Miracle-dz/UniFAR-FTUs huggingface dataset:
Oxytocin and social pretreatment have similar effects on processing of negative emotional faces in healthy adult males

Oxytocin has been shown to affect several aspects of human social cognition, including facial emotion processing. There is also evidence that social stimuli (such as eye-contact) can effectively modulate endogenous oxytocin levels. In the present study we directly tested whether intranasal oxytocin administration and pre-treatment with social stimuli had similar effects on face processing at the behavioral level. Subjects (N = 52 healthy adult males) were presented with a set of faces with expressions of different valence (negative, neutral, positive) following different types of pretreatment (oxytocin—OT or placebo—PL and social interaction—Soc or no social interaction—NSoc, N = 13 in each) and were asked to rate all faces for perceived emotion and trustworthiness. On the next day subjects' recognition memory was tested on a set of neutral faces and additionally they had to again rate each face for trustworthiness and emotion. Subjects in both the OT and the Soc pretreatment group (as compared to the PL and to the NSoc groups) gave higher emotion and trustworthiness scores for faces with negative emotional expression. Moreover, 24 h later, subjects in the OT and Soc groups (unlike in control groups) gave lower trustworthiness scores for previously negative faces, than for faces previously seen as emotionally neutral or positive. In sum these results provide the first direct evidence of the similar effects of intranasal oxytocin administration and social stimulation on the perception of negative facial emotions as well as on the delayed recall of negative emotional information.
Comparative analysis of the fecal bacterial community of five harbor seals (Phoca vitulina)

The gut microbiota has many beneficial effects on host metabolism and health, and its composition is determined by numerous factors. It is also assumed that there was a co‐evolution of mammals and the bacteria inhabiting their gut. Current knowledge of the mammalian gut microbiota mainly derives from studies on humans and terrestrial animals, whereas those on marine mammals are sparse. However, they could provide additional information on influencing factors, such as the role of diet and co‐evolution with the host. In this study, we investigated and compared the bacterial diversity in the feces of five male harbor seals (Phoca vitulina). Because this small population included two half‐brother pairs, each sharing a common father, it allowed an evaluation of the impact of host relatedness or genetic similarity on the gut microbial community. Fresh feces obtained from the seals by an enema were analyzed by fluorescence in situ hybridization and amplicon sequencing of 16S rRNA genes. The results showed that the bacterial communities in the seals' feces mainly consisted of the phyla Firmicutes (19–43%), Bacteroidetes (22–36%), Fusobacteria (18–32%), and Proteobacteria (5–17%) . Twenty‐one bacterial members present in the fecal samples of the five seals contributed an average relative abundance of 93.7 + 8.7% of the total fecal microbial community. Contrary to all expectations based on previous studies a comparison of the fecal community between individual seals showed a higher similarity between unrelated than related individuals.
Adaptive Extended Pipelined Second-Order Volterra Filter for Nonlinear Active Noise Controller

This correspondence presents an extended pipelined second-order Volterra (EPSOV) filter for active control of nonlinear noise processes. The corresponding nonlinear filtered-x algorithms using the filter bank implementation are also suggested. Compared to the standard SOV filter using the filtered-x least mean square (SOVFXLMS), those modules of the EPSOV filter can be performed simultaneously in a pipelined parallelism fashion, and this would lead to a significant improvement in its total computational efficiency. Results obtained from computer simulations for nonlinear noise processes demonstrate that the proposed method outperforms the SOV.
Application of Clustering Algorithms in Ip Traffic Classification

