Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid PSO-SA algorithm for training a Neural Network for Classification

In this work, we propose a Hybrid particle swarm optimization-Simulated annealing algorithm and present a comparison with i) Simulated annealing algorithm and ii) Back propagation algorithm for training neural networks. These neural networks were then tested on a classification task. In particle swarm optimization behaviour of a particle is influenced by the experiential knowledge of the particle as well as socially exchanged information. Particle swarm optimization follows a parallel search strategy. In simulated annealing uphill moves are made in the search space in a stochastic fashion in addition to the downhill moves. Simulated annealing therefore has better scope of escaping local minima and reach a global minimum in the search space. Thus simulated annealing gives a selective randomness to the search. Back propagation algorithm uses gradient descent approach search for minimizing the error. Our goal of global minima in the task being done here is to come to lowest energy state, where energy state is being modelled as the sum of the squares of the error between the target and observed output values for all the training samples. We compared the performance of the neural networks of identical architectures trained by the i) Hybrid particle swarm optimization-simulated annealing, ii) Simulated annealing and iii) Back propagation algorithms respectively on a classification task and noted the results obtained. Neural network trained by Hybrid particle swarm optimization-simulated annealing has given better results compared to the neural networks trained by the Simulated annealing and Back propagation algorithms in the tests conducted by us.
New Algorithm to Control TCP Behavior over Lossy Links

TCP (Transmission Control Protocol) is one of the most used transport protocol in the Internet. Many widely used applications use TCP for sending data like the File Transfer Protocol (FTP), Telnet and Web-HTTP connections. TCP was found to be performing poorly over lossy links when transmission (non-congestion) errors existed. This is due to the fact that TCP is unable to distinguish between congestion and transmission errors, and hence reduces its sending rate for all errors, assuming a congestion exists in the connection path. One solution is to discriminate between errors and deal with each error type differently. In this paper we present a new TCP congestion window cut algorithm to be used by any end-to-end TCP error discriminator when transmission errors occur. Instead of cutting the congestion window to half (like standard TCP) we delay the cut decision until TCP receive all duplicate acknowledgment for a given window of data (packets on flight). This will give TCP a clear image about the number of drops from this window. Then congestion window size is reduced only by number of dropped packets. Using this approach, the new algorithm managed to improve TCP performance in non-aggressive way by increasing the average congestion window size. On the other hand, this algorithm cut the congestion window for both congestion and non-congestion errors which help to reduce the effect of error mismatch on the network. Simulation results shows noticeable improvement when the new technique is added to an error discriminator. Also we discusses some limitations of the new algorithm.
Arbitraging Intraday Wholesale Energy Market Prices With Aggregations of Thermostatic Loads

We investigate the potential for aggregations of residential thermostatically controlled loads (TCLs), such as air conditioners, to arbitrage intraday wholesale electricity market prices via non-disruptive load control. We present two arbitrage approaches: 1) a benchmark that gives us an optimal policy but requires local computation or real-time communication and 2) an alternative based on a thermal energy storage model, which relies on less computation/communication infrastructure, but is suboptimal. We find that the alternative approach achieves around 60%-80% of the optimal wholesale energy cost savings. We use this approach to compute practical upper bounds for savings via arbitrage with air conditioners in California's intraday energy market. We investigate six sites over four years and find that the savings range from $2-$37 per TCL per year, and depend upon outdoor temperature statistics and price volatility.
The essential and downstream common proteins of amyotrophic lateral sclerosis: A protein-protein interaction network analysis

Amyotrophic Lateral Sclerosis (ALS) is a devastative neurodegenerative disease characterized by selective loss of motoneurons. While several breakthroughs have been made in identifying ALS genetic defects, the detailed molecular mechanisms are still unclear. These genetic defects involve in numerous biological processes, which converge to a common destiny: motoneuron degeneration. In addition, the common comorbid Frontotemporal Dementia (FTD) further complicates the investigation of ALS etiology. In this study, we aimed to explore the protein-protein interaction network built on known ALS-causative genes to identify essential proteins and common downstream proteins between classical ALS and ALS+FTD (classical ALS + ALS/FTD) groups. The results suggest that classical ALS and ALS+FTD share similar essential protein set (VCP, FUS, TDP-43 and hnRNPA1) but have distinctive functional enrichment profiles. Thus, disruptions to these essential proteins might cause motoneuron susceptible to cellular stresses and eventually vulnerable to proteinopathies. Moreover, we identified a common downstream protein, ubiquitin-C, extensively interconnected with ALS-causative proteins (22 out of 24) which was not linked to ALS previously. Our in silico approach provides the computational background for identifying ALS therapeutic targets, and points out the potential downstream common ground of ALS-causative mutations.
Analysis of Traffic Scenes by Using the Hierarchical Structure Code

