Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Operations with the Use of Differential Codes

This paper presents the new approach to a rate increase of multiplication, which is the most commonly used operation in data computation. Multiplications are usually done in multibit Pulse Code Modulation (PCM) format. However, applying this format is connected with doing many time-consuming operations what often causes the increase of the total calculation time. On the other hand, mixed formats which join differential codes with PCM codes can lead to the decrease of code word length and speed-up operation in the data computation. Differential codes can be obtained on the basis of differential kinds of Delta Modulation (DM). Some kinds of DM such as MDPCM (Modified DPCM) and SDPCM (Synthetic DPCM) enable to replace the multiplications with fast shift operations.
Influence of Rotor Pole Number on Electromagnetic Performance of Double-Stator Switched Flux PM Machines

This paper investigates the influence of rotor pole number on the electromagnetic performance of double-stator switched flux permanent magnet (DS-SFPM) machines. Both stators of the developed machines consist of doubly-salient 6-stator poles, whilst the machines with 10-, 11-, 13- and 14-rotor poles are selected for optimization and comparison. Moreover, the influence of leading design parameters such as the split ratio, PM thickness, rotor-iron arc/pitch ratio and back-iron thickness on the average torque is also investigated. The analyses include the open-circuit coil and phase flux linkages, and the back-EMF waveforms and spectra, the load characteristics, the flux weakening capability and inductance characteristics. The predicted results show that the developed machine with 13-rotor pole number exhibits the largest fundamental back-EMF and thus the largest torque density amongst its counterparts. A prototype is tested for validation.
In-building communication using massively distributed wireless microdevice network

A new concept is proposed for establishing a wireless communication link between two terminals and avoiding multipath interference. This low level protocol uses a network of periodically deployed microdevices acting as autonomous nodes, without any supervising entity. Nodes are neither primary sources of information nor routers. They act as repeaters with very limited processing power. No frames are used: the communication protocol works at the symbol level. In a first step, the minimum propagation time is measured by the terminals. Then the nodes situated along the corresponding shortest route are activated. Finally, information is exchanged using a full-duplex scheme.
Fraud Detection for Online Retail using Random Forests

As online commerce becomes more common, fraud is an increasingly important concern. Current anti-fraud techniques include blacklists, hand-crafted rules, and review of individual transactions by human experts. These methods have achieved good results, but a significant number of costly fraudulent transactions still occur, and the fraud detection process is expensive due to its heavy reliance on human experts. Our goal is to improve the quality of fraud detection and decrease the need for costly human analysis of individual transactions. We propose two modifications to the approval process for an online retailer based on a Random Forests classifier.
Formations for Fast Locomotion of Metamorphic Robotic Systems

In this paper, we examine the problem of dynamic self-reconfiguration of a modular robotic system (frequently referred to as a self-reconfigurable or metamorphic system), to a formation aimed at reaching a specified target position as quickly as possible. We present a number of fast formations for both rectangular and hexagonal systems, and prove upper and lower bounds on the speed of locomotion. In particular, the formations presented here achieve a constant ratio guarantee on the time to reach a given target in the asymptotic sense.
Content based Video Retrieval Systems - Methods, Techniques, Trends and Challenges

Content Based Video Retrieval (CBVR) has been increasingly used to describe the process of retrieving desired videos from a large collection on the basis of features that are extracted from the videos. The extracted features are used to index, classify and retrieve desired and relevant videos while filtering out undesired ones. Videos can be represented by their audio, texts, faces and objects in their frames. An individual video possesses unique motion features, color histograms, motion histograms, text features, audio features, features extracted from faces and objects existing in its frames. Videos containing useful information and occupying significant space in the databases are under-utilized unless CBVR systems capable of retrieving desired videos by sharply selecting relevant while filtering out undesired videos exist. Results have shown performance improvement (higher precision and recall values) when features suitable to particular types of videos are utilized wisely. Various combinations of these features can also be used to achieve desired performance. In this paper a complex and wide area of CBVR and CBVR systems has been presented in a comprehensive and simple way. Processes at different stages in CBVR systems are described in a systematic way. Types of features, their combinations and their utilization methods, techniques and algorithms are also shown. Various querying methods, some of the features like GLCM, Gabor Magnitude, algorithm to obtain similarity like Kullback-Leibler distance method and Relevance Feedback Method are discussed.
Brief Mist-netting and Update of New Record of Bats at Tumunong Hallu in Silam Coast Conservation Area (SCCA), Lahad Datu, Sabah, Malaysia