Classification of network traffic using port-based or payload-based analysis is becoming increasingly difficult with many peer-to-peer(P2P) applications using dynamic port numbers, nat techniques,and encryption to avoid detection. An alternative approach is to classify traffic by exploiting the distinctive characteristics of applications when they communicate on a network. We pursue this latter approach and demonstrate how cluster analysis can be used to effectively identify groups of traffic that are similar using only transport layer statistics. Our work considers two unsupervised clustering algorithms, namely K-Means and DBSCAN, that have previously not been used for network traffic classification. We evaluate these two algorithms, using empirical Internet traces. The experimental results show that both K-Means and DBSCAN work very well and much more quickly Our results indicate that although DBSCAN has lower accuracy compared to K-Means and, DBSCAN produces better clusters.
A scalable solution for personalized recommendations in large-scale social networks

The modern trend in many Web 2.0 applications is that users can interact with the applications in terms of social activity, for example they can express their trust for another user or another user's review etc. Bearing that in mind, creating recommendations for users could go one step further by reclaiming the social network information they share into these applications. This information can be exploited for creating better recommendations but also for optimizing the execution of existing algorithms in large scale datasets. In this paper, we introduce a scalable model for generating personalized user recommendations, which first uses the social information and divides the social graph into subgraphs and then applies Collaborative Filtering on the preferences information related to each subgraph. The results of our experiments show that our proposed model can perform faster than the traditional techniques while producing high level personalized recommendations. Moreover, it allows the parallel execution of the recommendation algorithm in each separate subgraph.
How to Prove that a Circuit is Fault-Free?

A new method of test generation based on the concept of partial test groups to prove the correctness of a combinational circuit is proposed. Stuck-at-faults (SAFs) of any multiplicity are assumed to be present in the circuit and we do not need to enumerate them. Unlike the known approaches, we do not target faults as test objectives. The goal is to verify by each test group the correctness of a selected part of the circuit. In case of passing of all the test groups, the circuit is proven fault-free. In case when not all test groups will pass, fault diagnosis by a sequential process of extending the fault-free core is possible.
Extension Method for Green Supplier Selection

Green supplier selection is a key phase in green supply chain management. The system of evaluation indices for green supplier selection was put forward based on electrical appliance manufacturing enterprise. At the same time, the multi-level extensible synthetic evaluation model based on entropy weight was constructed. The subjectivity which lies in ascertaining factors' weights in lower hierarchy was avoided in this model. So the evaluation result is more objective than other evaluation methods. The evaluation model was applied in green supplier selection in Frestech Co. Ltd. The order preference of suppliers is attained. The best supplier and better suppliers are ascertained for the enterprise. The evaluation process indicates that it is easier to implement this evaluation method in enterprises.
A zero-level packaged RF-MEMS switch with large contact force

This paper presents a novel compact RF-MEMS switch with large contact force. The switch can generate forces above 1.3mN, with 45V applied, while keeping a reasonable footprint. The actuator is based on a single metal 280μm diameter disc, that concentrates electrostatic force on a 8×8μm contact area. Zero level packaging is included in the fabrication, by intergating a metal electroplated shell fabricated above the mechanical part of the switch. The measured contact resistance of AuNi/Au contacts is 0. 3Ω with 45V applied, and the off state capacitance of the packaged switch is 48fF.
Clustering Intelligent Sensor Nodes for Distributed Fault Detection & Diagnosis

Having studied distributed process monitoring using intelligent nodes in a cluster, we now extend the study to include using multiple clusters of nodes to monitor multiple process units interconnected to form a process plant. Our case study is that of a reaction process consisting of two CSTRs and auxiliary equipments such as tanks, heat exchangers etc. The study aims to explore the possibility of the new framework of distributed (and collaborative) process monitoring, in which fault detection and diagnosis is performed at the physical sensor and sensor cluster level. The resulting framework should be a better advisory tool for situation analysis and fault diagnosis by plant operator; while potential extension includes prognosis and the inclusion in control and fault recovery.
Utilising survey data to inform public policy: Comparison of the cost-effectiveness of treatment of ten mental disorders

