Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the Accuracy of Prediction Algorithms for Classiication: an Overview

We provide a uniied overview of methods that currently are widely used to assess the accuracy of prediction algorithms, from raw percentages, quadratic error measures and other distances, correlation coeecients and to information theoretic measures such as relative entropy and mutual information. We brieey discuss the advantages and disadvantages of each approach. For classiication tasks, we derive new learning algorithms for the design of prediction systems by directly optimising the correlation coeecient. We observe and prove several results relating sensitivity and speciicity of optimal systems. While the principles are general, we illustrate the applicability on speciic problems such as protein secondary structure and signal peptide prediction.
Navigator: A Decentralized Scheduler for Latency-Sensitive ML Workflows

We consider ML query processing in distributed systems where GPU-enabled workers coordinate to execute complex queries: a computing style often seen in applications that interact with users in support of image processing and natural language processing. In such systems, coscheduling of GPU memory management and task placement represents a promising opportunity. We propose Navigator, a novel framework that unifies these functions to reduce job latency while using resources efficiently, placing tasks where data dependencies will be satisfied, collocating tasks from the same job (when this will not overload the host or its GPU), and efficiently managing GPU memory. Comparison with other state of the art schedulers shows a significant reduction in completion times while requiring the same amount or even fewer resources. In one case, just half the servers were needed for processing the same workload.
Advanced Energy Efficient Routing Strategy based on Election for Wireless Sensor Networ

In WSN Sensor node near to the sink will exhaust their limited energy more rapidly than other sensor nodes, since they will have to forward huge data during multi hop transfer to the base station. Therefore network lifetime will be reduced because of hotspot problem. Important research issue is how to cope up with network lifetime. In this paper, a modified Election based Protocol is proposed, the decision of selecting cluster heads by the sink is based on the additional energy and residual energy and geographical location at each node. Besides, the cluster head also selects the shortest path to reach the sink with the use of the congested link. Simulation results show that our approach enhanced the performance than traditional routing algorithms, such as LEACH.
BetrFS: Write-Optimization in a Kernel File System

Applications exhibit a mixture of I/O patterns, ranging from large, sequential reads to small, random writes, yet general-purpose file system designs trade good performance on some I/O patterns for poor performance on others. For instance, ext4 is designed to update data in place. Ext4 can issue sequential reads and writes at disk bandwidth, while only realizing a small fraction of disk bandwidth for random writes, as are commonly exhibited by applications such as SQLite, or common IMAP email servers. This talk describes BetrFS, an in-kernel file system for Linux that offers good performance on all operations. First, BetrFS uses a data structure called a B^\epsilon-tree to index on-disk data. A B^\epsilon-tree eliminates the trade-off between small, random writes and large, sequential scans. BetrFS also introduces techniques at the OS and data structure level to smoothly navigate other tensions, such as balancing large directory rename performance against maintaining on-disk locality for efficient directory searches. Compared to commodity file systems, BetrFS can improve workload performance by up to two orders of magnitude, and generally matches other file systems in the worst cases. For example, BetrFS improves performance of the Dovecot IMAP server by up to 41% over update-in-place file systems, such as ext4 or btrfs, and can improve rsync performance by up to 31.5x. More information about BetrFS, including source code, is available at betrfs.org. Bio: Don Porter is an Assistant Professor of Computer Science at the University of North Carolina at Chapel Hill. Prior to joining UNC, Porter was an Assistant Professor at Stony Brook University. Porter's research interests broadly involve developing more efficient and secure computer systems. Porter earned a Ph.D. and M.S. from The University of Texas at Austin, and a B.A. from Hendrix College. He has received awards including the NSF CAREER Award, Best Paper at FAST 2016 and EuroSys 2016, and the Bert Kay Outstanding Dissertation Award from UT Austin.
INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY

: Video object segmentation and tracking these are two important factors of smart surveillance systems. However, there are several issues that need to be resolved. Effective Threshold decision is a difficult problem for video object segmentation with a multi-background model. In this project, video object segmentation and tracking framework is proposed for smart cameras in visual surveillance networks with two major building blocks. Firstly, a local threshold decision algorithms which will be effective for video object segmentation with a multi-background model. The local version of automatic threshold decision can be developed to reduce the required memory size by generating the threshold values. In Second, a video object tracking framework based on a particle filter function composed of diffusion distance for measuring colour histogram and motion clue from video object segmentation is used. In addition, more powerful post processing for the object masks, such as Shadow and ghost region removal, can be further integrated into the object segmentation subsystem.
A Model for Document classification using Kernel Discriminant Analysis(KDA) and semantic analysis

