Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymptotic energy profile of a wave packet in disordered chains.

We investigate the long-time behavior of a wave packet initially localized at a single site n₀ in translationally invariant harmonic and anharmonic chains with random interactions. In the harmonic case, the energy profile averaged on time and disorder decays for large |n-n₀| as a power law ≈ C|n-n₀|(⁻η), where η=⁵/₂ and ³/₂ for initial displacement and momentum excitations, respectively. The prefactor C depends on the probability distribution of the harmonic coupling constants and diverges in the limit of weak disorder. As a consequence, the moments of the energy distribution averaged with respect to disorder diverge in time as t(β(ν)) for ν ≥ 2, where β=ν+1-η for ν>η-1 . Molecular-dynamics simulations yield good agreement with these theoretical predictions. Therefore, in this system, the second moment of the wave packet diverges as a function of time despite the wave packet is not spreading. Thus, this only criterion, often considered earlier as proving the spreading of a wave packet, cannot be considered as sufficient in any model. The anharmonic case is investigated numerically. It is found for intermediate disorder that the tail of the energy profile becomes very close to those of the harmonic case. For weak and strong disorders, our results suggest that the crossover to the harmonic behavior occurs at much larger |n-n₀| and larger time.
Sentiment mining from Bangla data using mutual information

Due to the explosion of social networking sites, blogs and review sites (for example, Amazon, Twitter, and Facebook, etc.) it provides an overwhelming amount of textual information. We need to organize, explore, analyze the information for making a better decision from the side of customers and companies. Thus, sentiment analysis is the best way in which it determines the author's feelings expressed in reviews as positive or negative opinions by analyzing an enormous number of documents. In this work, we used Mutual Information (MI) for the feature selection process and also used Multinomial Naive Bayes (MNB) for the classification of Bangla and English reviews. The experimental results demonstrate that the system can achieve satisfactory accuracy for Bangla dataset compare to English dataset where Bangla dataset is generated from Amazon's Watches English dataset.
Efficient clustering via spectral and linear programming methods

There is disclosed a silver halide color photographic material which comprises an acylacetamide yellow dye-forming coupler represented by the following formula (I) and a compound represented by the following formula (II) or (III): Formula (I) wherein R1 represents a monovalent group, Q represents a group of non-metallic atoms required to form together with the C a 3- to 5-membered cyclic hydrocarbon group or a 3- to 5-membered heterocyclic group, and YR represents a residue remaining after removing the acyl group from the acylacetamide yellow dye-forming coupler represented by formula (I), provided that R1 is not a hydrogen atom and does not bond to Q to form a ring, Formula (II) wherein Ra1, Ra2, Ra3, Ra4, and Ra5 each represent a hydrogen atom, or a substituent such as an alkyl group, an alkenyl group, an aryl group, a heterocyclic group, a cyano group, a halogen atom, or a nitro group, provided that Ra1, Ra2, Ra3, Ra4, and Ra5 are not hydrogen atoms at the same time, Formula (III) wherein R3 represents a hydrogen atom, or a substituent such as -OR4 in which R4 represents a hydrogen atom or an alkyl group, an oxy radical, R5' and R5'' each represent an alkyl group; B represents a group of non-metallic atoms required to form together with the nitrogen atom a 5- to 7-membered ring.
K-Means Clustering with Distributed Dimensions

Distributed clustering has attracted significant attention in recent years. In this paper, we study the k-means problem in the distributed dimension setting, where the dimensions of the data are partitioned across multiple machines. We provide new approximation algorithms, which incur low communication costs and achieve constant approximation ratios. The communication complexity of our algorithms significantly improve on existing algorithms. We also provide the first communication lower bound, which nearly matches our upper bound in a certain range of parameter setting. Our experimental results show that our algorithms outperform existing algorithms on real data-sets in the distributed dimension setting.
Assessment of a Novel Watchstanding Schedule on an Operational us Navy Vessel

One watchstanding schedule used in the US Navy is the modified 6-hr on/18-hr off, a 6-hr watch followed by an 18-hr non-watch period. In this four-section watchbill, rather than standing the same 6-hour period each day, two 3-hr watches are “dogged”, resulting in a day shorter than 24-hrs in length. We assessed the 6-hr on/18-hr off schedule to determine its affordance of rest/sleep and psychomotor vigilance, comparing it to a 3-hr on/9-hr off schedule. Results show the 3-hr on/9-hr off schedule is superior, affording 0.63 more hours of rest daily (p=0.054), 0.89 more hours of sleep daily (p=0.024), and decreased variability in psychomotor vigilance (i.e., reaction time and lapses) compared to the standard schedule (p<0.05). Variability of daily rest and sleep is also reduced (p<0.05). Subjective assessments between the 3-hr on/9-hr off and seven other watch schedules showed that participants preferred the 3-hr on/9-hr off schedule. The 3-hr on/9-hr off schedule yields better sleep hygiene, more stable performance and is well-accepted by crewmembers.
Efficient architecture and implementations of AES

