Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating a Trade-Off between DRAM and Persistent Memory for Persistent-Data Placement on Hybrid Main Memory

Persistent memory will be widely used as main memory in future computer systems because it is byte addressable, its cost is lower than that of DRAM, and its access speed is much higher than that of secondary storages. However, since the access speed (especially write speed) and write endurance are lower than those of DRAM, it will be combined with DRAM to constitute main memory. This is well-known as hybrid main memory system, and various techniques to optimize data placement on this system have been proposed. The fundamental concept of them is to place frequently written data on DRAM in order to hide the disadvantage of persistent memory. In this paper, we focus on applications which guarantee data persistence with logging mechanisms and suggest a new trade-off between DRAM and persistent memory on platforms that apply hybrid main memory with secondary storages. If data is placed on volatile DRAM, it can be accessed faster but logging incurs a non-trivial overhead. On the other hand, if the data is placed on non-volatile persistent memory, it suffers from the slower accesses while it can avoid the logging overhead. We quantitatively evaluate this trade-off using an in-memory database as a case study on a persistent memory emulation platform. The results reveal that it is better for performance to place frequently written data on persistent memory rather than DRAM. Compared to placing the data of a workload including 50% reads and 50% writes on DRAM with logging enabled, we can achieve 38% and 25% higher throughput of the database by placing the data on PM whose latency is double and four times of the DRAM latency, respectively.
DEVELOPMENT OF A JOINT REGIONAL BODY AND SURFACE WAVE TOMOGRAPHY METHOD

Inadequately modeled Earth structures cause systematic biases in predictions of geophysical parameters such as the travel times and amplitudes of regional seismic phases. More accurate and reliable predictions of these quantities (especially in aseismic regions) are needed to improve nuclear monitoring efforts to detect, locate and discriminate regional events. To specifically help improve regional location capabilities, Weston Geophysical Corporation and the Massachusetts Institute of Technology are collaborating on the development of a joint body-wave/surfacewave inversion method to derive self-consistent 3-D P and S velocity models for the crust and upper mantle. The method expands on our current Pn travel-time tomography algorithm, which uses finitedifference ray tracing to model body-wave travel times in 3-D Earth models. Surface-wave group delays (group travel times) are modeled in a two-step procedure. The first step calculates phaseand groupvelocity dispersion curves at each point of a latitude/longitude grid, applying 1-D dispersion modeling to the velocity-depth profile for each point. The second step employs 2-D finite-difference ray tracing to the resulting phase-velocity maps, period by period, and then integrates the group-velocity maps along the derived ray paths to obtain group delays. The use of 2-D ray tracing accounts approximately for the nongreat circle propagation of surface waves in 3-D Earth structures. Our inversion approach will follow these steps in reverse. Group-velocity maps will be fit to observed group delays along particular source-receiver paths, and the resulting group-velocity dispersion curve for each geographic grid point will be used to determine an updated velocity-depth profile for the point, using a 1-D inversion technique. Our approach uses the first-arrival body-wave travel times to directly update the 3-D P velocity structure. Our progress to date includes the development of some of the key components of our method. These include 1) surface-wave modeling algorithms to create phase and group velocity maps as a function of period; 2) enhancement of our tomography algorithm to derive either 3-D velocity models (for body-wave inversion) or 2-D group-velocity maps (for surface-wave inversion); and 3) improvements in the underlying inversion methodology to allow for velocity bounds and spatially variable smoothness constraints. In addition, we have generalized the interfaces between the various modules of our system to facilitate the integration of body-wave and surface-wave inversion results into a unified 3-D Earth model. We will apply our joint inversion method to a large region covering central and southern Asia. The bodywave data consist of travel times derived from the Engdahl, van der Hilst and Buland (EHB) bulletin for the years 1988 – 2004. The surface-wave data consist of measured group velocities from the University of Colorado research team, as well as measurements from previous and ongoing experiments in the region of study. 27th Seismic Research Review: Ground-Based Nuclear Explosion Monitoring Technologies
Assessing Indian Industries on the Basis of Financial Ratios Using Certain Data Mining Tools