: In recent days, digital communication has become inevitable. The scope of this research is to provide a theoretical framework of automatically classification of question paper. It can be helpful to the library of any University to automate their archival process. A survey has been conducted to understand the existing systems in this research area. After doing the survey we observed that many authors have done significant work in document classification but little work has been done to automatically store the documents in particular folder. Therefore, there is a huge scope to develop working model of the framework suggested in this paper.
Ancestral sequence reconstruction with phangorn ( Version 1 . 3-1 )

For parsimony analysis of the edge length represent the observed number of changes. Reconstructiong ancestral states therefore defines also the edge lengths of a tree. However there can exist several equally parsimonious reconstructions or states can be ambiguous and therefore edge length can differ. ”MPR” reconstructs the ancestral states for each (internal) node as if the tree would be rooted in that node. However the nodes are not independent of each other. If one chooses one state for a specific node, this can restrict the choice of neighboring nodes (figure 2). The funtion acctran (accelerated transformation) assigns edge length and internal nodes to the tree [3].
Package 'scrime' Title Analysis of High-dimensional Categorical Data Such as Snp Data

November 1, 2009 Type Package Title Analysis of High-Dimensional Categorical Data such as SNP Data Version 1.1. 9 Date 2009-10-30 Author Holger Schwender, Arno Fritsch Maintainer Holger Schwender <holger.schw@gmx.de> Suggests MASS, RSQLite (>= 0.4-15), oligoClasses (>= 1.1.9) Description Tools for the analysis of high-dimensional data developed/implemented at the group “Statistical Complexity Reduction In Molecular Epidemiology” (SCRIME). Main focus is on SNP data. But most of the functions can also be applied to other types of categorical data. License GPL-2 biocViews Genetics, SNPsAndGeneticVariability, Statistics, MultipleComparisons Repository CRAN Date/Publication 2009-11-01 16:17:36
A State Space Modeling Approach to 1 Real-Time Phase Estimation 2

8 Brain rhythms have been proposed to facilitate brain function, with an especially important role 9 attributed to the phase of low frequency rhythms. Understanding the role of phase in neural 10 function requires interventions that perturb neural activity at a target phase, necessitating 11 estimation of phase in real-time. Current methods for real-time phase estimation rely on 12 bandpass filtering, which assumes narrowband signals and couples the signal and noise in the 13 phase estimate, adding noise to the phase and impairing detections of relationships between 14 phase and behavior. To address this, we propose a state space phase estimator for real-time 15 tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the 16 requirement of bandpass filtering, separately models the signal and the noise, accounts for 17 rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in 18 simulations that the state space phase estimator outperforms current state-of-the-art real-time 19 methods in the contexts of common confounds such as broadband rhythms, phase resets and 20 co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method 21 is available as a ready-to-use plug-in for the OpenEphys acquisition system, making it widely 22 available for use in experiments. 23 Introduction 24 Rhythms, consistently periodic voltage fluctuations, are an ubiquitous phenomenon 25 observed in brain electrophysiology across scales and species. Many studies have described a 26 relationship between rhythms and behavior (Buzsáki, 2006; Haegens & Zion Golumbic, 2018; 27 VanRullen, 2016). A prominent feature of rhythms proposed as relevant for neural processing is 28 the phase. Phase has been proposed to coordinate neural spiking locally and potentially, 29 through coherent networks, even globally (Fries, 2015; Maris et al., 2016). For example, the 30 phase of slow rhythms (3-15 Hz) has shown relationships to dynamics in perception (Busch et 31 al., 2009; Gaillard et al., 2020; Gregoriou et al., 2009; Helfrich et al., 2018). Phase-amplitude 32 synchrony is common (Canolty & Knight, 2010; Hyafil et al., 2015), and has been suggested to 33
Journal of Geophysical Research: Solid Earth Meter-Scale Experiments on Magma-Water Interaction

Interaction of magma with groundwater or surface water can lead to explosive phreatomagmatic eruptions. Questions of this process center on eﬀects of system geometry and length and time scales, and these necessitate experiments at larger scale than previously conducted in order to investigate the thermohydraulic escalation behavior of rapid heat transfer. Previous experimental work either realized melt-water interaction at similar meter scales, using a thermite-based magma analog in a conﬁning vessel, or on smaller scale using ≃ 0.4 kg remelted volcanic rock in an open crucible, with controlled premix and a ≃ 5 J kinetic energy trigger event. The new setup uses 55 kg melt for interaction, and the timing and location of the magma-water premix can be controlled on a scale up to 1 m. A trigger mechanism is a falling hammer that drives a plunger into the melt ( ≃ 28 J kinetic energy). Results show intense interaction ( L max ≥ 0 . 25m 2 ) at relatively low magma/water mass ratio. A video analysis quantiﬁes rate and amount of melt ejection and compares results with those using the same melt in the smaller scale setup. Experiments show that on the meter scale intense interaction can start spontaneously without an external trigger if the melt column above the initial mixing location is larger than 0.3 m. Experiments suggest that buoyant rise of water domains in a melt column could promote explosive interaction. We assess interaction scenarios between introduced water domains and a magma column, some of which could result in eruptions of Strombolian style, promoting brecciation and incorporation of wall rock debris into a magma column.
A 𝛿 : Autodiff for Discontinuous Programs – Applied to Shaders

