Miracle-dz/UniFAR-FTUs huggingface dataset:
Explicit and Implicit Parallelisms in Decomposition Based Evolutionary Many-Objective Optimization Algorithms

Over the past two decades, evolutionary multiobjective optimization (EMO) algorithms have demonstrated their ability to find and maintain multiple trade-off solutions in two and three-objective problems. This is because their operators are able to establish an implicit parallel search within an evolving population around multiple optimal regions of the search space simultaneously. For many-objective optimization problems involving a large-dimensional objective space, the extent of parallelism present in early EMO methods was found to be too generic. Decompositionbased EMO algorithms which divide the overall computing task into a number of sub-tasks of focusing within a region of the search space have found to be successful in solving many-objective problems. In this paper, we term this external control of an algorithm’s parallelism as ‘explicit parallelism’ set by the algorithm developer. Although such a decomposition concept compromises on the implicit parallelism aspect of an EMO algorithm, an externally defined coordination among different subtasks is able to bring back the requisite parallelism needed to solve them. In this paper, we consider three decomposition-based EMO algorithms – MOEA/D, M2M, and NSGA-III – to investigate the effect of user-controlled explicit parallelism mechanism on their search operators. For this purpose, we first consider a number of M2M variants with differing levels of explicit parallelism and identify the most-balanced algorithm between explicit and implicit parallelisms by applying them on a number of standard many-objective unscaled and scaled ∗L. Chen and H. -L. Liu are with Guangdong University of Technology, Guangzhou, China, e-mail: chenaction@126.com, hlliu@gdut.edu. cn. †K. Deb is with Department of Electrical and Computer Engineering and BEACON Center for the Study of Evolution in Action (NSF DBI-0939454), Michigan State University, 428 S. Shaw Lane, 2120 EB, East Lansing, MI 48824, USA, e-mail: kdeb@egr.msu. edu ‡The visit of the first author to Michigan State University was supported by China Scholarship Council. This material is based in part upon work supported by the National Science Foundation under Cooperative Agreement No. DBI-0939454. test problems (DTLZ and WFG problems). Results from our extensive study indicate that by relaxing the decomposition effect, thereby re-establishing an appropriate parallel search within M2M operators, the performance of the resulting M2M variants can be improved. Motivated by the M2M-variant study, we repeat the procedure on NSGA-III and MOEA/D operators and observe interesting but unique balancing acts between explicit and implicit parallelisms that each of the algorithms requires. We also investigate the effect of normalization of objectives in improving the performance of MOEA/D and M2M methods and report much improved performances than the original methods. The overall approach helps to develop more efficient algorithms than the original methods. The principles of this study can be used to improve the performance other EMO methods.
Ischemic Preconditioning Enhances Performance and Erythrocyte Deformability of Responders

Abstract This pilot study aimed to evaluate the differential effects of a remote ischemic preconditioning (rIPC) manoeuvre on performance and red blood cell (RBC) deformability compared to a sham control and a placebo setting. Ten male subjects performed three test settings in a single-blind, crossover, and randomized control design. All settings started with 20 min of rest and were followed by 4 cycles of occlusion/reperfusion consisting of 5 min each. During rIPC and placebo, the cuff pressure was inflated to 200 mmHg and 120 mmHg, respectively. During the sham control setting, 10 mmHg pressure was applied. All tests were followed by a cycle exercise with lactate diagnostics. Power at 2 and 4 mmol/l lactate thresholds were calculated. RBC deformability was measured before and after the respective manoeuvre. Results showed that no effect resulted from any manoeuvre on performance values or RBC deformability. But 6 subjects showed a higher power at the 2 mmol/l threshold, and 5 subjects exerted higher power at the 4 mmol/l threshold when the rIPC manoeuvre preceded the exercise. In these responsive subjects, RBC deformability also improved. Hence, rIPC effects are much influenced by the subjects’ responsiveness, and improved RBC deformability might contribute to enhanced performance in responsive subjects.
On how complexity a↵ects the stability of a predictor

Given a finite random sample from a Markov chain environment, we select a predictor that minimizes a criterion function and refer to it as being calibrated to its environment. If its prediction error is not bounded by its criterion value, we say that the criterion fails. We define the predictor’s complexity to be the amount of uncertainty in detecting that the criterion fails given that it fails. We define a predictor’s stability to be the discrepancy between the average number of prediction errors that it makes on two random samples. We show that complexity is inversely proportional to the level of adaptivity of the calibrated predictor to its random environment. The calibrated predictor becomes less stable as its complexity increases or as its level of adaptivity decreases.
Electrophysiological Evidence for Failures of Item Individuation in Crowded Visual Displays