An equivalent optimized sub-pipelined architecture is proposed to implement the AES, every round including encryption and decryption needs one clock cycle. The SubBytes/InvSubBytes operation using composite field arithmetic in GF(24) and BlockRAMs respectively. In addition, an efficient key expansion which supports the output of 128 bits key per cycle and allows key changes every cycle is also presented. The novel pipelined design can achieve a throughput of 82.65Gbps on a Xilinx Virtex-4 xc4vlx100 device with composite field implementation of the SubBytes operation. By using 40 BlockRAMs and 8901 slices, the throughput of 64 Gbps is achieved with a frequency of 500MHz.These two designs' throughput/area rate are all over 6 Mbps/Slice indicate that our designs are low-cost for high-speed implementation.
Solving the perspective Shape from Shading problem using a new integration method

Among the techniques that allow a 3D Reconstruction of an object or a scene from a single image, we find the Shape from Shading methods. Those methods can be classified into two categories, the local methods of resolution in which the calculations are done using each pixel and its neighbors and the methods of global resolution that use all pixels of the image at the same time In this paper a local method has been proposed to solve the Shape From Shading problem. Our method is done following two steps: the generation of the normal field (the needle map) then the integration of the needle map for the scene reconstruction. The generation of the normal field is based on a method proposed by Li Jin [14], then we propose a new algorithm for the integration of the normal field generated on an image which is captured by a perspective camera. In our method each pixel is represented by a little lozenge (on the surface plane) oriented by the normal vector and the neighbors of this pixel. The lozenge will be bordered by four perspective lines. The method is tested on some synthetic and real images.
DOPAMINE GENES AND INFANT ATTENTION 1 Polymorphisms in Dopamine System Genes are Associated with Individual Differences in 1 Attention in Infancy 2

1 Knowledge about the functional status of the frontal cortex in infancy is limited. This study 2 investigated the effects of polymorphisms in four dopamine system genes on performance in 3 a task developed to assess such functioning, the Freeze-Frame task, at 9 months of age. 4 Polymorphisms in the catechol-O-methyltransferase (COMT) and the dopamine D4 receptor 5 (DRD4) genes are likely to impact directly on the functioning of the frontal cortex, while 6 polymorphisms in the dopamine D2 receptor (DRD2) and dopamine transporter (DAT1) 7 genes might influence frontal cortex functioning indirectly via strong fronto-striatal 8 connections. A significant effect of the COMT ValMet polymorphism was found. Infants 9 with the Met/Met genotype were significantly less distractible than infants with the Val/Val 10 genotype in Freeze-Frame trials presenting an engaging central stimulus. In addition, there 11 was an interaction with the DAT1 3’ VNTR polymorphism; the COMT effect was only 12 present in infants who did not have two copies of the DAT1 10-repeat allele. These findings 13 indicate that dopaminergic polymorphisms already affect selective aspects of attention in 14 infancy, and further validate the Freeze-Frame task as a frontal cortex task. 15 16
A novel continuous model to approximate time Petri nets: modelling and analysis

In order to approximate discrete-event systems in which there exist considerable states and events, David and Alla define a continuous Petri net (CPN). So far, CPNs have been a useful tool not only for approximating discrete-event systems but also for modelling continuous processes. Due to different ways of calculating instantaneous firing speeds of transitions, various continuous Petri net models, such as the CCPN (constant speed CPN), VCPN (variable speed CPN) and the ACPN (asymptotic CPN), have been proposed, where the continuous flow is specified uniquely by maximal firing speeds. However, in applications such as chemical processes there exist situations where the continuous flow must be above some minimal speed or in the range of minimal and maximal speeds. In this paper, from the point of view of approximating a time Petri net, the CPN is augmented with maximal and minimal firing speeds, and a novel continuous model, i.e., the Interval speed CPN (ICPN) is defined. The enabling and firing semantics of transitions of the ICPN are discussed, and the facilitating of continuous transitions is classified into three levels: 0-level, 1-level and 2-level. Some policies to resolve the conflicts and algorithms to undertake the behavioural analysis for the ICPN are developed. In addition, a chemical process example is presented.
“Supply Disruptions, Asymmetric Information, and a Dual Sourcing Option

