Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation for minimization of computation time of partial products for designing multipliers

Multiplier is one of the major hardware circuits of microprocessor and high performance systems such as digital signal processor; FIR filters, processing operations like Convolution, Cross Correlation, and auto-correlation of discrete signals, digital Image processing applications such as edge detection etc. The major design constraint of multiplier is speed which is affected due to propagation delay of adders, low power consumption, area which is conflicting constraints of speed etc. Our designs propose tradeoffs among computational time, latency and throughput of Vedic digital multiplier. This paper presents the high speed and low power multiplier logic design using Vedic multiplication method. The simulations have been carried out in Xilinx simulator for timing waveform simulation and a CMOS layout of Vedic multiplication is implemented in microwind layout simulator tool for analysis of power dissipation using 50nm technology.
The University of Illinois' Graduate School of Library and Information Science at TREC 2011

The University of Illinois’ Graduate School of Library and Information Science (GSLIS) participated in TREC’s microblog track this year–the track’s first iteration. In keeping with the foundational status of the track, our goals were chosen to emphasize the role of a single factor in microblog IR: time. As such, we adhered to the strictest guidelines of the task description, using only real-time information available in the microblog corpus to inform retrieval. Our innovation involved assessing the extent to which (and the way in which) temporal factors can improve microblog search effectiveness.
Co-adaptive Brain-Machine Interface via Reinforcement Learning

to control a prosthetic. Here, we present the theory and in vivo experimental paradigm to illustrate how this BMI learns to complete a reaching task using a prosthetic arm in a 3-D workspace based on the user's neuronal activity. This semi-supervised learning framework does not require user movements. We quantify BMI performance in closed-loop brain control over 6-10 days for three rats as a function of increasing task difficulty. All three subjects co-adapted with their BMI control algorithms to control the prosthetic significantly above chance at each level of difficulty.
Improved prediction of protein-protein interactions using AlphaFold2 and extended multiple-sequence alignments

Predicting the structure of single-chain proteins is now close to being a solved problem due to the recent achievement of AlphaFold2 (AF2). However, predicting the structure of interacting protein chains is still a challenge. Here, we utilise AF2 to optimise a protocol for predicting the structure of heterodimeric protein complexes using only sequence information. We find that using the default AF2 protocol, 32% of the models in the Dockground test set can be modelled accurately. By tuning the input alignment and identifying the best model, we adjusted the performance to 43%. Our protocol uses MSAs generated by AF2 and MSAs paired on the organism level generated with HHblits. In a more extensive, more realistic, independent test set, the accuracy is 59%. In comparison, the alternative fold-and-dock method RoseTTAFold is only successful in 10% of the cases on this set and traditional docking methods 22%. However, for the traditional method, the performance would be lower if the bound form of both monomers was not known. The success is higher for bacterial protein pairs, pairs with large interaction areas consisting of helices or sheets, and many homologous sequences. We can distinguish acceptable (DockQ>0.23) from incorrect models with an AUC of 0.84 on the test set by analysing the predicted interfaces. At an error rate of 1%, 13% are acceptable (at a 10% error rate, 40% of the models are acceptable). All scripts and tools to run our protocol are freely available at: https://gitlab.com/ElofssonLab/FoldDock.
Exploiting Coarse-Grained Task , Data , and Pipeline Parallelism in Stream Programs

As multicore architectures enter the mainstream, there is a pressing demand for high-level programming models that can effectively map to them. Stream programming offers an attractive way to expose coarse-grained parallelism, as streaming applications (image, video, DSP, etc.) are naturally represented by independent filters that communicate over explicit data channels. In this paper, we demonstrate an end-to-end stream compiler that attains robust multicore performance in the face of varying application characteristics. As benchmarks exhibit different amounts of task, data, and pipeline parallelism, we exploit all types of parallelism in a unified manner in order to achieve this generality. Our compiler, which maps from the StreamIt language to the 16-core Raw architecture, attains a 11.2x mean speedup over a single-core baseline, and a 1.84x speedup over our previous work.
Coupled Diffusion and Reaction Processes in Rock Matrices: Impact on Dilute Groundwater Plumes

Abstract : The overall goal of this research was to measure and evaluate the impacts of bedrock structure and mineralogy on the persistence and diffusive flux of trichloroethene (TCE) from rock matrices to groundwater, and to verify that abiotic dechlorination reactions capable of significantly reducing monitored natural attenuation (MNA) time frames actually occur in the field within bedrock matrices. Rates of abiotic chlorinated ethene degradation due to reaction with naturally occurring ferrous minerals within the rock matrices were compared to the rate of diffusive flux through the rock matrix. The impact of this degradation on attenuation time frames subsequently was assessed.
Evaluation of the Integrated Use of Nanosatellite Images and Classifiers based on Machine Learning for Studies of Hydrological Dynamics in the Nhecolândia Region (