■ Visual perception is strongly impaired when peripheral targets are surrounded by nearby distractors, a phenomenon known as visual crowding. One common behavioral signature of visual crowding is an increased tendency for observers to mistakenly report the features of nearby distractors instead of the target item. Here, our goal was to distinguish between two possible explanations of such substitution errors. On the one hand, crowding may have its effects after the deployment of attention toward—and individuation of—targets and flankers, such that multiple individuated perceptual representations compete to guide the behavioral response. On the other hand, crowding may prevent the individuation of closely spaced stimuli, thereby reducing the number of apprehended items. We attempted to distinguish these alternatives using the N2pc, an ERP that has been shown to track the deployment of spatial attention and index the number of individuated items within a hemifield. N2pc amplitude increased monotonically with set size in uncrowded displays, but this set size effect was abolished in crowded visual displays. Moreover, these crowding-induced declines in N2pc amplitude predicted individual differences in the rate of substitution errors. Thus, crowding-induced confusions between targets and distractors may be a consequence of failures to individuate target and distractor stimuli during early stages of visual selection. ■
A Video-based Attack for Android Pattern

Pattern lock is widely used for identification and authentication on Android devices. This article presents a novel video-based side channel attack that can reconstruct Android locking patterns from video footage filmed using a smartphone. As a departure from previous attacks on pattern lock, this new attack does not require the camera to capture any content displayed on the screen. Instead, it employs a computer vision algorithm to track the fingertip movement trajectory to infer the pattern. Using the geometry information extracted from the tracked fingertip motions, the method can accurately infer a small number of (often one) candidate patterns to be tested by an attacker. We conduct extensive experiments to evaluate our approach using 120 unique patterns collected from 215 independent users. Experimental results show that the proposed attack can reconstruct over 95% of the patterns in five attempts. We discovered that, in contrast to most people’s belief, complex patterns do not offer stronger protection under our attacking scenarios. This is demonstrated by the fact that we are able to break all but one complex patterns (with a 97.5% success rate) as opposed to 60% of the simple patterns in the first attempt. We demonstrate that this video-side channel is a serious concern for not only graphical locking patterns but also PIN-based passwords, as algorithms and analysis developed from the attack can be easily adapted to target PIN-based passwords. As a countermeasure, we propose to change the way the Android locking pattern is constructed and used. We show that our proposal can successfully defeat this video-based attack. We hope the results of this article can encourage the community to revisit the design and practical use of Android pattern lock.
The Mechanism of Rh-Catalyzed Transformation of Fatty Acids to Linear Alpha olefins

Linear alpha olefins (LAOs) are key commodity chemicals and petrochemical intermediates that are currently produced from fossil resources. Fatty acids are the obvious renewable starting material for LAOs, which can be obtained via transition-metal-catalyzed decarbonylative dehydration. However, even the best catalysts that have been obtained to date, which are based on palladium, are not active and stable enough for industrial use. To provide insight for design of better catalysts, we here present the first computationally derived mechanism for another attractive transition-metal for this reaction, rhodium. By comparing the calculated mechanisms and free energy profiles for the two metals, Pd and Rh, we single out important factors for a facile, low-barrier reaction and for a stable catalyst. While the olefin formation is rate limiting for both of the metals, the rate-determining intermediate for Rh is, in contrast to Pd, the starting complex, (PPh3)2Rh(CO)Cl. This complex largely draws its stability from the strength of the Rh(I)–CO bond. CO is a much less suitable ligand for the high-oxidation state Rh(III). However, for steric reasons, rhodium dissociates a bulkier triphenylphosphine and keeps the carbonyl during the oxidative addition, which is less favorable than for Pd. When compared to Pd, which dissociates two phosphine ligands at the start of the reaction, the catalytic activity of Rh also appears to be hampered by its preference for high coordination numbers. The remaining ancillary ligands leave less space for the metal to mediate the reaction.
Image classification by addition of spatial information based on histograms of orthogonal vectors

The Bag-of-Visual-Words (BoVW) model is widely used for image classification, object recognition and image retrieval problems. In BoVW model, the local features are quantized and 2-D image space is represented in the form of order-less histogram of visual words. The image classification performance suffers due to the order-less representation of image. This paper presents a novel image representation that incorporates the spatial information to the inverted index of BoVW model. The spatial information is added by calculating the global relative spatial orientation of visual words in a rotation invariant manner. For this, we computed the geometric relationship between triplets of identical visual words by calculating an orthogonal vector relative to each point in the triplets of identical visual words. The histogram of visual words is calculated on the basis of the magnitude of these orthogonal vectors. This calculation provides the unique information regarding the relative position of visual words when they are collinear. The proposed image representation is evaluated by using four standard image benchmarks. The experimental results and quantitative comparisons demonstrate that the proposed image representation outperforms the existing state-of-the-art in terms of classification accuracy.
The Protective Activities of Dietary Sea Cucumber Cerebrosides against Atherosclerosis through Regulating Inflammation and Cholesterol Metabolism in Male Mice

