Miracle-dz/UniFAR-FTUs huggingface dataset:
Picky: oligo microarray design for large genomes

MOTIVATION Many large genomes are getting sequenced nowadays. Biologists are eager to start microarray analysis taking advantage of all known genes of a species, but existing microarray design tools were very inefficient for large genomes. Also, many existing tools operate in a batch mode that does not assure best designs. RESULTS Picky is an efficient oligo microarray design tool for large genomes. Picky integrates novel computer science techniques and the best known nearest-neighbor parameters to quickly identify sequence similarities and estimate their hybridization properties. Oligos designed by Picky are computationally optimized to guarantee the best specificity, sensitivity and uniformity under the given design constrains. Picky can be used to design arrays for whole genomes, or for only a subset of genes. The latter can still be screened against a whole genome to attain the same quality as a whole genome array, thereby permitting low budget, pathway-specific experiments to be conducted with large genomes. Picky is the fastest oligo array design tool currently available to the public, requiring only a few hours to process large gene sets from rice, maize or human.
Compiler Approach for Reducing Soft Errors in Register Files

With continuous technology scaling, soft errors are becoming an increasingly important design concern even for earthbound applications. While compiler approaches have the potential to mitigate the effect of soft errors with minimal runtime overheads, static vulnerability estimation—an essential part of compiler approaches—is lacking due to its inherent complexity. This paper presents a static analysis approach for Register File (RF) vulnerability estimation. We decompose the vulnerability of a register into intrinsic and conditional basic-block vulnerabilities. This decomposition allows us to develop a fast, yet reasonably accurate, linear equation-based RF vulnerability estimation mechanism. We demonstrate its practical application to compiler optimizations. Our experimental results on benchmarks from MiBench suite indicate that not only our static RF vulnerability estimation is fast and accurate, but also compiler optimizations enabled by our static estimation can achieve very cost-effective protection of register files against soft errors.
PhD Proposal on qRTDB: QoS-Sensitive Real-Time Database

A multi-cell electrolyzer comprising a plurality of unit cells, each of which is composed of an anode chamber containing an anode and a cathode chamber containing a cathode and a cation exchange membrane for partitioning said unit cell into said anode chamber and said cathode chamber, and each of which is adapted to have an internal pressure maintained at a level higher than the atmospheric pressure in operation of the electrolyzer, said plurality of unit cells being arranged in series and adapted to be energized through a plurality of current lead plates, and rigid multi-contact electrically conductive means provided between the adjacent unit cells and/or between each current lead plate and the unit cell adjacent thereto, thereby establishing electrical connection between the adjacent unit cells and/or between each current lead plate and the unit cell adjacent thereto. With such a structure, not only is the electrical contact resistance between the adjacent unit cells and between each current lead plate and the unit cell adjacent thereto extremely reduced but also the current density in the unit cells is rendered uniform. Further, the present multi-cell electrolyzer can be easily constructed either in a bipolar form or in a monopolar form using unit cells common to both of the bipolar and monopolar forms.
Extended FNS for Constrained Parameter Estimation

We present a new method, called "EFNS" ("extended FNS"), for linearizable constrained maximum likelihood estimation. This complements the CFNS of Chojnacki et al. and is a true extension of the FNS of Chojnacki et al. to an arbitrary number of intrinsic constraints. Computing the fundamental matrix as an illustration, we demonstrate that CFNS does not necessarily converge to a correct solution, while EFNS converges to an optimal value which nearly satisfies the theoretical accuracy bound (KCR lower bound).
An Empirical Investigation of the Effect of Target-Related Information in Phishing Attacks

Analyzing the role of target-related information in a security attack is an understudied topic in the behavioral information security research field. This paper presents an empirical investigation of the effect of adding information about the target in phishing attacks. Data was collected by conducting two phishing experiments using a sample of 158 employees at five Swedish organizations. The first experiment included a traditional mass-email attack with no target-related information, and the second experiment was a targeted phishing attack in which we included specific information related to the targeted employees' organization. The results showed that the number of organizational employees falling victim to phishing significantly increased when target-related information was added in the attack. During the first experiment 5.1 % clicked on the malicious link compared to 27.2 % of the second phishing attack, and 8.9 % of those executed the binary compared to 3.2 % of the traditional phishing attack. Adding target-related information is an effective way for attackers to significantly increase the effectiveness of their phishing attacks. This is the first study that has showed this significant effect using organizational employees as a sample. The implications of the results are further discussed.
WiQoSM: An Integrated QoS-Aware Mobility and User Behavior Model for Wireless Data Networks