: The Lower Nhecolândia region is one of the most iconic landscapes in the Pantanal Basin. Its unique morphology comprises more than 10,000 lakes with saline-alkaline water and fresh water that coexist in an area of approximately 12,000 km². This region is subject to seasonal flooding that acts on runoff; however, little is known about its flooding dynamics. Recent advances in the area of geoprocessing have helped expand our knowledge about lacustrine environments. This work evaluates the performance of two supervised classifiers based on machine learning (Support Vector Machine and Random Forest), for characterizing the hydrological dynamics of the Nhecolândia region. The classifiers were applied to nanosatellite images (PlanetScope) using the Google Earth Engine cloud computing platform. The results showed satisfactory and similar performance of these two classifiers.
LAP-based tracking for focal adhesions

We present a focal adhesion (FA) detector and tracker based on an IMM algorithm combined with the double use of the linear assignment problem (LAP) for data association. Performance has been evaluated using a real confocal microscopy image sequence of a migrating NHEK stained for FAs. The tracker has been compared against several state-of-the-art, commercially available tracking algorithms. With a MOTA of 31.6 , it outperforms each of these other methods.
Communications of the Association for Information Systems Communications of the Association for Information Systems

: In this paper, we describe how a Danish problem-based learning university adapted to the circumstances surrounding the coronavirus disease of 2019 (COVID-19) pandemic. Our findings reveal that digital problem-based learning mitigated some negative consequences that appeared with the lockdown and resulting shift to 100 percent online teaching. While students prefer the traditional face-to-face teaching mode due to the energy, variation, and socialization associated with on-campus learning, we observed that students who have worked in project groups had a more positive experience with online learning, which indicates belonging to a project group can increase students’ motivation to participate in online teaching activities. Our findings challenge the idea that problem-based learning revolves around face-to-face interaction on campus. We highlight lessons that we learned when we rapidly shifted to online distance teaching.
Kobe University and Muroran Institute of Technology at TRECVID 20112 Semantic Indexing Task.

This paper describes our method developed for TRECVID 2012 Semantic INdexing (SIN) Task. Our main research purpose is the development of a fast method, which can work on a single processor with no performance degra- dation. To this end, computationally expensive processes are re-formulated based on matrix operation. We re-formulate the Euclidian distance computation for the kernel value computa- tion in an SVM, and the probability density computation of multivariate normal distributions for the GMM supervector representation. This enables accurate concept detection using a large number of training examples, and spatially-temporally dense features. The following four runs were submitted to SIN (light) task: L A kobe muro l5 4: This is our baseline run using five
The ACODEA framework : Developing segmentation 5 and classification schemes for fully automatic analysis 6 of online discussions 7

11 12 Abstract Research related to online discussions frequently faces the problem of 13 analyzing huge corpora. Natural Language Processing (NLP) technologies may allow 14 automating this analysis. However, the state-of-the-art in machine learning and text 15 mining approaches yields models that do not transfer well between corpora related to 16 different topics. Also, segmenting is a necessary step, but frequently, trained models 17 are very sensitive to the particulars of the segmentation that was used when the model 18 was trained. Therefore, in prior published research on text classification in a CSCL 19 context, the data was segmented by hand. We discuss work towards overcoming these 20 challenges. We present a framework for developing coding schemes optimized for 21 automatic segmentation and context-independent coding that builds on this segmenta22 tion. The key idea is to extract the semantic and syntactic features of each single 23 word by using the techniques of part-of-speech tagging and named-entity recognition 24 before the raw data can be segmented and classified. Our results show that the coding 25 on the micro-argumentation dimension can be fully automated. Finally, we discuss 26 how fully automated analysis can enable context-sensitive support for collaborative 27 learning.
COMPARING DAIRY FARM PERFORMANCE AND HEAT STRESS ABATMENT STRATEGIES IN THE UNITED STATES USING SUMMER TO WINTER RATIOS

OF THESIS COMPARING DAIRY FARM PERFORMANCE AND HEAT STRESS ABATMENT STRATEGIES IN THE UNITED STATES USING SUMMER TO WINTER RATIOS Heat stress abatement is a challenge for producers in the United States, especially in the southern states. Dairy producers could benefit by having a simply metric to measure heat stress abatement strategies with the goal of motivating improvement in heat stress management. Managing heat stress is key to ameliorating the effects on dairy cow performance. A study was performed to explore the use of a heat stress metric called the Summer to Winter performance ratio (S:W ratio), to quantify and compare farm performance variables among regions of the United States. Summer to Winter ratios were closest to 1.0 in the northern regions and furthest from 1.0 in the southern regions for all performance variables other than milk fat and protein percentage. This suggests that summer performance varies by region and shown using the S:W ratio. A second study compared S:W ratios among Southeast states and then applied the performance ratio to heat abatement strategies in Southeast states. The S:W ratio varied by performance measure and heat abatement strategies but tended to be best for herds implementing cow cooling strategies. The studies in this thesis demonstrated S:W ratios can identify heat stress differences by region and heat abatement strategies by herds.
Stored Program Controlled Network: 800 Service using SPC network capability