Analyzing financial performance of companies and grading them in today’s information-rich society can be a daunting task. With the evolution of the technology, Internet access to massive amounts of financial data, typically in the form of financial statements, is widespread. Managers and stakeholders of the industries are in need of the data-mining tools to quickly and accurately analyze the financial data to know about the position of their companies. An emerging technique that may be suited for such applications is the self-organizing map. The purpose of the present study is to evaluate the performances of 247 companies consist of five major industries from Indian corporate database. The time frame of the data pertaining the present study is 2001-2010. The salient feature of this study is the application of Factor, Kmeans clustering and Self Organizing Map (SOM) analyses as data mining tools to develop the hidden structure present in the data for each of the study periods. The scores from extracted factors are used to find initial groups by K-means clustering algorithm. A few outlier industries, which could not be classified to any of the larger groups, are discarded as some of the ratios possessed higher values. Finally, SOM is applied and the groups are identified as companies belonging to A-Class (High performance), B-Class (Moderate performance) and C-Class (Low performance) in that order. The results of the study indicate that self-organizing maps can be a feasible tool for the financial analysis of large amounts of financial ratio data.
A Top-Down Unified Framework for Instance-level Human Parsing

Instance-level human parsing is one of the essential tasks for human-centric analysis which aims to segment various body parts and associate each part with the corresponding human instance simultaneously. Most state-of-the-art methods group instances upon multi-human parsing results, but they tend to miss instances and fail in grouping under the crowded scene. To address this problem, we propose a top-down uniﬁed framework to simultaneously detect human instance and parse every part within that instance. To better parse the single human, we also design an attention module, which is aggregated to our parsing network. As a result, our approach is capable of obtaining ﬁne-grained parsing results and the corresponding human mask in a single forward pass. Experiments show that the proposed algorithm performs favorably against state-of-the-art methods on the CIHP and PASCAL-Person-Part datasets.
Down the Black Hole: Dismantling Operational Practices of BGP Blackholing at IXPs

Large Distributed Denial-of-Service (DDoS) attacks pose a major threat not only to end systems but also to the Internet infrastructure as a whole. Remote Triggered Black Hole filtering (RTBH) has been established as a tool to mitigate inter-domain DDoS attacks by discarding unwanted traffic early in the network, e.g., at Internet eXchange Points (IXPs). As of today, little is known about the kind and effectiveness of its use, and about the need for more fine-grained filtering. In this paper, we present the first in-depth statistical analysis of all RTBH events at a large European IXP by correlating measurements of the data and the control plane for a period of 104 days. We identify a surprising practice that significantly deviates from the expected mitigation use patterns. First, we show that only one third of all 34k visible RTBH events correlate with indicators of DDoS attacks. Second, we witness over 2000 blackhole events announced for prefixes not of servers but of clients situated in DSL networks. Third, we find that blackholing on average causes dropping of only 50% of the unwanted traffic and is hence a much less reliable tool for mitigating DDoS attacks than expected. Our analysis gives also rise to first estimates of the collateral damage caused by RTBH-based DDoS mitigation.
Theory of Mind in Major Depressive Disorder Major Depresif Bozuklukta Zihin Kuramı

Theory of mind refers to the ability to explain the appearing behavior of others with reference to their mental state. Theory of mind ability is thought to be an integral part of maintaining social functioning and interpersonal relationships. One of the important clinical features of major depressive disorder is the deterioration of social and interpersonal functions. Since interpersonal difficulties of people with major depression have become widespread, it is of great importance to have a more detailed understanding of the underlying cognitive mechanisms of these social deficits. The aim of this article is to find out current state of research on theory of mind in depressive disorders. When the findings of the studies are considered, it is thought that the difficulties in the social and interpersonal areas seen in the individuals with major depressive disorder may be due, at least in part, to the distortions in their ability to interpret the emotional stimuli and mental states correctly.
Experimental Evidence on Inequality Aversion : Dictators Give to Help the Less Fortunate +