Modeling mobility and user behavior is of fundamental importance in testing the performance of protocols for wireless data networks. Although several models have been proposed in the literature, none of them can at the same time capture important features such as geographical mobility, user-generated traffic, and the wireless technology at hand. When collectively considered, these three aspects determine the user-perceived quality-of-service (QoS) level, which, in turn, might have an influence on the mobility of those users (we call them QoS-driven users) who do not display constrained mobility patterns, but they can decide to move to less congested areas of the network in case their perceived QoS level becomes unacceptable. In this paper, we introduce the Wireless QoS-aware Mobility (WiQoSM) model, which collectively considers all of the above mentioned aspects of wireless data networks. WiQoSM is composed of 1) a user mobility model, 2) a user traffic model, 3) a wireless technology model, and 4) a QoS model. Components 1,2, and 3 provide input to the QoS model, which, in turn, can influence the mobility behavior of QoS-driven users. WiQoSM is very simple to use and configure and can be used to generate user and traffic traces at the access points (APs) composing a wireless data network. WiQoSM is shown to be able to generate traces that resemble statistical features observed in traces extracted from real-world wireless local area network (WLAN) deployments. Furthermore, WiQoSM has the nice feature of allowing the fine tuning of a disjoint set of parameters in order to influence different statistical properties of the generated traces and of providing the network designer with a high degree of flexibility in choosing network parameters such as the number of users and APs, wireless channel technology, traffic mix, and so on. Given the above features, WiQoSM can be a valuable tool in the simulation of wireless data network protocols.
Automatic partial loop summarization in dynamic test generation

Whitebox fuzzing extends dynamic test generation based on symbolic execution and constraint solving from unit testing to whole-application security testing. Unfortunately, input-dependent loops may cause an explosion in the number of constraints to be solved and in the number of execution paths to be explored. In practice, whitebox fuzzers arbitrarily bound the number of constraints and paths due to input-dependent loops, at the risk of missing code and bugs. In this work, we investigate the use of simple loop-guard pattern-matching rules to automatically guess an input constraint defining the number of iterations of input-dependent loops during dynamic symbolic execution. We discover the loop structure of the program on the fly, detect induction variables, which are variables modified by a constant value during loop iterations, and infer simple partial loop invariants relating the value of such variables. Whenever a guess is confirmed later during the current dynamic symbolic execution, we then inject new constraints representing pre and post loop conditions, effectively summarizing sets of executions of that loop. These pre and post conditions are derived from partial loop invariants synthesized dynamically using pattern-matching rules on the loop guards and induction variables, without requiring any static analysis, theorem proving, or input-format specification. This technique has been implemented in the whitebox fuzzer SAGE, scales to large programs with many nested loops, and we present results of experiments with a Windows 7 image parser.
Tight Lower Bounds for Data-Dependent Locality-Sensitive Hashing

We prove a tight lower bound for the exponent $\rho$ for data-dependent Locality-Sensitive Hashing schemes, recently used to design efficient solutions for the $c$-approximate nearest neighbor search. In particular, our lower bound matches the bound of $\rho\le \frac{1}{2c-1}+o(1)$ for the $\ell_1$ space, obtained via the recent algorithm from [Andoni-Razenshteyn, STOC'15]. In recent years it emerged that data-dependent hashing is strictly superior to the classical Locality-Sensitive Hashing, when the hash function is data-independent. In the latter setting, the best exponent has been already known: for the $\ell_1$ space, the tight bound is $\rho=1/c$, with the upper bound from [Indyk-Motwani, STOC'98] and the matching lower bound from [O'Donnell-Wu-Zhou, ITCS'11]. We prove that, even if the hashing is data-dependent, it must hold that $\rho\ge \frac{1}{2c-1}-o(1)$. To prove the result, we need to formalize the exact notion of data-dependent hashing that also captures the complexity of the hash functions (in addition to their collision properties). Without restricting such complexity, we would allow for obviously infeasible solutions such as the Voronoi diagram of a dataset. To preclude such solutions, we require our hash functions to be succinct. This condition is satisfied by all the known algorithmic results.
Data I / O provision for Spark applications in a Mesos cluster

At present there is a crucial need to take into account the I/O capabilities of commodity servers of clusters in production environments. In order to support such a demand in production environments, we propose a solution that can be integrated into Mesos to control I/O data throughput for Spark applications. The proposed solution takes into account the I/O capability to provide resource mapping, control and enforcement functionality for applications that process HDFS data. The aim is to minimize I/O contention situations that lead to the degradation of quality of service offered to Spark applications and clients.
An improved matching functions for information retrieval using Genetic Algorithm