A bat survey was conducted at Tumunong Hallu in Silam Coast Conservation Area (SCCA), Lahad Datu, Sabahfollowing the Silam Scientific Expedition 2015 from 7th July until 5th August 2015. A total of nine bat species belonging to two families were captured at SCCA. Among the noteworthy species recorded from this survey were Rhinolophus sedulus and Pteropus vampyrus, which are listed as Near Threatened in the IUCN Red List.
Use of Computational Modeling to Predict Responses to Upper Airway Surgery in Obstructive Sleep Apnea

Objectives. Despite the well‐recognized consequences of obstructive sleep apnea (OSA), its treatment remains unsatisfactory. Therapeutic strategies are complicated by often poor adherence in the case of continuous positive airway pressure or the highly variable efficacy in the case of many upper airway surgeries. Computational models of the upper airway using finite element analysis to simulate the effects of various anatomic and physiologic manipulations on pharyngeal mechanics could be helpful in predicting surgical success.
New Dinucleoside Analogues via Cross-Coupling Metathesis

Synthesis of 3′-3′, 5′-5′, and 3′-5′ dimeric thymidine, linked by an olefinic chain between glycosidic moieties is described. Cross metathesis reaction of 3′ or 5′ O-allyl analogues of thymidine led to the expected 3′-3′ and 5′-5′ dimeric compounds, respectively. In order to obtain the 3′-5′ dimer, 5′-O-allyl and 3′-O-allyl monomers were first linked by their free 3′ OH and 5′ OH groups through a glutaryl spacer; ring closing metathesis was then operated upon this temporary dimer, followed by glutaryl removal. #Financial support from the “Conseil Regional du Limousin” is gratefully acknowledged. The authors are grateful to Dr. Michel Guilloton for useful comments on the manuscript.
Energy storage scheduling for imbalance reduction of strategically formed balancing groups

A Power Producer and Supplier (PPS) in the deregulated energy market requires forward market interactions to procure energy as precisely as possible in order to reduce imbalance energy (on-line demand vs. supply gap). This paper presents, 1) (off-line) an effective demand aggregation based strategy for creating a number of balancing groups that leads to higher predictability of group-wise demand, 2) (on-line) a robust energy storage scheduling that minimizes the imbalance for a particular balancing group considering the demand prediction uncertainty. The group formation is performed by a probabilistic programming approach using Bayesian Markov Chain Monte Carlo (MCMC) method, applied on the historical demand statistics. An upper-limit of the storage rating/capacity is derived as a by-product of Bayesian MCMC, which is utilized in the on-line operation. A robust energy storage scheduling method is proposed that minimizes the on-line imbalance energy and cost (a non-linear function of imbalance energy) while incorporating the demand uncertainty of a particular group. The proposed methods are applied on the real apartment buildings' demand data in Tokyo (Japan). Simulation results are presented to verify the effectiveness of the proposed methods.
Belief Propagation and LP Relaxation for Weighted Matching in General Graphs