On April 25, 1982 the Federal Communications Commission approved the Bell System tariff for Expanded 800 Service. In the approval the FCC noted that the new features would “provide subscribers with operational and call routing flexibility previously unavailable. ” This service marks the first use, on a ubiquitous nationwide basis, of powerful call control capabilities provided by the stored program controlled network. This paper reviews the application of these capabilities to our 800 Service.
Reconnaissance et Extraction de Pièces d'identité

This article deals with the problem of recognition and location of identity documents within a scanned image. The aim is to detect if an identity document is present in order to locate and extract the region of interest containing it and then to provide the region to an OCR. Initially, a model of an identity document is defined and a set of points of interest are extracted from this model. In a second step, a set of points of interest is extracted from the query image. This set is matched with the model set. This matching is used to calculate the geometric transformation (translation, rotation, zoom) allowing the registration between the model and query image. The position is determined by using the transformation matrix, so the identity can be extracted.
Malicious- and Accidental-Fault Tolerance for Internet Applications

ii Abstract MAFTIA's Work-package 6 is pursuing the overall goal of " rigorously defining the basic concepts developed by MAFTIA, and verifying results of the work on dependable middle-ware. " In the former MAFTIA deliverable D4, we presented a general rigorous model for the security of reactive systems. This model comprised various types of faults (attacks) and topology as considered in MAFTIA, but was restricted to a synchronous timing model. In this deliverable, we focus on a model-variant for asynchronous reactive systems. This variant is highly important for MAFTIA, since several of the major MAFTIA middle-ware-protocols are asynchronous. To illustrate the use of the asynchronous model a proof of secure message transmission in the asynchronous case is included. We chose this example which delivers a similar service as the example from D4, to illustrate the analogies as well as the differences between the two variants of the secure reactive systems model. As in the synchronous model, we prove a composition theorem for its asynchronous counterpart, which allows modular proofs in this model. Furthermore, we discuss how to model adaptive corruptions in the presented models. Finally, we discuss the relation between the proposed models and the real world: Every model abstracts in certain ways from the real world and makes assumptions. So do the presented models of secure reactive systems. These abstractions are, on the one hand, necessary to enable reasoning about protocols at all. On the other hand, they can lead to insecure systems in the real world if they are naively implemented. Guided by the goal of secure real world systems, we present an assessment of the model's abstractions and discuss the possible impact on the real world security when implementing reactive systems which are proven secure in this model.
3‐D Density, Thermal, and Compositional Model of the Antarctic Lithosphere and Implications for Its Evolution

We create a 3‐D density, temperature, and composition model of the Antarctic lithosphere using an integrative approach combining gravity, topography, and tomography data with mineral physics constraints and seismic data on crustal structures. The latter is used to create a new Moho and crustal density model. Temperature and thermal density variations are estimated based on S wave velocities from two independent tomography models (SL2013sv and AN1‐S). Results of the Antarctic continent show the well‐known distinction between East and West Antarctica in temperature and density to a depth of about 200 km. Incorporating compositional variations in the temperature calculations increases temperatures in depleted regions by up to 150 °C, giving improved insights into thermal structures. The thickness of the lithospheric root also varies strongly between these regions, with values below 100 km in the west and above 200 km in the east. Regions with negative compositional density variations (<−0.040 g/cm3 at 100 km), high depletion (Mg # > 91.5), and low temperatures (<800 °C; central Dronning Maud Land, along the east flank of the Transantarctic Mountains) are interpreted as Precambrian cratonic fragments. Nearly undepleted lithosphere is found in the Lambert Graben and the Aurora Subglacial Basin and is attributed to Mesozoic rifting activity that has caused lithospheric rejuvenation.
GALOPP: Multi-Agent Deep Reinforcement Learning For Persistent Monitoring With Localization Constraints

: In this paper, we consider a persistent monitoring (PM) problem where 1 a team of agents need to persistently monitor a dynamic area under localization 2 and communication constraints. We assume the agents have limited communica-3 tion range and a subset of the agents (anchors) have accurate localization capabil-4 ity, while the rest of the agents (auxiliary agents) have lower localization accuracy. 5 To accurately localize itself, the auxiliary agents must be within the communica-6 tion range of the anchor agents, directly or indirectly. The agents have to coop-7 erate with each to persistently monitor the complete area by communicating their 8 current state to the other agents. Designing strategies for the agents is highly chal-9 lenging due to the conﬂicting objectives of complete coverage and the cooperation
Neural Network-Based Multiple Robot Simultaneous Localization and Mapping