We design an experiment to identify the motivation underlying dictators’ behavior. In the typical dictator game, the recipient’s payoff is completely determined by the amount passed. We give an endowment to the recipient as well as the dictator, breaking the equivalence between the amount passed and the recipient’s payoff. The majority of dictators behave as if recipients’ payoffs are normal goods. When we increased recipients’ endowments, dictators decreased the amounts passed. More than half of dictators are averse to inequality. They passed nothing when endowments were equalized. We conclude that in the standard dictator game most dictators pass because the recipients are given no endowments and inequality is at its maximum.
Financing Investment in Physical and Intangible Capitals in China ( Preliminary version for First World Congress of Comparative Economics )

Using firm-level micro panel data for non-listed and domestic industrial firms in China from 2000 to 2009, which are drawn from a novel database, we study their manners of financing manners of physical and intangible capital investments. We find: (1) receiving more trade credit enables receiver firms to allocate more internal cash flow to physical capital investment instead of day-to-day operations, namely, indirectly financing physical capital investment by trade credit; (2) complementary relation between internal and external finances changes from that between cash flow and trade credit to that between cash flow and bank loans as investment has higher risk; (3) * Go YANO: Corresponding author. E-mai l : rswtj922@yahoo.co.jp * * Maho SHIRAISHI E-mai l : mshira@kitakyu-u.ac. jp
Present Status of Fish Farming and Livelihood of Fish Farmers in ShahrastiUpazila of Chandpur District , Bangladesh

The present study was conducted to assess the status of fish farming and livelihood of fish farmer in the ShahrastiUpazila, Bangladeshfrom February to September, 2010. Data were collected through the use of well-structured questionnaire from the selected area. The survey revealed that average pond size was 0.24 ha with 85% of the farmers having ponds of single ownership. Indian major carps and exotic carpswere mainly cultured where 10% ponds were seasonal and 90% perennial. Most of the fish farmers were belonged to the age groups of 41 to 60 years (44%), represented by 75% Muslims. About 10% had no education while 16%, 48%, 16% and 10% had primary, secondary, higher secondary and bachelor level of education respectively. About 20% of fish farmers were involved in fish farming as their primary occupation while 45%, 25% and 10% were involved in business, agriculture and service respectively. The provision of training facilities was insufficient as only 34% of the fish farmers received formal training on fish farming. About 94% of the fish farmers reportedtheir socioeconomic conditions were improved through fish farming.Fish disease, non availability of fish fry, pouching, insufficient water in dry season, poor technical knowledge, lack of quality feed and lack of money for fish farming was identified as the major constraints.
Chapter 6 Statistical MWE-aware parsing

This chapter aims at presenting different strategies that have been designed to incorporate multiword expression (MWE) identification in the process of syntactic parsing using statistical approaches. We discuss MWE representation in treebanks, pipeline and joint orchestrations, the integration of external lexicons and the evaluation of MWE-aware parsers, concluding with our suggestions for future research.
PCR-Detection of Ry and Rx Genes from Potato with Potential for Wide Applications in Plants