We study a manufacturer’s strategic use of a dual-sourcing option when facing suppliers who possess private information about their likelihood of experiencing a supply disruption. The manufacturer can diversify its supply by ordering from both suppliers, but we find that the cost of doing so is inflated under asymmetric information due to the suppliers’ incentives to misrepresent their reliabilities. If the manufacturer instead sole-sources, competition between the suppliers curbs their informational rents. Therefore, asymmetric information pushes the manufacturer away from diversification and towards sole-sourcing. Surprisingly, the additional cost that asymmetric information imposes on diversification may cause the manufacturer to cease diversifying in reaction to uniformly eroding supply base reliability, while it would do just the opposite under symmetric information. Despite these trends away from diversification, the value of the dual-sourcing option should not be underestimated for manufacturers who are unsure of their suppliers’ reliabilities — the dual-sourcing option is actually more valuable under asymmetric information than under symmetric information if the manufacturer’s cost of replacing a unit lost due to a disruption is moderate. We also analyze the effect of codependence between supply disruptions, and find that a reduction in supplier codependence increases the manufacturer’s value of information. Therefore, strategic actions to reduce codependence between supply disruptions should not be seen as a substitute for learning about the suppliers’ reliabilities. ∗Department of Industrial & Operations Engineering, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI 48109. E-mail: zhibiny@umich.edu †Department of Industrial & Operations Engineering, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI 48109. E-mail: ayding@umich.edu ‡Department of Industrial & Operations Engineering, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI 48109. E-mail: babich@umich.edu §Ross School of Business, University of Michigan, 701 Tappan St., Ann Arbor, MI 48109. E-mail: dbeil@bus.umich.edu
Role Strains and Mood in Husbands of Women with Fibromyalgia Syndrome: A Test of the Stress Process Model

Spouses of patients experience role strains as a result of informal caregiving, which has been associated with mood in numerous research studies. However, most research is on female caregivers, and little is known about the experi- ence of male spouses, or of the caregiving provided to fibromyalgia patients. The Stress Process Model was used to exam- ine mediators and moderators of the relation between role strain and mood among 135 husbands of women with fibromy- algia. Results indicated that the more activities of daily living and instrumental activities of daily living performed by the husband, the greater the role strain. Role strain was associated with worse mood. A test of the Stress Process Model sup- ported a partial mediation model, where social support and emotion-focused coping partially mediated the relation be- tween role strain and mood. No evidence was found for a moderation model or for problem-focused coping as a mediator. Our research suggests significant impairment and caregiving needs among this patient population, which in turn relates to the mood of the husband who is also an informal caregiver. Our findings also support the Stress Process Model in explain- ing the complexity of caregiving effects. The results of the study suggest avenues for intervention for individuals strained by their partners' illness.
Design of Software Security Verification with Formal Method Tools

Summary Formal methods ensure the stability and reliability of soft-ware systems by using mathematical principles and proving conformance to a given set of requirements. The stable and reliable operation of software is especially important for system applications dealing with security. Although very effective in identifying a non-conformance in security requirements, formal methods typically involve a steep learning curve before full adoption. Automated tools can be used to alleviate difficulties associated with formal methods. An observation is made that the existing attempts to apply formal methods to check conformance to security requirements, have not efficiently taken advantage of such tools. Therefore, this paper proposes a novel methodology to leverage well-known formal method tools to verify how closely a security software product satisfies its requirements. More specifically, this paper formally verifies an Access Control System (ACS) using RoZ and Z/EVES, two of the many verification tools available for ensuring the integrity of software applications. For this, a UML model of ACS with Z annotations is first created. Next, the model is transformed into a Z specification which is, in turn, verified by the Z/EVES prover. Using this process, one can also find security vulnerabilities created during a development process. Index Terms—formal methods, formal specification, formal verification, software security verification, RoZ, Z/EVES
Information Processing Strategies and Pathways in the Primate Visual System .

The mammalian visual pathway is an amazingly complex and intricate system, capable of processing vast amounts of sensory information for use in the precise control of both immediate and long-term behavior. In many respects, the system is very well engineered for efficient extraction and encoding of information. To give just one example, it is well known that the spacing between photoreceptors in the region of highest acuity is closely matched to the limits of resolution imposed by the physical optics of the eye (Snyder & Miller, 1977). In this chapter, we consider how the notion of "good engineering" can help to understand information processing strategies used throughout the visual system. While some aspects of human vision are mentioned, our analysis will focus on the macaque monkey, which has a superb visual system very similar to that of humans.
Domain Driven Two-Phase Feature Selection Method Based on Bhattacharyya Distance and Kernel Distance Measurements

This paper proposes a two-phase feature selection method specific for bioinformatics domain from classification perspective in data mining. In the first phase, Bhattacharyya distance measurement is used for filtering the majority of irrelevant genes. Upon the basis, we apply floating sequential search method (FSSM) to further select informative gene set using kernel distance as measurement of class separability. The verification of colon tissue dataset using support vector machines (SVMs) proves that informative gene set selected by our method is acceptable for disease identification.
Automatic Composition and Optimization of Multicomponent Predictive Systems With an Extended Auto-WEKA