Object recognition in natural surroundings is one of the most ambitious goals of digital image processing. In our work, strategies for the analysis of traffic scenes are investigated. The Hierarchical Structure Code (HSC) is applied to this problem and it has been shown that fast object recognition is possible. The HSC provides a unified approach for contour-based and region-based descriptions of structures. Hardware is being built to compute the HSC in video real time and a library of HSC-operations has been developed to extract invariant features like structure types, shape descriptions, or relations between structures. As part of the PROMETHEUS-project, we examined a set of 141 traffic scenes in order to develop strategies for an automatic analysis. Some of them are already implemented by using the set of domain independent HSC-operations together with some newly created ones. Hereby, we could show that fast analysis of traffic scenes is possible with the HSC by using parallel hardware architectures. 1
Interactive cosegmentation by Isoperimetric Graph Partitioning

An interactive method for cosegmentation using isoperimetric graph partitioning is proposed. I use a joint image graph, which merges two individual weight matrices of an image pair and the correspondence matrix between the pair. By combining pixel similarity and feature correspondence which contains object information, the semantic quality of segmentation is improved. This algorithm only requires handful user inputs for the location information of the target object in one image, and the threshold values in the discretization of the segmentation solution of each image. Although it requires users to set the threshold for the last segmentation results, the original continuous solution also shows significant separation between the foreground object and the background. Compared to existing algorithms, this algorithm is robust to small number of matched features. It also skips the manual integration of over-segmented pieces or multiple rigid parts of the object. The algorithm is flexible enough to handle multiple object classes from buildings to human bodies. Most importantly, the execution time is fast enough for the interactive environment, because the partitioning algorithm relies on linear system.
Chinese Abbreviation Identification Using Abbreviation-Template Features *

Chinese abbreviations are frequently used without being defined, which has brought much difficulty into NLP. In this study, the definition-independent abbreviation identification problem is proposed and resolved as a classification task in which abbreviation candidates are classified as either ‘abbreviation’ or ‘non-abbreviation’ according to the posterior probability. To meet our aim of identifying new abbreviations from existing ones, our solution is to add generalization capability to the abbreviation lexicon by replacing words with word classes and therefore create abbreviation-templates. By utilizing abbreviation-template features as well as context information, a SVM approach is employed as the classifier. The evaluation on a raw Chinese corpus obtains an encouraging performance. Our experiments further demonstrate the improvement after integrating with extended word clustering (We design it to enable a joint learning of word classes), morphological analysis, substring analysis and person name identification. To our knowledge, this is the first definition-independent machine learning approach for Chinese abbreviation identification.
Growth factors and interleukin-6 across the lung circulation in pulmonary hypertension

The aim of our study was to assess the levels of growth factors and interleukin (IL)-6 across the pulmonary circulation in patients with pulmonary arterial hypertension (PAH) and correlate them with clinical and haemodynamic data and outcome. Simultaneous arterial and pulmonary arterial blood samples in patients with PAH (n = 44) and controls (n = 20) were obtained during right heart catheterisation. Vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF)-BB, transforming growth factor (TGF)-β1 and IL-6 were measured using ELISA. Arterial median (interquartile range) values for VEGF, PDGF-BB, TGF-β1 and IL-6 were significantly higher in patients (377 (218–588) versus 9.0 pg·mL−1; 1,955 (1,371–2,519) versus 306 (131–502) pg·mL−1; 26.42 (11.3–41.1) versus 7.0 (1.8–18.4) ng·mL−1; and 3.98 (0.7–8.1) versus 0.7 pg·mL−1, respectively; p<0.001 for all variables). There was a consistent step-up of VEGF, PDGF-BB and TGF-β1 across the lungs in PAH patients (p<0.001, p = 0.002 and p<0.001, respectively), whereas in controls, arterial and pulmonary arterial serum levels of IL-6 and growth factors were similar (statistically nonsignificant). In multivariate analysis, increased IL-6 levels predicted mortality (hazard ratio 1.08 (95% confidence interval 1.02–1.15); p = 0.012). Our findings indicate increased release and/or decreased clearance of growth factors at the lung vascular level, which may contribute to vascular remodelling in PAH.
The Simulation of Violent Free-Surface Dynamics at Sea and in Space

Non-linear free-surface phenomena, such as extreme waves and violent sloshing, and their impact on the dynamics response of the containing vessels have long been subjects that could only be studied with experimental methods. Nowadays, computational CFD tools can make a significant contribution to these flow problems. The paper starts with a short overview of the most popular numerical methods to simulate highly non-linear free-surface phenomena, with emphasis on Navier--Stokes methods. Thereafter, it describes the development efforts made in the maritime SAFE-FLOW project and the micro-gravity SloshSat FLEVO project. In particular, the improved Volume-of-Fluid (iVOF) free-surface simulation method ComFlo is presented. Examples of violent fluid dynamics in both application areas are presented. In all cases experimental data are available to validate the calculations.
Efficient convolutional sparse coding