SCOPE Cerebroside is an active component extracted from sea cucumber (SCC), which is a traditional tonic food in China. In this study, the protective effect of SCC on atherogenesis is investigated and the possible underlying mechanism is determined. METHOD AND RESULTS Male C57BL/6J obese mice were first used to explore the impact of SCC at different doses on alleviating lipid disorders and insulin sensitivity. Then ApoE-/- mice were used to evaluate the protective activities of SCC on atherosclerosis. The results demonstrated that dietary SCC increased the insulin sensitivity and reduced serum and hepatic lipid profiles in a dose-dependent manner in C57BL/6J mice. In ApoE-/- mice, SCC treatment significantly decreased the atherosclerotic lesion formation and attenuated inflammation by decreasing the levels of inflammatory cytokines, such as CRP, TNF-α, IL-6. Compared to the model group, the SCC group showed lower cholesterol levels in serum and liver by mediating the expression of genes related to hepatic LDL uptake and cholesterol excretion. CONCLUSION Dietary SCC has the potential to eliminate atherosclerosis through regulating inflammation and cholesterol metabolism, and may be beneficial for the health of patients with cardiovascular disease.
Gait variability and symmetry assessment with inertial sensors for quantitative discrimination of Trendelenburg sign in total hip arthroplasty patients: a pilot study based on convenience sampling

Abstract Introduction The aim of this pilot study based on convenience sampling was to analyze the feasibility to quantitatively discriminate Trendelenburg sign (TS), a characteristic drop in pelvic position during gait in hip disfunctions, in patients with total hip arthroplasty (THA), by assessing gait variability and symmetry using inertial sensors. Methods Thirteen patients with right THA, divided into two groups with (GTS, n=4) and without TS (GnTS, n=9) assessed by experienced physician, were enrolled in the study. Harris Hip Score was applied for specific evaluation of THA. The protocol consisted in walking on a level treadmill during 3 minutes with two inertial sensors attached at anterior superior iliac spine of both sides. For each left and right step, features were extracted from the Y-axis gyroscope signals: peak value, mean absolute value, standard deviation and range. For each feature, a symmetry ratio was calculated as the ratio between left and right side. Results No significant differences were found in Harris Hip Score between groups. The variability assessed by standard deviation for left step, contralateral to the replaced side, was significantly larger for GTS group (p<0.001). Significant differences in the symmetry ratios were found between GTS and GnTS for all features extracted from gyroscopes Y-axis (W=144, p<0.001). The symmetry ratios for GnTS group were approximately equal one (except for range), whereas for the GTS group they exceed the 10% criterion. Conclusion The variability and symmetry ratios of gait features extracted from inertial sensors were successful to discriminate TS in THA patients.
In silico read normalization using set multi-cover optimization

De Bruijn graphs are a common assembly data structure for large sequencing datasets. But with the advances in sequencing technologies, assembling high coverage datasets has become a computational challenge. Read normalization, which removes redundancy in large datasets, is widely applied to reduce resource requirements. Current normalization algorithms, though efficient, provide no guarantee to preserve important k-mers that form connections between regions in the graph. Here, normalization is phrased as a set multi-cover problem on reads and a heuristic algorithm, ORNA, is proposed. ORNA normalizes to the minimum number of reads required to retain all k-mers and their relative kmer abundances from the original dataset. Hence, all connections and coverage information from the original graph are preserved. ORNA was tested on various RNA-seq datasets with different coverage values. It was compared to the current normalization algorithms and was found to be performing better. It is shown that combining read error correction and normalization allows more accurate and resource efficient RNA assemblies compared to the original dataset. Further, an application was proposed in which multiple datasets were combined and normalized to predict novel transcripts that would have been missed otherwise. Finally, ORNA is a general purpose normalization algorithm that is fast and significantly reduces datasets with little loss of assembly quality. ORNA can be found under https://github.com/SchulzLab/ORNA
Policy and Value Transfer in Lifelong Reinforcement Learning

We consider the problem of how best to use prior experience to bootstrap lifelong learning, where an agent faces a series of task instances drawn from some task distribution. First, we identify the initial policy that optimizes expected performance over the distribution of tasks for increasingly complex classes of policy and task distributions. We empirically demonstrate the relative performance of each policy class’ optimal element in a variety of simple task distributions. We then consider value-function initialization methods that preserve PAC guarantees while simultaneously minimizing the learning required in two learning algorithms, yielding M AX QI NIT , a practical new method for value-function-based transfer. We show that M AX QI NIT performs well in simple lifelong RL experiments.
pCAMP: Performance Comparison of Machine Learning Packages on the Edges