Background Mental health survey data are now being used proactively to decide how the burden of disease might best be reduced. Aims To study the cost-effectiveness of current and optimal treatments for mental disorders and the proportion of burden avertable by each. Method Data for three affective, four anxiety and two alcohol use disorders and for schizophrenia were compared in terms of cost, burden averted and efficiency of current and optimal treatment. We then calculated the burden unavertable given current knowledge. The unit of health gain was a reduction in the years lived with disability (YLDs). Results Summing across all disorders, current treatment averted 13% of the burden, at an average cost of AUS$30 000 per YLD gained. Optimal treatment at current coverage could avert 20% of the burden, at an average cost of AUS$18 000 per YLD gained. Optimal treatment at optimal coverage could avert 28% of the burden, at AUS$16 000 per YLD gained. Sixty per cent of the burden of mental disorders was deemed to be unavertable. Conclusions The efficiency of treatment varied more than tenfold across disorders. Although coverage of some of the more efficient treatments should be extended, other factors justify continued use of less-efficient treatments for some disorders.
Instanton Counting and Chern-Simons Theory

The instanton partition function of N = 2, D = 4 SU(2) gauge theory is obtained by taking the field theory limit of the topological open string partition function, given by a Chern-Simons theory, of a CY3-fold. The CY3-fold on the open string side is obtained by geometric transition from local IP{sup 1} x IP{sup 1} which is used in the geometric engineering of the SU(2) theory. The partition function obtained from the Chern-Simons theory agrees with the closed topological string partition function of local IP{sup 1} x IP{sup 1} proposed recently by Nekrasov. We also obtain the partition functions for local F{sub 1} and F{sub 2} CY3-folds and show that the topological string amplitudes of all three local Hirzebruch surfaces give rise to the same field theory limit. It is shown that a generalization of the topological closed string partition function whose field theory limit is the generalization of the instanton partition function, proposed by Nekrasov, can be determined easily from the Chern-Simons theory.
Abstraction Reﬁnement for Separation Logic Program Analyses

Abstract domains based on separation logic formulae enable compositional and automated analysis of heap-manipulating programs, and their eﬀectiveness on real-world software has been extensively demonstrated. Tractability is achieved by restricting the set of formulae that are allowed in invariants. Unfortunately, this restriction is generally tied into the deﬁnition of the analysis and cannot vary during analysis of a program. As a result, separation logic analyses can fail to prove safety even when such a proof is possible using the fragment of separation logic on which the analysis is built. In this paper, we propose a method for automatic reﬁnement of separation logic analyses geared towards checking existential properties of data structures. Our approach is based on abduction, a technique for calculating suﬃcient preconditions for commands. We use abduction to work backwards from a spurious error, identify the location at which necessary information was lost, and reﬁne the forward analysis so that this information will be preserved. We show soundness and progress properties of our approach and demonstrate its eﬀectiveness on code patterns arising in dynamic software updates.
Information Systems in Perspective

The purpose of the paper Is to place " r e fo rma t ion sys tems in perspective," t h a t is, to place the e lements of in format ion sys tems in the i r proper posi t ions wi th respect to impor tance , content , and u t i l i ty An in format ion sys tem is in tended to provide in fo rmat ion needed by the user in the conduct of his business Since t h a t covers a b road set of s i tua t ions , the paper is res t r ic ted to the general purpose aspects of file-oriented in fo rmat ion systems, h ighl ight ing the trade-offs of ~mportance to the user, the designer , the implementer , and the operator In par t icular , the management in fo rma t ion sys tem (MIS) is discussed. In thin paper, MIS is defined as "an in fo rmat ion sys t em which provides the manager wi th t h a t in format ion he needs to make dec is ions . "
A Survey of Architectural Design and Implementation Tradeoffs in Network on Chip Systems

