Miracle-dz/UniFAR-FTUs huggingface dataset:
Network-Aware Feasible Repairs for Erasure-Coded Storage

A significant amount of research on using erasure coding for distributed storage has focused on reducing the amount of data that needs to be transferred to replace failed nodes. This continues to be an active topic as the introduction of faster storage devices looks to put an even greater strain on the network. However, with a few notable exceptions, most published work assumes a flat, static network topology between the nodes of the system. We propose a general framework to find the lowest cost feasible repairs in a more realistic, heterogeneous and dynamic network, and examine how the number of repair strategies to consider can be reduced for three distinct erasure codes. We devote a significant part of the paper to determining the set of feasible repairs for random linear network coding (RLNC) and describe a system of efficient checks using techniques from the arsenal of dynamic programming. Our solution involves decomposing the problem into smaller steps, memorizing, and then reusing intermediate results. All computationally intensive operations are performed prior to the failure of a node to ensure that the repair can start with minimal delay, based on up-to-date network information. We show that all three codes benefit from being network aware and find that the extra computations required for RLNC can be reduced to a viable level for a wide range of parameter values.
Implementation aspects of a pipeline ADMM-based LP decoding of LDPC convolutional codes

The enforcement of the linear programming (LP) decoding was recently extended to LDPC convolutional codes (LDPC-CC). It was demonstrated that their convolutional structure suites the message-passing based representation of the LP problem, thanks to the application of the alternating directions method of multipliers (ADMM). In this paper, a modified formulation of the ADMM-LP problem is introduced for pipeline decoding of LDPC-CCs based on the layered schedule. Moreover, an assessment of the fixed-point format required for hardware implementation is provided to evaluate the complexity of the underlying decoding algorithm. Simulations show that an 8-bit quantization scheme yields negligible error correction performances loss of about 0.05 dB regarding the floating-precision ADMM based LDPC-CC decoder. Further, an algorithmic optimization of the Euclidean projection skipping technique, described in [1] [2], is proposed. This improved version enables to reduce the computational complexity of the decoding process without memory penalty contrary to the original formulation.
Utility of state-level influenza disease burden and severity estimates to investigate an apparent increase in reported severe cases of influenza A(H1N1) pdm09 – Arizona, 2015–2016

Abstract The Arizona Department of Health Services identified unusually high levels of influenza activity and severe complications during the 2015–2016 influenza season leading to concerns about potential increased disease severity compared with prior seasons. We estimated state-level burden and severity to compare across three seasons using multiple data sources for community-level illness, hospitalisation and death. Severity ratios were calculated as the number of hospitalisations or deaths per community case. Community influenza-like illness rates, hospitalisation rates and mortality rates in 2015–2016 were higher than the previous two seasons. However, ratios of severe disease to community illness were similar. Arizona experienced overall increased disease burden in 2015–2016, but not increased severity compared with prior seasons. Timely estimates of state-specific burden and severity are potentially feasible and may provide important information during seemingly unusual influenza seasons or pandemic situations.
Design of 180nm CMOS linear temperature sensor

This paper presents a compact low power sub-threshold based temperature sensor. The proposed temperature sensor doesn't require any bipolar transistor(BJT) and it is based on the concept that the temperature is dependent on the threshold voltage of MOSFETs. The sensor is designed using MOS transistors, which are operated in sub-threshold region. The circuit has been simulated in Cadence tool with SCL180nm library using a power supply of ±1.25V. The circuit linearly senses the temperaturesfrom 0°C to 140°C, while consumes just 197nW at 1.25V power supply.
OFMAGNETIC VORTEX CORES IN PLANARMAGNETIC MATERIALS USING A VOLTAGE INDUCED STRAIN