Composition and parameterization of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps are a challenging task. Auto-WEKA is a tool to automate the combined algorithm selection and hyperparameter (CASH) optimization problem. In this paper, we extend the CASH problem and Auto-WEKA to support the MCPS, including preprocessing steps for both classification and regression tasks. We define the optimization problem in which the search space consists of suitably parameterized Petri nets forming the sought MCPS solutions. In the experimental analysis, we focus on examining the impact of considerably extending the search space (from approximately 22000 to 812 billion possible combinations of methods and categorical hyperparameters). In a range of extensive experiments, three different optimization strategies are used to automatically compose MCPSs for 21 publicly available data sets. The diversity of the composed MCPSs found is an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. We also present the results on seven data sets from real chemical production processes. Our findings can have a major impact on the development of high-quality predictive models as well as their maintenance and scalability aspects needed in modern applications and deployment scenarios. Note to Practitioners—The extension of Auto-WEKA to compose and optimize multicomponent predictive systems (MCPSs) developed as part of this paper is freely available on GitHub under GPL license, and we encourage practitioners to use it on a broad variety of classification and regression problems. The software can either be used as a blackbox—where search space is made of all possible WEKA filters, predictors, and metapredictors (e.g., ensembles)—or as an optimization tool on a subset of preselected machine learning methods. The application has a graphical user interface, but it can also run from command line and can be embedded in any project as a Java library. There are three main outputs once an Auto-WEKA run has finished: 1) the trained MCPS ready to make predictions on unseen data; 2) the WEKA configuration (i.e., parameterized components); and 3) the Petri net in a Petri Net Markup Language format that can be analyzed using any tool supporting this standard language. There are, however, some practical considerations affecting the quality of the results that must be taken into consideration, such as the CPU time budget or the search starting point. These are extensively discussed in this paper.
Hybrid compression techniques for energy efficient Wireless Sensor Networks

In today's Wireless Sensor Networks, energy efficiency is the key interest as these sensors are small in size and have no refillable battery. These sensors are deployed to accomplish various tasks e.g. to measure the environmental attributes such as temperature, humidity, pollution level of sea water; to monitor some ongoing activities such as surveillance, tracking of suspects; and etc. Also their batteries don't last very long since it requires continues transmission of data towards the user/customer/sink. Our main goal here is how to provide simple and effective approach to provide hybrid data compression (spatial and temporal both) in the wireless sensor network in order to improve energy efficiency of the network. WSNs are deployed in great amount over the detected environment and it has been observed that the data from neighboring sensors usually have large correlation so sometimes it is waste of energy for the transmission of this duplicate information from neighboring sensors rather we can exploit this spatial correlation to reduce the heavy burden of data in transmission. Besides it has been seen that on a particular sensor, their readings are also correlated for a limited duration so we can exploit this temporal correlation to reduce the amount of data. Our scheme is not suitable to real time data monitoring rather it is quite better for delay tolerant services as it requires a little time for processing of data before transmitting it to the sink. Neighboring correlated sensors forms a cluster and their data are collectively stored in two dimensional Matrix and afterwards, Compression techniques are applied in both domain (Space and Time). That's why it is termed as Hybrid Compression. After applying these compression methods the data are transmitted towards the sink. Our result shows that our proposed scheme gives satisfactory approximation of received data at the sink.
Effects of cortisol on memory in women with borderline personality disorder: role of co-morbid post-traumatic stress disorder and major depression

Background Stress and cortisol administration are known to have impairing effects on memory retrieval in healthy humans. These effects are reported to be altered in patients with major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) but they have not yet been investigated in borderline personality disorder (BPD). Method In a placebo-controlled cross-over study, 71 women with BPD and 40 healthy controls received either placebo or 10 mg of hydrocortisone orally before undertaking a declarative memory retrieval task (word list learning) and an autobiographical memory test (AMT). A working memory test was also applied. Results Overall, opposing effects of cortisol on memory were observed when comparing patients with controls. In controls, cortisol had impairing effects on memory retrieval whereas in BPD patients cortisol had enhancing effects on memory retrieval of words, autobiographical memory and working memory. These effects were most pronounced for specificity of autobiographical memory retrieval. Patients with BPD alone and those with co-morbid PTSD showed this effect. We also found that co-morbid MDD influenced the cortisol effects: in this subgroup (BPD + MDD) the effects of cortisol on memory were absent. Conclusions The present results demonstrate beneficial effects of acute cortisol elevations on hippocampal-mediated memory processes in BPD. The absence of these effects in patients with co-morbid MDD suggests that these patients differ from other BPD patients in terms of their sensitivity to glucocorticoids (GCs).