As the number of components on a given chip increase in this billion transistor era, System on Chip (SoC) architectures become ever more powerful. Key to this architecture is the ability to integrate multiple heterogeneous components into a single architecture, which requires modularity and abstraction. An integral part of this architectural design is the methods by which the various components communicate with one another. Network on Chip (NoC) architectures attempt to address these concerns by providing various component level architectures with specific interconnection network topologies and routing techniques. The choice of architecture, design, and routing strategy can have a large impact on performance, power dissipation, and efficient usage of area on the silicon chip. Discussed will be the affects of topological, architectural and routing design choices on overall efficiency and performance.
Learning Vector Quantization ( LVQ ) and k-Nearest Neighbor for Intrusion Classification

Attacks on computer infrastructure are becoming an increasingly serious problem nowadays, and with the rapid expansion of computer networks during the past decade, computer security has become a crucial issue for protecting systems against threats, such as intrusions. Intrusion detection is an interesting approach that could be used to improve the security of network system. Different soft-computing based methods have been proposed in recent years for the development of intrusion detection systems. This paper presents a composition of Learning Vector Quantization artificial neural network and k-Nearest Neighbor approach to detect intrusion. A Supervised Learning Vector Quantization (LVQ) was trained for the intrusion detection system; it consists of two layers with two different transfer functions, competitive and linear. Competitive (hidden) and output layers contain a specific number of neurons which are the sub attack types and the main attack types respectively. k-Nearest Neighbor (kNN) as a machine learning algorithm was implemented using different distance measures and different k values, but the results demonstrates that using the first norm instead the second norm and using k=1 gave the best results among other possibilities. The experiments and evaluations of the proposed method have been performed using the NSL-KDD 99 intrusion detection dataset. Hybrid (LVQ_kNN) was able to classify the datasets into five classes at learning rate 0.09 using 23 hidden neurons with classification rate about 89%. KeywordsIntrusion Detection System; Learning Vector Quantization; k-Nearest Neighbor.
Refolding Technologies for Antibody Fragments

Refolding is one of the production technologies for pharmaceutical grade antibody fragments. Detergents and denaturants are primarily used to solubilize the insoluble proteins. The solubilized and denatured proteins are refolded by reducing the concentration of the denaturants or detergents. Several refolding technologies have been used for antibody fragments, comprising dilution, dialysis, solid phase solvent exchange and size exclusion chromatography, as reviewed here. Aggregation suppressor or folding-assisting agents, including arginine hydrochloride, ionic liquids and detergents or denaturants at low concentrations, are included in the refolding solvent to enhance refolding yield.
Expression patterns of Slit and Robo family members in adult mouse spinal cord and peripheral nervous system

The secreted glycoproteins, Slit1-3, are classic axon guidance molecules that act as repulsive cues through their well characterised receptors Robo1-2 to allow precise axon pathfinding and neuronal migration. The expression patterns of Slit1-3 and Robo1-2 have been most characterized in the rodent developing nervous system and the adult brain, but little is known about their expression patterns in the adult rodent peripheral nervous system. Here, we report a detailed expression analysis of Slit1-3 and Robo1-2 in the adult mouse sciatic nerve as well as their expression in the nerve cell bodies within the ventral spinal cord (motor neurons) and dorsal root ganglion (sensory neurons). Our results show that, in the adult mouse peripheral nervous system, Slit1-3 and Robo1-2 are expressed in the cell bodies and axons of both motor and sensory neurons. While Slit1 and Robo2 are only expressed in peripheral axons and their cell bodies, Slit2, Slit3 and Robo1 are also expressed in satellite cells of the dorsal root ganglion, Schwann cells and fibroblasts of peripheral nerves. In addition to these expression patterns, we also demonstrate the expression of Robo1 in blood vessels of the peripheral nerves. Our work gives important new data on the expression patterns of Slit and Robo family members within the peripheral nervous system that may relate both to nerve homeostasis and the reaction of the peripheral nerves to injury.
Design of a frequency synthesizer with digital calibration and spur reduction technology for communications networks application