The present work studies the effects of inducing a strain gradient on micron-sized Galfenol and nickel devices containing vortex cores by means of a piezoelectric element. Micromagnetic simulations show that the strain induced anisotropy gradient causes vortex core translation along the direction of the gradient, which is key to efficient excitation of the core dynamics. The effect of the strain gradient symmetry on the displacement of the core is investigated and the experimental implementation of the strain gradient is also discussed. For the latter, two approaches are considered. The first is to make use of the ferroelastic multi-domain state of a ferroelectric substrate. The second is based on tailoring a gradient in the strain by patterning two electrodes on top of the piezoelectric substrate.
Analysis of the use of genetic algorithms for indoor localisation via cloud point matching

A system's ability to precisely locate itself in a known physical environment is key to its capacity to interact with the environment in an intricate manner. The indoor localisation problem has been approached in a variety of ways, ranging from the identification of pre-defined features or topologies to the more general cloud-point matching. Cloud point matching can be achieved using a variety of algorithms, each with benefits and drawbacks. Recent improvements have focused on the application of genetic algorithms to solve the initial 'global' search for a solution, before refining this solution to a precise position through a non-genetic algorithm. This project aims to demonstrate the inefficiency of genetic algorithms applied to the global search problem for the issue of indoor localisation; this is thought to be caused by the solution space's low dimensionality, solution landscape topology and the inefficacy of crossover operators in the algorithm. Based on our assumptions of map topologies, we conclude that significant redundancies can be found in some purely genetic heuristics and suggest further development of landscape analysis to allow the use of algorithms appropriate to the scenario's complexity.
A Lattice-Partition Framework of Downlink Non-Orthogonal Multiple Access Without SIC

In this paper, a novel lattice-partition-based downlink non-orthogonal multiple access framework is proposed. This framework is motivated by recognizing the algebraic structure behind the previous scheme recently proposed by Shieh and Huang as a lattice partition in <inline-formula> <tex-math notation="LaTeX">$ \mathbb {Z}$ </tex-math></inline-formula> and is in fact a generalization of the scheme to any base lattice. The schemes in the proposed framework enjoy many desirable properties such as explicit and systematic design and discrete input distributions. Moreover, the proposed method only requires a limited knowledge of channel parameters. The rates achieved by the proposed scheme with <italic>any base lattice</italic> and with single-user decoding (i.e., without successive interference cancellation) are analyzed, and a universal upper bound on the gap to the multiuser capacity is obtained as a function of the normalized second moment of the base lattice. Since the proposed framework has a substantially larger design space than that of the previous scheme of Shieh and Huang whose base lattice is a 1-D lattice, one can easily find instances in larger dimensions that can provide superior performance. Design examples with the base lattices <inline-formula> <tex-math notation="LaTeX">$A_{2}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$D_{4}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$E_{8}$ </tex-math></inline-formula>, and Construction A lattices, respectively, are provided, and both theoretical and simulation results exhibit smaller gaps to the multiuser capacity as dimensions increase.
EnVision-a Vision System for Envirobot Hardware Aspects Spring Semester 2018

EnVision is a computer vision system based on a spiking neural network (SNN) for the Envirobot. The Envirobot was developed at the Biorobotics Laboratory at EPFL Lausanne [1]. It is a snakelike swimming robot with an anguilliform type of locomotion. This project was inspired by a study of the Department of Computational Biology at KTH Royal Institute of Technology in Stockholm [2] which aims to establish a computational model of the behavior based on visual stimuli of a lamprey. Three basic behaviors escape, avoidance and approach were considered in this work. For each of those behaviors, one specific color is used as visual stimuli which triggers a certain behavior when this color is detected. The work to carry out by the Biorobotics Laboratory was firstly the development of a webots based simulation in order to observe the behavior of the lamprey following this computational model [3]. The next goal is the implementation of this simulation on the hardware Envirobot. The present paper describes the hardware aspects which are necessary to enable the integration of EnVision in the Envirobot.
Demand power forecasting with data mining method in smart grid