Nine potato cultivars (‘Lady Rosetta’, ‘Spunta’, ‘Burren’, ‘Cara’, ‘Hermine’, ‘Nicola’, ‘Hermes’, ‘Draga’ and ‘Diamond’) and five wild species (Solanum tuberosum andigena, S. acaule, S. demissum, S. hougasii and S. stoloniferum) were cultivated over three successive seasons from 2004 to 2008 and were systemically infected with potato virus y (PVY) and potato virus X (PVX). DAS-ELISA detected the presence of both viruses in 9 inoculated cultivars and 5 wild species and the infected plants were categorized according to their degree of infection. Among the five designed primers, the 242 bp primer detected the Ryadg gene in five cultivars and S. stoloniferum and five primers detected the Ryadg gene in S. andigena. One of the two designed primers with 541 bp detected the Ryfsto gene in six cultivars. The Rx gene was detected in four cultivars, S. acaule and S. andigena using six designed primers. The existence of Ryadg and Rx genes among ‘Lady Rosetta’, ‘Cara’, ‘Diamond’, ‘Nicola’, ‘Draga’ and ‘Spunta’ and the wild species were confirmed using 242 and 241 bp fragments as probes, respectively by dot-blot hybridization, which showed similar results. Two fragments of PCR products with 201 and 456 bp corresponding to Rx and Ry genes, respectively were sequenced, analyzed and submitted to GenBank under EU687573 and EU687574. _____________________________________________________________________________________________________________
Joint Power and Beamwidth Optimization for Full Duplex Millimeter Wave Indoor Wireless Systems

In this paper, a joint power and beam-level beamwidth control scheme is proposed for full duplex (FD) millimeter wave (mmWave) indoor wireless systems. Energy efficiency of the proposed scheme is investigated considering various system parameters, such as maximum transmit power level, level of self-interference cancellation and pilot transmission overhead. With this analysis for a realistic indoor wireless communication scenario, the feasibility of FD is studied for mmWave links, considering their specific propagation characteristics, namely, narrow transmission and reception beam-level beamwidths and high absorption losses, as well as massive bandwidth which is much larger than the existing sub 6 GHz bands. We evaluate the performance of the proposed FD mmWave system for three power budget schemes (low, moderate and high) in terms of average total energy efficiency. Our simulation results show that, for currently available state-of-the-art self-interference cancellation levels, FD mmWave with proposed joint power and beam-level beamwidth control outperforms the smart half duplex (HD) mmWave with joint transmission slot and beam-level beamwidth control by a factor of up to four times and improves FD mmWave with only power control by up to 33.92 %. If higher (close to ideal) self-interference cancellation can be achieved, the net average total energy efficiency improvements over existing abovementioned schemes, are up to 4.8 times and 26.45 %, respectively. It is concluded that with the proposed joint power and beamwidth control, the current FD mmWave technology promises a good potential for indoor wireless networks.
A Data Effect Aware Microcomponent-Based Estimation Approach For Accurate System-Level Memory Device Power Evaluation

As memory is a major power dominant, we propose a highly efficient microcomponent-based approach with data-aware refinement for accurate system-level power estimation. The proposed method pre-calibrates the power consumption pattern of each identified microcomponent for power simulation. To achieve high accuracy, the data variation effect is considered and a simple interpolation technique is proposed to further boost accuracy. The proposed approach produces accurate results of less than 2% error rate in average for system-level power analysis.
Queries Predictions Encoder Decoder Parity ModelModel Instance k Model Instance 1 Frontend Query 1 Query k Parity QueryPredictio

Machine learning models are becoming the primary workhorses formany applications. Services deploymodels through prediction serving systems that take in queries and return predictions by performing inference on models. Prediction serving systems are commonly run on many machines in cluster settings, and thus are prone to slowdowns and failures that inflate tail latency. Erasure coding is a popular technique for achieving resource-efficient resilience to data unavailability in storage and communication systems. However, existing approaches for imparting erasure-coded resilience to distributed computation apply only to a severely limited class of functions, precluding their use for many serving workloads, such as neural network inference. We introduce parity models, a new approach for enabling erasure-coded resilience in prediction serving systems. A parity model is a neural network trained to transform erasurecoded queries into a form that enables a decoder to reconstruct slow or failed predictions. We implement parity models in ParM, a prediction serving system that makes use of erasure-coded resilience. ParM encodes multiple queries into a “parity query,” performs inference over parity queries using parity models, and decodes approximations of unavailable predictions by using the output of a parity model. We showcase the applicability of parity models to image classification, speech recognition, and object localization tasks. Using parity models, ParM reduces the gap between 99.9th percentile and median latency by up to 3.5×, while maintaining the same median. These results display the potential of parity models to unlock a new avenue to imparting resourceefficient resilience to prediction serving systems. Jack Kosaian is supported by an SOSP 2019 student scholarship from the ACM Special Interest Group in Operating Systems. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. SOSP ’19, October 27–30, 2019, Huntsville, ON, Canada © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-1-4503-6873-5/19/10. $15.00 https://doi.org/10.1145/3341301.3359654 CCS Concepts • Computer systems organization → Redundancy; Reliability.
Sustainability Impact Assessment: Approaches and applications in Europe