Loopy belief propagation has been employed in a wide variety of applications with great empirical success, but it comes with few theoretical guarantees. In this paper, we analyze the performance of the max-product form of belief propagation for the weighted matching problem on general graphs. We show that the performance of max-product is exactly characterized by the natural linear programming (LP) relaxation of the problem. In particular, we first show that if the LP relaxation has no fractional optima then max-product always converges to the correct answer. This establishes the extension of the recent result by Bayati, Shah and Sharma, which considered bipartite graphs, to general graphs. Perhaps more interestingly, we also establish a tight converse, namely that the presence of any fractional LP optimum implies that max-product will fail to yield useful estimates on some of the edges. We extend our results to the weighted b-matching and r -edge-cover problems. We also demonstrate how to simplify the max-product message-update equations for weighted matching, making it easily deployable in distributed settings like wireless or sensor networks.
Castor oil polyurethane containing silica nanoparticles as filling material of bone defect in rats.

PURPOSE To evaluate the biologic behavior of the castor polymer containing silica nanoparticles as a bone substitute in diafisary defect. METHODS Twenty seven male Rattus norvegicus albinus Wistar lineage were submitted to bone defect filled with castor oil polymer. Three experimental groups had been formed with nine animals each: (1) castor oil polymer containing only calcium carbonate; (2) castor oil polymer with calcium carbonate and doped with 5% of silica nanoparticles; (3) castor polymer with calcium carbonate doped with 10% of silica nanoparticles; 3 animals of each group were submitted to euthanasia 15, 30 and 60 days after experimental procedure, and their femurs were removed to histological evaluation. RESULTS there was bone growth in all the studied groups, with a greater tendency of growth in the group 1. After 30 days all the groups presented similar results. After 60 days a greater amount of fibroblasts, osteoblasts, osteocytes and osteoclasts in group 3 was observed, with integrated activity of 3 kinds of cells involved in the bone activation-reabsorption-formation. CONCLUSIONS The castor polymer associated to the silica nanoparticles is biocompatible and allows osteoconduction. The presence of osteoprogenitors cells suggests silica osteoinduction capacity.
Large‐scale distribution of microbial and viral populations in the South Atlantic Ocean

Summary Viruses are abundant, diverse and dynamic components of the marine environments and play a significant role in the ocean biogeochemical cycles. To assess potential variations in the relation between viruses and microbes in different geographic regions and depths, viral and microbial abundance and production were determined throughout the water column along a latitudinal transect in the South Atlantic Ocean. Path analysis was used to examine the relationships between several abiotic and biotic parameters and the different microbial and viral populations distinguished by flow cytometry. The depth‐integrated contribution of microbial and viral abundance to the total microbial and viral biomass differed significantly among the different provinces. Additionally, the virus‐to‐microbe ratio increased with depth and decreased laterally towards the more productive regions. Our data revealed that the abundance of phytoplankton and microbes is the main controlling factor of the viral populations in the euphotic and mesopelagic layers, whereas in the bathypelagic realm, viral abundance was only weakly related to the biotic and abiotic variables. The relative contribution of the three viral populations distinguished by flow cytometry showed a clear geographical pattern throughout the water column, suggesting that these populations are composed of distinct taxa able to infect specific hosts. Overall, our data indicate the presence of distinct microbial patterns along the latitudinal transect. This variability is not limited to the euphotic layer but also detectable in the meso‐ and bathypelagic layers.
Visualization of the polarity of isolated titin molecules: a single globular head on a long thin rod as the M band anchoring domain?