Nowadays increasing electricity demand is a key issue. As the demand is increasing day by day, obtaining energy efficiency is also getting important. Hence developing accurate demand forecasting methods is crucial for ensuring energy efficiency through efficient system operation. In this paper, we suggested a demand forecasting method with data mining techniques. We proposed a hybrid method which combined K-means clustering, Bayesian classification and ARIMA. Most of the previous research tried to solve this issue from supply side management but here in this paper the proposed forecasting model works on consumer side. Case study has been carried out with actual load profile from Jeju island, South Korea. The minimum error rate is 0.1853 from proposed Hybrid Model. The performance of the proposed model was also compared with the Neural Network based forecasting. The comparison shows better performance of proposed model compared to Neural Network.
Detection of lesion in mammograms

Breast cancer is one of the most dangerous diseases that causes countless mortals among women. In fact, the early detection of this illness is the only way to reduce the mortality rate. Several studies have been carried out, over the last two decades, to develop tools for diagnosis (CAD). In this context, we present a method to detect breast cancer based on three steps. The first one is a pre-processing stage in which histogram equalization and adaptive limited contrast are used. The second step is image segmentation relying on the application of histogram thresholding method, while the third step employing the co-occurrence matrix consists in characteristics extraction. The proposed method was tested on several mammograms from the mini-MIAS mammographic database.
Visually grounded cross-lingual keyword spotting in speech

Recent work considered how images paired with speech can be used as supervision for building speech systems when transcriptions are not available. We ask whether visual grounding can be used for cross-lingual keyword spotting: given a text keyword in one language, the task is to retrieve spoken utterances containing that keyword in another language. This could enable searching through speech in a low-resource language using text queries in a high-resource language. As a proof-of-concept, we use English speech with German queries: we use a German visual tagger to add keyword labels to each training image, and then train a neural network to map English speech to German keywords. Without seeing parallel speech-transcriptions or translations, the model achieves a precision at ten of 58%. We show that most erroneous retrievals contain equivalent or semantically relevant keywords; excluding these would improve P@10 to 91%.
Wearable Assistive Technologies for Autism: Opportunities and Challenges

Autism is a lifelong developmental condition that affects how people perceive the world and interact with others. Challenges with typical social engagement, common in the autism experience, can have a significant negative impact on the quality of life of individuals and families living with autism. Recent advances in sensing, intelligent, and interactive technologies can enable new forms of assistive and augmentative technologies to support social interactions. However, researchers have not yet demonstrated effectiveness of these technologies in long-term real-world use. This article presents an overview of the social and sensory challenges of autism, which offer great opportunities and challenges for the design and development of assistive technologies. We review existing work on developing wearable technologies for autism particularly to assist social interactions, analyze their potential and limitations, and discuss future research directions.
SODA: Strategy-Proof Online Double Auction Scheme for Multimicrogrids Bidding

In this paper, we present theory and a design of the online double auction for the trading of energy within a smart grid with microgrids (MGs). The online double auction has the potential to enable the allocation of surplus electricity to the MGs that need electricity with the highest gain in the real-time market. Nonetheless, two critical issues remain challenging when designing an effective online double auction scheme in such a system. First, as the agents are allowed to arrive and depart at any time, the auctioneer needs to make decisions without the information of further bids and asks. Second, the economic properties of strategy-proof, individual rational, and (weak) budget balance should be satisfied. To address these issues and enable multiunit electricity trading among local MGs, in this paper, we propose a strategy-proof online double auction (SODA) scheme, in which the surplus and insufficient MGs in the system are treated as sellers and buyers, respectively, and the MG center controller is capable of maximizing the social welfare of MGs by appropriately matching buyers and sellers. Via theoretical analysis, we prove that SODA can achieve the properties of individual rationality, (weak) budget balance, strategy-proofness, and computational efficiency. Experiments also show that SODA is capable of reducing the energy purchasing cost of the MGs and shifting the peak-load, while achieving great performance with respect to social welfare, seller/buyer satisfaction ratio, social efficiency, and computation overhead.
A novel face detection approach using local binary pattern histogram and support vector machine