In this paper, a decentralized platform for simultaneous localization and mapping (SLAM) with multiple robots is developed. Each robot performs single robot view-based SLAM using an extended Kalman filter to fuse data from two encoders and a laser ranger. To extend this approach to multiple robot SLAM, a novel occupancy grid map fusion algorithm is proposed. Map fusion is achieved through a multistep process that includes image preprocessing, map learning (clustering) using neural networks, relative orientation extraction using norm histogram cross correlation and a Radon transform, relative translation extraction using matching norm vectors, and then verification of the results. The proposed map learning method is a process based on the self-organizing map. In the learning phase, the obstacles of the map are learned by clustering the occupied cells of the map into clusters. The learning is an unsupervised process which can be done on the fly without any need to have output training patterns. The clusters represent the spatial form of the map and make further analyses of the map easier and faster. Also, clusters can be interpreted as features extracted from the occupancy grid map so the map fusion problem becomes a task of matching features. Results of the experiments from tests performed on a real environment with multiple robots prove the effectiveness of the proposed solution.
Toeholder: a Software for Automated Design and In Silico Validation of Toehold Riboswitches Authors and affiliations

Département de biochimie, microbiologie et bio-informatique, Université Laval, Quebec City, QC, Canada Département de biologie, Université Laval, Quebec City, QC, Canada Institut de biologie intégrative et des systèmes, Université Laval, Quebec City, QC, Canada Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de Québec, Quebec City, QC, Canada Centre de recherche en données massives de l’Université Laval, Université Laval, Quebec City, QC, Canada Regroupement québécois de recherche sur la fonction, l’ingénierie et la structure des protéines (PROTEO), Université Laval, Quebec City, QC, Canada
Real-Time Query Scheduling for Wireless Sensor Networks

Recent years have seen the emergence of wireless sensor network systems that must support high data rate and realtime queries of physical environments. This paper proposes Real-Time Query Scheduling (RTQS), a novel approach to conflict-free transmission scheduling for real-time queries in wireless sensor networks. First, we show that there is an inherent trade-off between prioritization and throughput in conflict-free query scheduling. We then present three new real-time scheduling algorithms. The non-preemptive query scheduling algorithm achieves high throughput while introducing priority inversions. The preemptive query scheduling algorithm eliminates priority inversion at the cost of reduced throughput. The slack stealing query scheduling algorithm combines the benefits of preemptive and non- preemptive scheduling by improving the throughput while meeting query deadlines. Furthermore, we provide schedulability analysis for each scheduling algorithm. The analysis and advantages of our scheduling algorithms are validated through NS2 simulations.
Language-Parametric Compiler Validation with Application to LLVM

Modern optimizing compilers such as LLVM [6] and GCC [3] are huge and complex, and mature releases routinely have uncaught bugs [15]. Beyond harm to software development, the lack of formal correctness guarantees for the compilation process seriously limits the guarantees other software systems can provide. For example, the seL4 operating system microkernel [5] had to use a custom compilation verification solution in order to provide formal correctness guarantees for the microkernel [14]. As another example, most iOS applications and all watchOS and tvOS applications are shipped by developers to the Apple Store as LLVM bitcode [1, 4] and compiled to machine code on Apple’s servers. This has raised concerns among developers that the machine code may not be identical to what they tested before shipping, exposing unexpected bugs [17]. Compilation verification can be valuable to increase confidence in the correctness of the final native code. In this work, we attack the problem of compilation verification: providing a formal guarantee that a compilation of a program is correct. We do that not only theoretically, as an instance of program equivalence, but from a practical standpoint as well: We aim for a solution suitable for production optimizing compilers such as LLVM. Full static verification of the compiler source code itself, e.g., as in CompCert [7], has only been shown to work for compilers designed specially from the ground up for formal proofs, and requires extensive proof engineering by formal methods experts (see Section 6). We pursue an alternative approach called Translation Validation, better suited to production compilers. Translation Validation (TV) [11] aims to prove correctness of a single compilation run, by considering only a specific pair of input and output programs. TV techniques are well suited to practical compilation verification because they can be retrofitted to production compilers and (in our approach) require relatively little formal methods expertise from compiler developers, once the operational semantics of compiler internal representations (IRs) are formally defined. There are three essential components of a TV system: 1. A formal notion of program equivalence. 2. A verification condition (VC) generator that generates a sufficient set of obligations to be discharged in order to prove equivalence. Verification conditions relate program points and variables in the input and output programs. 3. A proof system that accepts the verification conditions, generates a machine-checkable equivalence proof, and checks the proof for correctness. 2. Limitations of the State of the Art