criterion applies to a relatively broad class of functions. Moreover, we show that the method is effective in practice for arbitrary programs, including features for which we cannot prove correctness. We evaluate this approach on procedural shader programs, where the task is to optimize unknown parameters in order to match a target image, and our method outperforms baselines in terms of both convergence and efficiency. Our compiler outputs gradient programs in TensorFlow, PyTorch (for quick prototypes) and Halide with an optional auto-scheduler (for efficiency). The
Impact of shape of container on natural convection and melting inside cavities

The present paper numerically analyzes a passive cooling system using cavities with different geometries filled with thermal conductivity-enhanced phase change material (PCM). A numerical code is developed using an unstructured finite-volume method and an enthalpy-porosity technique to solve for natural convection coupled to a solid-liquid phase change. Five geometries containing the same volume of PCM are compared while cooling the same surface. The unsteady evolution of the melting front and the velocity and temperature fields is detailed. Other indicators of cooling efficiency are monitored, including the maximum temperature reached at the cooled surface. The computational results show the high impact of varying geometry: a maximum temperature difference as high as 40 °C is observed between two of the cavities. The best efficiency is obtained for a cavity shifted vertically relative to the cooled surface. Other findings and recommendations are made for the design of PCM-filled cavities.
九州大学学術情報リポジトリKyushu University

The Ewens sampling formula is well-known as a distribution of a random partition of a positive integer n or a set of integers f 1 ; 2 ; (cid:1) (cid:1) (cid:1) ; n g . The number K n of distinct components of the formula has the asymptotic normality. For its well-known form and the related form, Yamato(2013) gives their Edgeworth expansions. But, their appropriateness depend on the parameter. Using the functions of R, we consider its normal approximation suitable for any value of the parameter. As the application, we show the method to search the the maximum likelihood estimator of the parameter graphically, and gives its approximate distribution. We also consider the approximate distribution of K n in case where the parameter of the formula is the random variable. These results are shown with the graphs.
Purdue e-Pubs Purdue e-Pubs

We describe comprehensive analysis tools for human joints, such as knees, hips, and hands. The tools construct solid models of the bone geometry from computer tomography images, analyze the contacts between pairs of interacting bones, simulate the rigid body dynamics of the joint, compute the bone stresses, and visualize the results with 3D, interactive graphics. The user provides the computer tomography data, material properties, and driving forces. The tools simulate the joint physics with minimal user intervention and compute at interactive speed despite the great size of the data sets. They support non-invasive diagnostics, systematic preoperative planning, custom prosthesis design, quantitative models of strain injuries, and medical education software.
ScholarWorks ScholarWorks

Debriefing is an important step in game-based learning environments. In the present study, the effect of different debriefing strategies in terms of two factors, grouping (self vs. team) and timing (in-game vs. post-game), was investigated on the motivation and self-efficacy levels of students. In a 2x2 ANOVA design, 62 sixth grade students were randomly assigned into two debriefing groups: self-debriefing and team debriefing. About half of members in each group performed either one of the two debriefing: in-game debriefing or post-game debriefing. Students in the self-debriefing as well as in the team-briefing group played the game three days a week over nine weeks. As students finished the task, motivation and self-efficacy scales were administered and semi-structured interviews were conducted. Findings indicate that students showed higher motivation and self-efficacy scores in the team debriefing than in the self-debriefing. Moreover, the in-game debriefing group outperformed the post-game debriefing group in terms of self-efficacy and motivation levels. Semi-structured interviews supported the quantitative results that students benefited more from collaborative debriefing sessions.
Offline Signature Recognition using Radon Transform

Vector Machine (SVM) is presented. In the first step, projections of original signatures along four specified directions have been performed using radon transform. Then, FDs of four obtained vectors are calculated to construct a feature vector for each signature. These vectors are then fed into SVM classifier for recognition of signatures. In order to evaluate the effectiveness of the system several experiments are carried out. Offline signature database from signature verification competition (SVC) 2004 is used during all of the tests. Experimental result indicates that the proposed method achieved high accuracy rate in signature recognition.
Implicit Updates in an RKHS Recall

We present a new algorithm for online learning in reproducing kernel Hilbert spaces (RKHS). Our algorithm, ILK (implicit online learning with kernels), employs a new, implicit update technique that can be applied to a wide variety of convex loss functions. We prove mistake bounds and analyze the convergence rates of our algorithm. We also point out connections to a number of existing algorithms.
Invisible Glue: Scalable Self-Tunning Multi-Stores.