This article presents a novel information retrieval algorithms using genetic algorithm to increase the performance of information retrieval system. The novel matching functions called Overall Matching Function (OMF) and Virtual Center based Matching Function (VCF) are proposed for improving the retrieval performance. Overall Matching Function gives the results by finding the average of matching scores from classical matching functions and VCF is based on finding the virtual center from the set of centroids present in clustering space. VCF based Genetic Algorithm (VCGA) are used for information retrieval. Working of both the matching functions is compared to check the performance. We got promising results. This paper is presented as extension to our previous research papers in which the idea of GA based model for clustering and retrieval was proposed and the algorithms for VCF and VCGA was propose. The experimental results show improved values for precision and recall if retrieval is done using VCF and VCGA.
Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition

We propose a simple two-step approach for speeding up convolution layers within large convolutional neural networks based on tensor decomposition and discriminative fine-tuning. Given a layer, we use non-linear least squares to compute a low-rank CP-decomposition of the 4D convolution kernel tensor into a sum of a small number of rank-one tensors. At the second step, this decomposition is used to replace the original convolutional layer with a sequence of four convolutional layers with small kernels. After such replacement, the entire network is fine-tuned on the training data using standard backpropagation process. We evaluate this approach on two CNNs and show that it is competitive with previous approaches, leading to higher obtained CPU speedups at the cost of lower accuracy drops for the smaller of the two networks. Thus, for the 36-class character classification CNN, our approach obtains a 8.5x CPU speedup of the whole network with only minor accuracy drop (1% from 91% to 90%). For the standard ImageNet architecture (AlexNet), the approach speeds up the second convolution layer by a factor of 4x at the cost of $1\%$ increase of the overall top-5 classification error.
Crystallization and Preliminary Diffraction Data of a Major Pollen Allergen

Group V major allergen Phl p 5b of timothy grass pollen induces allergic rhinitis and bronchial asthma in 90% of grass pollen-allergic patients. In addition to its allergenicity ribonuclease activity has recently been attributed to this 29-kDa protein. The allergen was expressed in Escherichia coli and subsequently purified. Spontaneous conversion of these preparations to a mixture of various forms with molecular sizes between 10 and 29 kDa was consistently observed. Surprisingly, crystals could be grown from this heterogenous preparation. Single crystals, redissolved and analyzed by SDS-polyacrylamide gel electrophoresis and immunoblot, yielded one distinct low molecular weight protein, which was identified by amino acid sequencing as the C-terminal 13-kDa portion of the allergen. Histamine release assays with single crystal solutions using basophils of an allergic patient demonstrated allergenicity comparable with that of the holo-allergen. By contrast, RNase activity of the crystallized C-terminal form was 23 times higher than that of the full-length parent allergen. Crystals were used to collect preliminary diffraction data; the space group was evaluated to I4122 with cell dimensions of a = 87.7 Å, b = 87.7 Å, and c = 59.6 Å. We conclude that preferential crystal growth of the 13-kDa form is indicative of a compact conformation of this particular C-terminal portion of the allergen. Thus, we show here that protein crystallization is not only a prerequisite for structural analyses, but it also can provide a unique separation technique to localize the functional domain of a major allergen.
ESTIMATING MULTILEVEL MODELS FOR CATEGORICAL DATA VIA GENERALIZED LEAST SQUARES

Montero et al. (2002) proposed a strategy to formulate multilevel models related to a contingency table sample. This methodology is based on the application of the general linear model to hierarchical categorical data. In this paper we applied the method to a multilevel logistic regression model using simulated data. We find that the estimates of the random parameters are inadmissible in some circumstances; large bias and negative estimates of the variance are expected for unbalanced data sets. In order to correct the estimates we propose to use a numerical technique based on the Truncated Singular Value Decomposition (TSVD) in the solution of the problem of generalized least squares associated to the estimation of the random parameters. Finally a simulation study is presented to shows the effectiveness of this technique for reducing the bias of the estimates.
Human Mobility in Advanced and Developing Economies: A Comparative Analysis

The deployment of ubiquitous computing technologies in the real-world has enabled the capture of large-scale quantita tive data related to human behavior, including geographical information. This type of data creates an opportunity to characterize human mobility, with potential applications ranging from modeling the spread of viruses to transportation planning. This paper presents an initial study focused on understanding the similarities and differences in mobility patt erns across countries with different economic levels. In partic ular, we analyze and compare human mobility in a developing and an advanced economy 1 by means of their cell phone traces. We characterize mobility in terms of (1) average distance traveled, (2) area of influence of each individual, and (3) geographic sparsity of the social network. Our results indicate that there are statistically significant differen ces in human mobility across countries with different economic levels. Specifically, individuals in the developing economy sh ow smaller mobility and smaller geographical sparsity of their social network when compared to individuals in the advanced economy.
A stereo input-output superdirective beamformer for dual channel noise reduction

