves across the network. The proposed schemes result in speedups up to 74.9% for our workloads.
Rummaging Around Workload Portrayal for Web Servers

Use of the Internet and the World Wide Web has improved swiftly over the past few years. Personals deploying Web servers crave to appreciate how their servers are being used by Internet users, how those patterns of use are changing over time, and what steps they should take to ensure adequate server response to the incoming requests today and in the future. This requires an evaluation of the requests offered to the Web server and the characteristics of the server’s response to those requests over a suitably long time interval. In this paper we present the results of a study of Web server system of http://www.lawetalnews. com for a six month period. In this duration the traffic to the website raised drastically in terms of incoming requests and outgoing bytes. We study the request and response types, and portray the traffic distribution on the basis of request size, response time, as well as some other factors. We wind up with system performance recommendations and identify future directions for our Web research.
Designing Supplementary Material "English for Culinary" for Vocational High School Students

This research aims to design supplementary materials “English for Culinary” based on the text-based approach as teaching materials for Vocational High School students. The teaching materials were designed and developed based on English teachers’, students’ needs and the syllabus context. Each task is provided with task instruction in the form of individual, pair, and group work. There are some features in each lesson that can help the teacher and students to understand the materials and tasks in each lesson. Those features are learning goals, lesson focuses and language focuses. The methods-used in this research were analyzing, designing, and developing. The validation checklists revealed that the supplementary grammar materials were scored between adequate and excellent. This result shows that the supplementary grammar materials are useful. In addition, the supplementary grammar materials which were designed based on the result of interview data, students’ questionnaire, curriculum 2013 and syllabus review indicate that the supplementary grammar materials are workable as the teaching materials in SMK Negeri 5 Pontianak.Keywords: ADDIE Instructional Design, Supplementary Materials, Text- based Approach
Citation Indexes in Sociological and Historical Research

The use of citation data in the computer construction of historical network diagrams is proposed. A new ‘critical path method’ of evaluating the impact of individual scientific discoveries is described as well as a ‘critical impact’ factor which measures the arborization of specific papers or ideas through the subsequent literature. The danger of indiscriminate, unqualified use of quantitative citation data in sociological evaluations is emphasized. However, citation indexes do make thorough qualitative evaluations possible by providing a practical tool for locating subsequent criticisms of particular papers by specified authors.
Result diversification based on query-specific cluster ranking

Result diversification is a retrieval strategy for dealing with ambiguous or multi-faceted queries by providing documents that cover as many facets of the query as possible. We propose a result diversification framework based on query-specific clustering and cluster ranking, in which diversification is restricted to documents belonging to clusters that potentially contain a high percentage of relevant documents. Empirical results show that the proposed framework improves the performance of several existing diversification methods. The framework also gives rise to a simple yet effective cluster-based approach to result diversification that selects documents from different clusters to be included in a ranked list in a round robin fashion. We describe a set of experiments aimed at thoroughly analyzing the behavior of the two main components of the proposed diversification framework, ranking and selecting clusters for diversification. Both components have a crucial impact on the overall performance of our framework, but ranking clusters plays a more important role than selecting clusters. We also examine properties that clusters should have in order for our diversification framework to be effective. Most relevant documents should be contained in a small number of high-quality clusters, while there should be no dominantly large clusters. Also, documents from these high-quality clusters should have a diverse content. These properties are strongly correlated with the overall performance of the proposed diversification framework. © 2011 Wiley Periodicals, Inc.
A map of the landscape diversity of Poland

Presented here is a map of Poland drawn up to show differences in values obtained for the Shannon Diversity Index, as calculated using Corine Land Cover data for the 2012 situation regarding the country’s landscape. The level of detail is that of the Polish commune (gmina – unit of local-government administration), so the analysis may prove to be of practical value. The same method gained previous use in depicting issues relevant to Poland’s 2011 National Spatial Development Concept 2030.
Finding Optimum Solutions to the Shift Minimization Personnel Task Scheduling Problem With a New Pack-based Approach

Workforce scheduling is an important practical problem whose solution quality significantly affects an organization’s finances, morale and quality of service. It follows that good algorithms for various shift generation problems are needed for efficient real-world solutions. The Shift Minimization Personnel Task Scheduling Problem (SMPTSP) is to assign all the tasks to the minimum number of employees s.t. the employees have the requisites to carry out the tasks assigned to them, and that no two tasks assigned to the same employee overlap in time. We present a new pack-based heuristic for SMPTSP. The heuristic combines a powerful pack-based local search operator with a reinforcement learning weighting scheme for the tasks. The new heuristic performs better than the previously published methods. For almost all of the known 277 benchmark instances, the heuristic finds the optimum solution within ten seconds.