Chip Design of a frequency synthesizer with digital calibration and spur reduction method operating at 5.0-GHz band is proposed and fabricated in TSMC 0.18-um CMOS process. The proposed PLL with digital calibration reduces VCO gain, Kvco to achieve better phase noise and spur performance in systems networks and architectures for high end computing. The proposed CP reduces phase error and spur at the PFD. When the proposed CP is used, the measured reference spur is reduced to -59.1 dBm. Measurement results show that digital supply voltage of 1.8 V, VCO output frequency is from 4.9~5.12 GHz, and frequency synthesizer phase noise is about -114.82dBc/Hz at 1MHz. Power consumption is 20.1 mW. Including pads, the chip area is 0.83 mm2 for applications of computer, and sensor networks.
High Performance Architecture Design of CAVLC Encoder in H.264/AVC

Context-based adaptive variable length coding (CAVLC) is a new and important feature of the H.264/AVC. Based on analysis and modification of the conventional run-length coding architecture, a novel high efficiency VLSI architecture for H.264/AVC CAVLC encoding is presented in this paper. An approach called arithmetic table structure is exploited to replace look-up-table ROM for reducing hardware resource. Moreover, a modified VLC packer is used to increase the throughout of CAVLC encoder architecture. With the synthesis constrain of 133 MHz, the hardware cost of the proposed design is 13113 gates based on 0.18 CMOS technology. Simulations show that the proposed design is capable of real-time processing for 1920 1080 30fps videos.
The RoadRunner Dynamic Analysis Framework for Concurrent Programs

RoadRunner is a dynamic analysis framework designed to facilitate rapid prototyping and experimentation with dynamic analyses for concurrent Java programs. It provides a clean API for communicating an event stream to back-end analyses, where each event describes some operation of interest performed by the target program, such as accessing memory, synchronizing on a lock, forking a new thread, and so on. This API enables the developer to focus on the essential algorithmic issues of the dynamic analysis, rather than on orthogonal infrastructure complexities. Each back-end analysis tool is expressed as a filter over the event stream, allowing easy composition of analyses into tool chains. This tool-chain architecture permits complex analyses to be described and implemented as a sequence of more simple, modular steps, and it facilitates experimentation with different tool compositions. Moreover, the ability to insert various monitoring tools into the tool chain facilitates debugging and performance tuning. Despite RoadRunner's flexibility, careful implementation and optimization choices enable RoadRunner-based analyses to offer comparable performance to traditional, monolithic analysis prototypes, while being up to an order of magnitude smaller in code size. We have used RoadRunner to develop several dozen tools and have successfully applied them to programs as large as the Eclipse programming environment.
Developing a regression equation for predicting premorbid functioning in an Australian sample using the National Adult Reading Test

Abstract Objective In neuropsychological assessment, predicting the level of premorbid functioning is useful in ascertaining the presence or absence of cognitive decline post‐injury. This study set out to develop regression equations using the National Adult Reading Test (NART) in association with demographic factors to predict the Wechsler Adult Intelligence Scale—Fourth Edition (WAIS‐IV) full‐scale intelligence quotient (FSIQ) and its index scores in a cognitively healthy Australian sample, and to compare the accuracy of these regression equation with the Test of Premorbid Functioning (TOPF) in a clinical sample. Method The 111 cognitively healthy male and female participants aged 18–65-years completed a battery of tests, including the WAIS‐IV, NART, and TOPF. The accuracy of the premorbid IQ estimates was then examined on 22 neurologically impaired participants. Results The regression equation using NART errors explained 39.1% of the variance in WAIS‐IV FSIQ. When age, sex, and education were added to the model, the variance increased to 46.6%. Both the regression equations using NART errors and the TOPF significantly over‐estimated IQ in the clinical sample (p < .001), whereas the regression equation combining errors and demographics, and the TOPF with demographic correction, did not. Conclusion Estimating premorbid functioning is an important aspect of neuropsychological assessment. This paper developed a series of regression equations using the NART which are suitable for the Australian context.