When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but the independent sparse coding of each patch results in a representation that is not optimal for the image as a whole. A recent development is convolutional sparse coding, in which a sparse representation for an entire image is computed by replacing the linear combination of a set of dictionary vectors by the sum of a set of convolutions with dictionary filters. A disadvantage of this formulation is its computational expense, but the development of efficient algorithms has received some attention in the literature, with the current leading method exploiting a Fourier domain approach. The present paper introduces a new way of solving the problem in the Fourier domain, leading to substantially reduced computational cost.
Cluster Analysis of Time-Series Medical Data Based on the Trajectory Representation and Multiscale Comparison Techniques

This paper presents a cluster analysis method for multidimensional time-series data on clinical laboratory examinations. Our method represents the time series of test results as trajectories in multidimensional space, and compares their structural similarity by using the multiscale comparison technique. It enables us to find the part-to-part correspondences between two trajectories, taking into account the relationships between different tests. The resultant dissimilarity can be further used with clustering algorithms for finding the groups of similar cases. The method was applied to the cluster analysis of Albumin-Platelet data in the chronic hepatitis dataset. The results denonstrated that it could form interesting groups of cases that have high correspondence to the fibrotic stages.
Reducing access latency in erasure coded cloud storage with local block migration

Erasure coding has been applied in many cloud storage systems to enhance reliability at a lower storage cost than replication. While a large amount of prior work aims to enhance recovery performance and reliability, the overall access delay in coded storage still needs to be optimized. As most production systems adopt a systematic code and place the original copy of each block on only one server to be read normally, it is harder to balance server loads and more likely to incur latency tails in coded storage than in three-way replication, where a block can be read from any of the 3 servers storing the block. In this paper, we propose to reduce the access latency in coded storage systems by moving blocks with anti-correlated demands onto same servers for statistical load balancing. We formulate the optimal block placement as a problem similar to Min-k-Partition, propose a local block migration scheme, and derive an approximation ratio as a function of demand variation across blocks. Based on request traces from Windows Azure Storage, we demonstrate that our scheme can significantly reduce the access latency with only a few block moves, especially when the request demand is skewed.
Smoking correlates with increased cytoskeletal protein‐related coding region mutations in the lung and head and neck datasets of the cancer genome atlas

Cancer from smoking tobacco is considered dependent on mutagens, but significant molecular aspects of smoking‐specific, cancer development remain unknown. We defined sets of coding regions for oncoproteins, tumor suppressor proteins, and cytoskeletal‐related proteins that were compared between nonsmokers and smokers, for mutation occurrences, in the lung adenocarcinoma (LUAD), head and neck squamous carcinoma (HNSC), bladder carcinoma (BLCA), and pancreatic adenocarcinoma ( PAAD) datasets from the cancer genome atlas (TCGA). We uncovered significant differences in overall mutation rates, and in mutation rates in cytoskeletal protein‐related coding regions (CPCRs, including extracellular matrix protein coding regions), between nonsmokers and smokers in LUAD and HNSC (P < 0.001), raising the question of whether the CPCR mutation differences lead to different clinical courses for nonsmoker and smoker cancers. Another important question inspired by these results is, whether high smoker cancer mutation rates would facilitate genotoxicity or neoantigen‐based therapies. No significant, mutation‐based differences were found in the BLCA or PAAD datasets, between nonsmokers and smokers. However, a significant difference was uncovered for the average number of overall cancer mutations, in LUAD, for persons who stopped smoking more than 15 years ago, compared with more recent smokers (P < 0.032).
Area minimization for general floorplans

Two methods for the area minimization problem in floorplanning are presented. These methods can be viewed as generalizations of Stockmeyer's algorithm in the sense that they reduce to Stockmeyer's algorithm for floorplans that are slicing. The present methods can also be applied to general floorplans. Compared with the branch-and-bound algorithm, which is enumerative in nature and does not have any nontrivial performance bound for general floorplans, these methods are probably better than exhaustive methods for all the floorplans studied. <<ETX>>
Optical Fiber Relative Humidity Sensor Based on a FBG with a Di-Ureasil Coating