This ESDN Quarterly Report (QR) focuses on sustainability impact assessments (SIAs) in Europe. Generally speaking, impact assessments (IAs) address governance challenges like informed (or knowledge-based) decision-making, policy integration, improved strategic management, transparency and stakeholder participation. The growing acceptance of sustainable development (SD) as an overarching guiding principle for policy-making stimulated the use of IAs in order to evaluate the impacts of (cross-)sectoral policies regarding SD. This QR will first provide a definition and overview of different IA approaches. Second, it will describe the integrated IA method developed and applied by the European Commission. Finally, two case studies on Switzerland and Belgium will show how IAs are applied to national policy-making. analysed plan’s trade-offs.
Role of HSWI/SNF associated PRMT5 and MSIN3A/HDAC in the control of gene expression and cancer.

Gene expression is associated with dynamic changes in chromatin structure that allow activator and repressor proteins to access the highly compact DNA. The BRG1 and BRM based human SWI/SNF (hSWI/SNF) complexes represent one of the chromatin remodeling activities, which have been linked to transcriptional activation as well as repression. hSWI/SNF complexes have been purified in association with co-repressor mSIN3/HDAC, but the role of mSIN3/HDAC containing hSWI/SNF complexes in gene repression is not well documented. The goal of this study is to understand the role of hSWI/SNF complexes in transcriptional repression, to identify other hSWI/SNF associated factor(s), and to elucidate their function in vivo. The work presented here shows that both BRG1and BRM-based hSWI/SNF complexes that contain mSIN3A/HDAC are associated with the protein arginine methyltransferase, PRMT5, which methylates histones H3 and H4. Moreover, interaction studies indicate that c-MYC as well as mSIN3A and PRMT5 interact directly with BRG1, BRM, BAF57, and BAF45/INI1 subunits of hSWI/SNF complexes, and that the interaction of c-MYC and PRMT5/mSIN3A with hSWI/SNF is mutually exclusive. To understand the in vivo relevance of these interactions, expression of a c-MYC target gene, CARBAMOYL-PHOSPHATE SYNTHASE-ASPARTATE CARBAMOYLTRANSFERASE-DIHYDROOROTASE (CAD) was analyzed in the presence of catalytically inactive BRG1 and HDAC inhibitor. The results presented in
A Study on Feature Selection , Clustering Techniques for Stuttering Identification in Automatic Speech Recognition

Speech Recognition technology has evolved for more than fifty years spurred on by advances in acoustic processing of signals, developing algorithms, designing architecture and hardware. Speech Recognition Technology it has gone from a laboratory level to a science, and eventually to a full fledged advanced technology that is practiced and utilized by a many people like research scientists, linguists, engineers and system designers. Many researchers enrich the technology and yet so much of speech applications are to come over the upcoming years. Recent development in automatic speech recognition (ASR) technology leads to the need for development of many sophisticated interesting and more accurate speech recognition applications. Much effort was involved to increase the accuracy of the speech recognition system but still erroneous output is generating in current speech recognition systems. This paper includes the ASR technology which is used to create the training system which was used for giving training for the children with fluency disorders. Section I describes the ASR system, Section II describes the feature selection, Section III describes the Confidence measures, and Section IV describes the Clustering Techniques.