Several applications including access control, system security need a robust and real-time face identification systems. The last is almost performed by two processes: the detection of the face and then the recognition. In literature, it can be found that some techniques are used exclusively for the detection process and some others are dedicated for the recognition. In this paper, we propose to test and evaluate a common technique for both processes. More particularly, we consider in this paper only the detection process. In order to do that, we have investigated three descriptors: Local Binary Pattern (LBP), Local Binary Pattern Histogram (LBPH), and Histogram Of Gradient (HOG) to get features of faces. These features are used for training process with the same Support Vector Machine classifier taken in different conditions in order to have training dataset. The use of LBPH desciptor followed by SVM classifier for the detection process consitite the second contribution of the paper. Two sets composed of negative images (non-faces) and positive images (faces) are used to generate the training dataset. The test is performed on another dataset. We evaluate the three techniques for face detection on the standard FERET datasets in order to compare the efficiency of these three methods under different face variations and different illuminations. The Local Binary Pattern Histogram with Support Vector Machine classifier outperform the others techniques in terms of efficiency and robustness of face detection. It has been found that the correct detection rate is greater than 98.04% in average.
Endoscopic optical coherence tomography with a flexible fiber bundle

Abstract. We demonstrate in vivo endoscopic optical coherence tomography (OCT) imaging in the forward direction using a flexible fiber bundle (FB). In comparison to current conventional forward-looking probe schemes, our approach simplifies the endoscope design by avoiding the integration of any beam steering components in the distal probe end due to two-dimensional scanning of a focused light beam over the proximal FB surface. We describe the challenges that arise when OCT imaging with an FB is performed, such as multimoding or cross coupling. The performance of different FBs varying in parameters, such as numerical aperture, core size, core structure, and flexibility, was consequently compared, and image quality degrading artifacts were described in detail. Based on our findings, we propose an optimal FB design for endoscopic OCT imaging.
Application of a JA-Ile Biosynthesis Inhibitor to Methyl Jasmonate-Treated Strawberry Fruit Induces Upregulation of Specific MBW Complex-Related Genes and Accumulation of Proanthocyanidins

Fleshy fruits are an important source of anthocyanins and proanthocyanidins (PAs), which protect plants against stress, and their consumption provides beneficial effects for human health. In strawberry fruit, the application of exogenous methyl jasmonate (MeJA) upregulates anthocyanin accumulation, although the relationship between the jasmonate pathway and anthocyanin and PA biosynthesis in fruits remains to be understood. Anthocyanin and PA accumulation is mainly regulated at the transcriptional level through R2R3-MYB and bHLH transcription factors in different plant species and organs. Here, the effect of jarin-1, a specific inhibitor of bioactive JA (jasmonoyl-isoleucine, JA-Ile) biosynthesis, on anthocyanin and PA accumulation was evaluated during strawberry (Fragaria × ananassa) fruit development using an in vitro ripening system for 48 h. Also, we observed the effects of MeJA and the application of jarin-1 to MeJA-treated fruits (MeJA + jarin-1 treatment). We assessed changes of expression levels for the JA-Ile and MeJA biosynthetic (FaJAR1.2 and FaJMT), JA signaling-related (FaMYC2 and FaJAZ1), MYB-bHLH-WD40 (MBW) complex-related (FabHLH3/33, FaMYB9/10/11, and repressor FaMYB1), and anthocyanin and PA biosynthetic (FaANS, FaUFGT, FaANR, and FaLAR) genes. In addition, the promoter region of MBW complex-related MYB genes was isolated and sequenced. We found a higher redness of strawberry fruit skin and anthocyanin content in MeJA-treated fruits with respect to jarin-1-treated ones concomitant with an upregulation of FaANS and FaUFGT genes. Inversely, the PA content was higher in jarin-1- and MeJA + jarin-1-treated than in MeJA-treated fruits. MeJA + jarin-1 treatment resulted in an upregulation of FaANR and associated transcription factors such as FabHLH33 and FaMYB9/11 along with FaJMT and FaJAR1.2. Finally, we found JA-responsive elements in the promoter regions of FaMYB1/9/10/11 genes. It is proposed that PA biosynthesis-related genes can be upregulated by the application of jarin-1 to MeJA-treated fruit, thus increasing PA accumulation in strawberry.
Transactive Response DNA-Binding Protein 43 Abnormalities after Traumatic Brain Injury.