TII, the extractable form of titin, was purified from myofibrils and separated by high resolution gel permeation chromatography into two fractions (TIIA and TIIB). Novel specimen orientation methods used before metal shadowing and EM result in striking pictures of the two forms. Molecules layered on mica become uniformly oriented when subjected to centrifugation. TIIB comprises a very homogeneous fraction. All molecules reveal a single globular head at one end on a long and very thin rod of uniform diameter. The lengths of the rods have a very narrow distribution (900 +/- 50 nm). TIIA molecules seem lateral oligomers of TIIB, attached to each other via the head regions. While dimers are the predominant species, trimers and some higher oligomers can also be discerned. Mild proteolysis destroys the heads and converts TIIA and TIIB into TIIB-like rods. Similar molecules also result from titin purified from myofibrils by certain established purification schemes. Headless titin molecules show in gel electrophoresis only the TII band, while head bearing molecules give rise to two additional polypeptides at 165 and 190 kD. Immunoelectron microscopy of myofibrils identifies both titin-associated proteins as M band constituents. We speculate that in the polar images of TII the globular head region corresponds to the M band end of the titin molecules. This hypothesis is supported by immunoelectron micrographs of TIIB molecules using titin antibodies of known epitope location in the half sarcomere. This proposal complements our previous immunoelectron microscopic data on myofibrils. They showed that epitopes present only on the nonextractable TI species locate to the Z line and its immediately adjacent region (Furst, D. O., M. Osborn, R. Nave, and K. Weber. 1988. J. Cell Biol. 106:1563-1572). Thus, the two distinct ends of the titin molecule attach to Z and M band material respectively.
Linear Response Algorithms for Approximate Inference in Graphical Models

Belief propagation (BP) on cyclic graphs is an efficient algorithm for computing approximate marginal probability distributions over single nodes and neighboring nodes in the graph. However, it does not prescribe a way to compute joint distributions over pairs of distant nodes in the graph. In this article, we propose two new algorithms for approximating these pairwise probabilities, based on the linear response theorem. The first is a propagation algorithm that is shown to converge if BP converges to a stable fixed point. The second algorithm is based on matrix inversion. Applying these ideas to gaussian random fields, we derive a propagation algorithm for computing the inverse of a matrix.
Workload analysis of a large-scale key-value store

Key-value stores are a vital component in many scale-out enterprises, including social networks, online retail, and risk analysis. Accordingly, they are receiving increased attention from the research community in an effort to improve their performance, scalability, reliability, cost, and power consumption. To be effective, such efforts require a detailed understanding of realistic key-value workloads. And yet little is known about these workloads outside of the companies that operate them. This paper aims to address this gap. To this end, we have collected detailed traces from Facebook's Memcached deployment, arguably the world's largest. The traces capture over 284 billion requests from five different Memcached use cases over several days. We analyze the workloads from multiple angles, including: request composition, size, and rate; cache efficacy; temporal patterns; and application use cases. We also propose a simple model of the most representative trace to enable the generation of more realistic synthetic workloads by the community. Our analysis details many characteristics of the caching workload. It also reveals a number of surprises: a GET/SET ratio of 30:1 that is higher than assumed in the literature; some applications of Memcached behave more like persistent storage than a cache; strong locality metrics, such as keys accessed many millions of times a day, do not always suffice for a high hit rate; and there is still room for efficiency and hit rate improvements in Memcached's implementation. Toward the last point, we make several suggestions that address the exposed deficiencies.
ODMAC: an on-demand MAC protocol for energy harvesting - wireless sensor networks

Energy Harvesting (EH) provides a promising solution to one of the biggest problems faced by Wireless Sensor Networks (WSN), namely the energy supply. By harvesting energy from the surrounding environment, the sensors can have an in finite lifetime without any needs for battery recharge or replacement. Battery-powered WSNs are typically designed to maximize the energy conservation in order to postpone as much as possible the inevitable battery depletion. Instead, EH-WSNs are being designed on a different principle. The focus is on maximizing the network performance while operating at a state that is energetically sustainable. In this paper, we present ODMAC, an on demand MAC protocol for EH-WSNs which is able to support individual duty cycles for nodes with different energy profiles. Hence, each node is able to increase its energy consumption, thus its performance, to the level that the energy consumed is at the same level to the energy harvested. The protocol is implemented and evaluated using the OPNET simulator.
NCSOStools: a computer algebra system for symbolic and numerical computation with noncommutative polynomials