In this work we proposed a relative humidity (RH) sensor based on a Bragg grating written in an optical fiber, associated with a coating of organo-silica hybrid material prepared by the sol-gel method. The organo-silica-based coating has a strong adhesion to the optical fiber and its expansion is reversibly affected by the change in the RH values (15.0–95.0%) of the surrounding environment, allowing an increased sensitivity (22.2 pm/%RH) and durability due to the presence of a siliceous-based inorganic component. The developed sensor was tested in a real structure health monitoring essay, in which the RH inside two concrete blocks with different porosity values was measured over 1 year. The results demonstrated the potential of the proposed optical sensor in the monitoring of civil engineering structures.
CPM : A Graph Pattern Matching Kernel with Diffusion for Accurate Graph Classification

Graph data mining is an active research area. Graphs are general modeling tools to organize information from heterogenous sources and have been applied in many scientific, engineering, and business fields. With the fast accumulation of graph data, building highly accurate predictive models for graph data emerges as a new challenge that has not been fully explored in the data mining community. In this paper, we demonstrate a novel technique called G ̄ raph P ̄ attern M ̄ atching kernel (GPM). Our idea is to leverage existing frequent pattern discovery methods and to explore the application of kernel classifier (e.g. support vector machine) in building highly accurate graph classification. In our method, we first identify all frequent patterns from a graph database. We then map subgraphs to graphs in the graph database and use a process we call “pattern diffusion” to label nodes in the graphs. Finally we designed a novel graph matching algorithm to compute a graph kernel. We have performed a comprehensive testing of our algorithm using 16 chemical structure data sets and have compared our methods to all major graph kernel functions that we know. The experimental results demonstrate that our method outperforms state-of-the-art graph kernel methods with a large margin.
Performance analysis of LPG PCA algorithm in medical images

This paper presents the performance analysis of the LPG PCA algorithm in medical images. Medical images containing lot of information are often affected by noise and artifacts, which leads to the inefficient diagnosis. LPG PCA which is a statistical decorrelation technique is found to be one of the efficient methods which could be used in improving the performance of medical images. For better preservation of fine structures in an image, a pixel and its nearest neighbors are modeled as a vector variable whose training samples are selected using a moving window in the image. Such a local vector variable preservation leads to the selection of similar intensity characteristics. This method is done in two stages for improving the denoising performance. Performance analysis of this technique is found using various image quality measures.
A novel model of autosomal dominant Alport syndrome in Dalmatian dogs.

BACKGROUND Autosomal dominant Alport syndrome is a rare inherited disease characterized clinically by haematuria, renal failure and deafness, and ultrastructurally by a lamellated glomerular basement membrane (GBM). It is usually caused by mutations in the COL4A3 or COL4A4 genes which code for the alpha3 and alpha4 chains of type IV collagen. We describe here a novel spontaneous model of autosomal dominant Alport syndrome in Dalmatian dogs. METHODS Affected dogs were identified by a urinary protein creatinine >/=0.3. A total of 10 affected adult Dalmatians and eight unaffected age- and sex-matched dogs from breeds other than Dalmatians were examined. In addition, kidneys from five Dalmatian fetuses from affected mothers were examined histologically and ultrastructurally. RESULTS All affected dogs were purebred Dalmatians and had a common progenitor. Successive generations were affected, and males and females were affected equally often and equally severely, consistent with autosomal dominant inheritance. The median age at onset of renal failure was 18 months (range 8 months to 7 years). Affected dogs were not clinically deaf, and did not have the ocular abnormalities seen in human X-linked or autosomal recessive Alport syndrome. In addition, they did not have the leucocyte inclusions, low platelet counts or large platelets seen in autosomal dominant hereditary nephritis due to MYH9 mutations. The renal histology and ultrastructural appearance of the GBM appeared to be normal in utero. However, affected adult kidneys demonstrated segmental glomerular hyalinosis and sclerosis with tubulo-interstitial inflammation and fibrosis, and on ultrastructural examination the GBM was lamellated with subepithelial frilling, vacuolation and occasional intramembranous deposits. All alpha1(IV)-alpha5(IV) type IV collagen chains were present in the affected GBM and Bowman's capsule. CONCLUSIONS Autosomal dominant Alport syndrome in Dalmatians resembles the disease in Bull terriers but has arisen independently. These models will enable us to determine how genetic mutations affect the corresponding proteins and overall membrane structure in autosomal dominant Alport syndrome.
Mixing and decoherence in continuous-time quantum walks on long-range interacting cycles

We study the effect of small decoherence in continuous-time quantum walks on long-range interacting cycles, which are constructed by connecting all the two nodes of distance m on the cycle graph. In our investigation, each node is continuously monitored by an individual point contact, which induces the decoherence process. We obtain the analytical probability distribution and the mixing time upper bound. Our results show that, for small rates of decoherence, the mixing time upper bound is independent of distance parameter m and is proportional to inverse of decoherence rate.