Machine learning has changed the computing paradigm. Products today are built with machine intelligence as a central attribute, and consumers are beginning to expect near-human interaction with the appliances they use. However, much of the deep learning revolution has been limited to the cloud. Recently, several machine learning packages based on edge devices have been announced which aim to offload the computing to the edges. However, little research has been done to evaluate these packages on the edges, making it difficult for end users to select an appropriate pair of software and hardware. In this paper, we make a performance comparison of several state-of-the-art machine learning packages on the edges, including TensorFlow, Caffe2, MXNet, PyTorch, and TensorFlow Lite. We focus on evaluating the latency, memory footprint, and energy of these tools with two popular types of neural networks on different edge devices. This evaluation not only provides a reference to select appropriate combinations of hardware and software packages for end users but also points out possible future directions to optimize packages for developers.
Advanced filter design technique based on equivalent circuits and coupling matrix segmentation

This paper presents a technique for the efficient design of bandpass waveguide microwave filters using a segmentation technique applied to an equivalent circuit. The technique is based on first developing an equivalent circuit that synthesizes the desired transfer function. Then, the different parts of the real physical structure are optimized by segmenting this equivalent circuit. The technique was originally developed for in‐line filters, and the main contribution of this paper is in the combination of this technique with the coupling matrix formalism. In this way, we adapt for the first time this design strategy to the design of complex coupling topologies, beyond the in‐line configuration. As an example, a complex sixth‐order dual‐mode filter, implemented in all‐inductive waveguide technology, is designed using the new coupling matrix segmentation technique, showing the effectiveness of the presented theory. A prototype of the filter has been manufactured, and the accuracy of the design technique is verified by measurements on the real prototype.
A Survey on Facilitating Document Annotation Techniques

: Annotations can be comments, important notes, explanation about data or other types of related information or remarks that can be attached to document or to a specific part of a document. As these annotations are external, it is possible to annotate any Web document independently, without need of editing the document itself. Annotations are seen as metadata as they give additional information about an existing piece of data. As a large amount of data is produced and shared in different organization which is in text format contains the metadata about organizing product and services. This type of text structured information is getting hidden in unstructured text. Annotations are useful in effective information retrieval. Document annotation techniques add keywords or comments along with a document or part of information, this attached information is checked first for information retrieval. There are number of techniques which are useful for obtaining best annotation for documents. Techniques contain extracting information from raw data, extraction of structured metadata and many more. In this paper, we are doing a survey on document annotation techniques.
Detection of Outliers Using Hybrid Algorithm on Categorical Datasets

The outlier is an observation that is different from the other remaining values in a data set. Real life contains large number of categorical data. There is some outlier detection algorithms have been designed for categorical data. There are two main problems of outlier detection for categorical data, which are the time complexity and accuracy for detection of outliers in categorical dataset. Categorical dataset have some limited approaches as compared to numeric dataset. This paper describes about some existing algorithms for outlier detection in categorical dataset. The novel Hybrid method which overcomes limitations of previous methods (NAVF and ROAD) has been implemented. The algorithm is implemented and tested on different types of networking datasets, in which detected outliers are virus or intrusion whose behavior is different than behavior in normal networking data.
Bidirectional LSTM-CRF for Adverse Drug Event Tagging in Electronic Health Records

Adverse drug event (ADE) detection is a vital step towards effective pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs. Electronic health records (EHRs) of patients in hospitals contain valuable information regarding the ADEs and hence are an important source for detecting ADE signals. We have developed a deep learning based system that utilizes a three layered deep learning architecture of 1) RNN (bidirectional long short-term memory (bi-LSTM)) for character-level word representation 2) bi-LSTM for context representation and 3) Conditional Random Fields (CRF) for the final output prediction, by integrating them into one deep network architecture. Furthermore, we have developed customized rule-based tokenization techniques for preprocessing text to deal with the noise in the EHR text. In this paper, we share our system architecture and its performance w.r. t the MADE1.0 NLP challenge.
An account of the Speech-to-Song Illusion using Node Structure Theory

In the Speech-to-Song Illusion, repetition of a spoken phrase results in it being perceived as if it were sung. Although a number of previous studies have examined which characteristics of the stimulus will produce the illusion, there is, until now, no description of the cognitive mechanism that underlies the illusion. We suggest that the processes found in Node Structure Theory that are used to explain normal language processing as well as other auditory illusions might also account for the Speech-to-Song Illusion. In six experiments we tested whether the satiation of lexical nodes, but continued priming of syllable nodes may lead to the Speech-to-Song Illusion. The results of these experiments provide evidence for the role of priming, activation, and satiation as described in Node Structure Theory as an explanation of the Speech-to-Song Illusion.