NCSOStools is a Matlab toolbox for • symbolic computation with polynomials in noncommuting (NC) variables; • constructing and solving sum of Hermitian squares (with commutators) programs for polynomials in NC variables. It can be used in combination with semidefinite programming software, such as SeDuMi, SDPA or SDPT3, to solve these constructed programs. This paper provides an overview of the theoretical underpinning of the sum of Hermitian squares (with commutators) programs and provides a gentle introduction to the primary features of NCSOStools.
Energy Aware Scheduling for Unrelated Parallel Machines

We consider the problem of energy aware scheduling of a set of jobs on a set of unrelated parallel machines with the average weighted completion time plus energy objective. The processing time and the energy consumption of the jobs are machine and speed dependent. Also, every job is subject to a machine-dependent release date. Firstly, we aim to find a non-preemptive schedule of the jobs minimizing the average weighted completion time plus energy, and we propose a randomized approximation algorithm that we derandomize obtaining a deterministic approximation algorithm. We then consider the budget variant of the problem where the objective is to minimize the average completion time while the total energy consumption does not exceed a given budget.
Recent Changes in the Occurrences and Damages of Floods and Droughts in the United States

In this study, we compile and analyse 136,920 records of flood and 50,967 records of drought from a survey-based database to investigate recent changes in annual occurrence and economical cost in the United States. Results show that an average of 6520 floods has occurred per year during 1996–2016, with annual mean economic losses up to 3986 million US dollars, while 2427 drought events/year are recorded causing an average loss of 1684 million US dollars per year. Importantly, we found there is no evident changing tendency in annual economic damages of floods and droughts, despite an upward trend in their annual occurrences. This could be partly explained by changes in regional vulnerabilities, as indirectly reflected by the ratio of damaging events to total number of events experienced and the average damage per event. Spatially, vulnerability to droughts has decreased in most of the country, while increased vulnerability to floods is observed in a number of states. Despite limitations from the records and incomplete characterization of vulnerability, this study has great implications for targeted mitigation and adaptation, through identifying the regions that are most vulnerable to floods and droughts respectively and highlighting the contrasting patterns in regional vulnerability to floods and droughts.
GPGPU performance and power estimation using machine learning

Graphics Processing Units (GPUs) have numerous configuration and design options, including core frequency, number of parallel compute units (CUs), and available memory bandwidth. At many stages of the design process, it is important to estimate how application performance and power are impacted by these options. This paper describes a GPU performance and power estimation model that uses machine learning techniques on measurements from real GPU hardware. The model is trained on a collection of applications that are run at numerous different hardware configurations. From the measured performance and power data, the model learns how applications scale as the GPU's configuration is changed. Hardware performance counter values are then gathered when running a new application on a single GPU configuration. These dynamic counter values are fed into a neural network that predicts which scaling curve from the training data best represents this kernel. This scaling curve is then used to estimate the performance and power of the new application at different GPU configurations. Over an 8× range of the number of CUs, a 3.3× range of core frequencies, and a 2.9× range of memory bandwidth, our model's performance and power estimates are accurate to within 15% and 10% of real hardware, respectively. This is comparable to the accuracy of cycle-level simulators. However, after an initial training phase, our model runs as fast as, or faster than the program running natively on real hardware.
SMILE : Twitter emotion classification using domain adaptation

Despite the widely spread research interest in social media sentiment analysis, sentiment and emotion classification across different domains and on Twitter data remains a challenging task. Here we set out to find an effective approach for tackling a cross-domain emotion classification task on a set of Twitter data involving social media discourse around arts and cultural experiences, in the context of museums. While most existing work in domain adaptation has focused on feature-based or/and instance-based adaptation methods, in this work we study a model-based adaptive SVM approach as we believe its flexibility and efficiency is more suitable for the task at hand. We conduct a series of experiments and compare our system with a set of baseline methods. Our results not only show a superior performance in terms of accuracy and computational efficiency compared to the baselines, but also shed light on how different ratios of labelled target-domain data used for adaptation can affect classification performance.