Next-generation data centric applications often involve di-verse datasets, some very large while others may be of mod-erate size, some highly structured (e.g., relations) while others may have more complex structure (e.g., graphs) or little structure (e.g., text or log data). Facing them is a variety of storage systems, each of which can host some of the datasets (possibly after some data migration), but none of which is likely to be best for all, at all times. Deploying and efficiently running data-centric applications in such a complex setting is very challenging. We propose Estocada, an architecture for efficiently han-dling highly heterogeneous datasets based on a dynamic set of potentially very different data stores. Estocada pro-vides to the application/programming layer access to each data set in its native format, while hosting them internally in a set of potentially overlapping fragments, possibly dis-tributing (fragments of) each dataset across heterogeneous stores. Given workload information, Estocada self-tunes for performance, i.e., it automatically choses the fragments of each data set to be deployed in each store so as to op-timize performance. At the core of Estocada lie powerful view-based rewriting and view selection algorithms, required in order to correctly handle the features (nesting, keys, con-straints etc.) of the diverse data models involved, and thus to marry correctness with high performance.
Building Scalable Dynamic Hash Tables on Persistent Memory

1 MOTIVATION Dynamic hash tables are fundamental building blocks of many dataintensive systems such as database systems and key-value stores. Byte-addressable persistent memory (PM) such as Intel Optane DCPMM promises persistence, high capacity, low cost and high performance on the memory bus. These features make it very attractive to build hash tables that directly operate and persist on PM with high performance and instant recovery. Although there have been a new breed of hash tables [3, 6] speci cally designed for PM, they were mostly based on DRAM emulation before actual PM was available, and we notice two critical issues when they are deployed on DCPMM: (1) they do not scale well, and (2) desirable properties are often traded o . The main culprit of low scalability is Optane DCPMM’s limited bandwidth which is ∼3–14× lower than DRAM’s [5]. Notably, DCPMM exhibits very limited performance for small, random accesses, which are inherent access pattern for hash tables. Excessive PM accesses could easily saturate the system and prevent the system from scaling. We describe two major sources of excessive PM accesses that were not given enough attention before, followed by a discussion of important but missing functionality in prior work. Excessive PM Reads. Much prior work focused on reducing writes and cacheline ushes to PM, however, we note that it is also imperative to reduce PM reads; yet many existing solutions reduce PM writes by incurring more PM reads. Di erent from the devicelevel behavior (PM reads being faster than writes), end-to-end write latency (i.e., the entire data path including CPU caches and write bu ers in the memory controller) is often lower than reads [5]. In particular, existence checks in record probing constitute a large proportion of such PM reads: to nd out if a key exists, one or multiple buckets (e.g., with linear probing) have to be searched. Heavyweight Concurrency Control. Most prior work sidestepped the impact of concurrency control. Bucket-level read-write locking has been widely used [3, 6], but it incurs additional PM writes to acquire/release read locks, further pushing bandwidth consumption towards the limit. Missing Functionality.We observe in prior designs, necessary functionality is often traded o for performance (though scalability is still an issue on real PM). (1) High load factor is important but often sacri ced by organizing buckets using larger segments in exchange for smaller directories (fewer cache misses) [3]. (2) Prior designs lack e cient support for variable-length keys. (3) Instant recovery is a unique, desirable feature on PM, but is often omitted in prior work which requires a linear scan of the metadata whose size scales with data size. (4) Prior approaches also often side-step the PM programming issues (e.g., PM allocation), which impact the proposed solution’s scalability and adoption in practice.
Understanding Motion Capture for Computer Animation

Understanding Motion Capture for Computer Animation discusses the latest technology developments in digital design, film, games, medicine, sports, and security engineering. Motion capture records a live-motion event and translates it into a digital context. It is the technology that converts a live performance into a digital performance. In contrast, performance animation is the actual performance that brings life to the character, even without using technology. If motion capture is the collection of data that represents motion, performance animation is the character that a performer represents. The book offers extensive information about motion capture. It includes state-of-the-art technology, methodology, and developments in the current motion-capture industry. In particular, the different ways to capture motions are discussed, including using cameras or electromagnetic fields in tracking a group of sensors. This book will be useful for students taking a course about digital filming, as well as for anyone who is interested in this topic. * Completely revised to include almost 40% new content with emphasis on RF and Facial Motion Capture Systems* Companion site with source code, motion capture data translator, and example files help you write conversion and motion data manipulation programs* Describes all the mathematical principles associated with motion capture and 3D character mechanics* Helps you budget by explaining the costs associated with individualized motion capture projects