Initial studies have found some evidence for transactive response DNA-binding protein 43 (TDP-43) abnormalities after traumatic brain injury (TBI), and the presence of protein inclusions consisting of TDP-43 are a pathological hallmark of amyotrophic lateral sclerosis (ALS), a condition associated with TBI. However, no study has characterized changes in TDP-43 phosphorylation, mislocalization, and fragmentation (i.e., abnormalities linked to hallmark TDP-43 pathology) after TBI, and how these relate to functional outcomes. Further, how TBI affects an individual with a known predisposition to TDP-43 pathology is unknown. Therefore, this study examined the effects of TBI on TDP-43 post-translational processing, localization, and behavioral outcomes in wild-type (WT) mice and mutant TDP-43A315T mice (i.e., mice predisposed to TDP-43 pathology) at 24 h and 1 week after TBI. Post-mortem brain tissue from human patients with acute TBI was also examined. Western blots found that WT mice given TBI had increased TDP-43 phosphorylation, mislocalization, and fragmentation compared with sham-injured WT mice. The TDP-43A315T mice given a TBI had exacerbated TDP-43 abnormalities, worse cell death, and cognitive deficits compared with all other groups. In the human TBI patients, the only significant finding was increased nuclear accumulation of phosphorylated TDP-43 fragments. The discrepancy between the robust mouse findings and the largely non-significant human findings may be due to factors including heterogeneity in clinical TBI, the small group sizes, and temporal complexities with TDP-43 abnormalities. These findings indicate that TBI can induce a number of TDP-43 abnormalities that may contribute to the neurological consequences of TBI, though further research is still needed.
Evaluation of low power wide area network technologies for smart urban drainage systems

Smart urban drainage systems (SUDS) with real-time control are considered to be one of the prominent approaches to tackle the impact of climate change which results in heavy rainfall and floods in urban areas. However, the lack of reliable and efficient communication with sensors buried underground is considered as one of the main deficiencies preventing ubiquitous application of SUDS. With the recent developments of new noise insensitive technologies for ultra-narrow band (UNB) and chirp spread spectrum (CSS) communication, introducing new low-power long-range communication technologies (e.g. NB-IoT, LoRa), implementation of such drainage systems is becoming feasible. This paper presents a comparative study of NB-IoT and LoRa and evaluate the potential of these technologies to be used for SUDS. The study is conducted in different underground scenarios such as dry and damp conditions to highlight the potential benefits offered by LoRa and NB-IoT in terms of coverage, information rate, and energy consumption. It has been shown through the simulation results that NB-IoT offers capillary coverage with a high level of scalability as compared to LoRa. Furthermore, NB-IoT also provide high information rate and low energy consumption and is best suited for underground scenarios.
Development of architecture of visual program system for distributed data processing in software defined networks

At present days innovative approaches based on the technology of software defined networks (SDN) are widely used in organizing of work of computer networks (CN). In SDN rules of pipeline processing of data are transferred from switch to controller. The key features of SDN — functional division of levels of transmission and management of data, network virtualization and automation with using software applications for controller. The purpose of this work is the development of architecture of visual program system for distributed processing of data in SDN. In the work the review of the developed program system is executed and also questions of its application as a part of program infrastructure of dynamic configuring and control of SDN are considered. The description of architecture of the developed program system is executed on the basis of UML class diagrams.