This contribution presents a stereo input-output beamformer for dual channel noise reduction. The computationally very efficient beamformer adapts superdirective filter design techniques to binaural input signals to optimally enhance signals from a given spatial direction. The beamformer outputs a stereo enhanced signal and thus preserves the spatial impression. Experiments in a real environment using a dummy head and various speech sources indicate that the proposed algorithm is capable of improving the speech intelligibility significantly.
Association Between Physical Activity and Risk of All-Cause Mortality and Cardiovascular Disease in Patients With Diabetes

OBJECTIVE The association between habitual physical activity (PA) and lowered risk of all-cause mortality (ACM) and cardiovascular disease (CVD) has been suggested in patients with diabetes. This meta-analysis summarizes the risk reduction in relation to PA, focusing on clarifying dose-response associations. RESEARCH DESIGN AND METHODS Electronic literature searches were conducted for cohort studies that examined relative risk (RR) of ACM or CVD in relation to PA in patients with diabetes. For the qualitative assessment, RR for the highest versus the lowest PA category in each study was pooled with a random-effects model. We added linear and spline regression analyses to assess the quantitative relationship between increases in PA and ACM and CVD risk. RESULTS There were 17 eligible studies. Qualitatively, the highest PA category had a lower RR [95% CI] for ACM (0.61 [0.52–0.70]) and CVD (0.71 [0.60–0.84]) than the lowest PA category. The linear regression model indicated a high goodness of fit for the risk of ACM (adjusted R2 = 0.44, P = 0.001) and CVD (adjusted R2 = 0.51, P = 0.001), with the result that a 1 MET-h/day incrementally higher PA was associated with 9.5% (5.0–13.8%) and 7.9% (4.3–11.4%) reductions in ACM and CVD risk, respectively. The spline regression model was not significantly different from the linear model in goodness of fit (P = 0.14 for ACM risk; P = 0.60 for CVD risk). CONCLUSIONS More PA was associated with a larger reduction in future ACM and CVD risk in patients with diabetes. Nevertheless, any amount of habitual PA was better than inactivity.
Digital Sliding-Mode Control of Piezoelectric Micropositioning System Based on Input–Output Model

Piezoelectric actuation micro-/nanopositioning systems have been widely employed in diverse micro-/nanomanipulation applications. This paper presents the design, analysis, and validation of a new control scheme termed input-output-based digital sliding-mode control (IODSMC) to suppress the unmolded nonlinearity and disturbance in piezoelectric micro-/nanopositioning systems. The controller is established based on a linear digital input-output nominal model. The scheme facilitates a rapid implementation because the construction of either a hysteresis model or a state observer is not needed. The chattering-free control is capable of achieving an O(T2) output tracking accuracy by overcoming the model disturbance. Moreover, the stability of the control system is proved, and its effectiveness is validated through experimental investigations on a piezo-driven micropositioning system. Results demonstrate that the IODSMC scheme is superior to the conventional proportional-integral-derivative control for motion-tracking tasks. Furthermore, it exhibits promising robustness in front of internal and external disturbances.
streamingRPHash: Random Projection Clustering of High-Dimensional Data in a MapReduce Framework

The expanding needs for analysis on large datasets has increased as the amount and availability of data continues to grow. The size and format of this data makes manual analysis infeasible and has motivated the drive for automated methods such as data clustering. Among the most commonly used clustering algorithms, K-means has been proven as one of the most popular choice that delivers acceptable results in reasonable time. For many years, K-means has proven to be statistically efficient and easy to implement. While K-means is widely used for clustering streaming data, it has performance issues when it comes to robustness with noise, parallelism and working with very large, high data sets. In particular, K-Means (and other conventional techniques) for data clustering do not parallelize or scale well with the increasing dimensionality of data.
Induction based replicative patterns algorithm for routing analysis in unidirectional flow networks

This paper propose a novel, induction based replicative patterns algorithm towards searching all routes in a unidirectional flow network. The fundamental approach is to generate all paths from a source node to sink node (destination node), represented in a two-dimensional matrix applying the proposed induction based on replicative patterns of matrix cells. It is found that, by starting a network with node size three, a matrix with all routes of any network size can be arrived. The proposed algorithm effectively provides list of all routes in any unidirectional flow networks with only data of the number of nodes in the network. The algorithm apply the concept of iterative blanking of replicative patterns, which are generated by definite combination of rows and columns in a two dimensional matrix.