Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding Literary Themes With Relevance Feedback

A common task in text analysis is find conceptually-linked passages of text such as examples of themes, imagery, and descriptions of events. In the past, researchers looking to find such passages have had to rely on searching for sets of keywords. However, themes, descriptions, and imagery may surface with many different phrasings, making retrieval based on keyword search difficult. We investigated the application of relevance feedback to this problem. First, we implemented a relevance feedback system for sentence-length text. Then, we evaluated the system’s ability to support gathering examples of themes in the works of Shakespeare. Participants with at least undergraduate backgrounds in English language or literature used either our system(N = 11) or keyword search(N = 12) to retrieve examples of a theme chosen by a professional Shakespeare scholar. Their examples were judged on relevance by our expert collaborator. Our results suggest that relevance feedback is effective. On average, participants with relevance feedback gathered more sentences, and more relevant sentences, with fewer searches than participants with keyword search. However, a larger study is needed to establish statistical significance.
Efficient depth intra mode decision by reference pixels classification in 3D-HEVC

The uniform intra prediction increases the intra prediction modes up to 35 and brings better coding efficiency in HEVC. Besides, depth modelling modes (DMMs) are introduced in depth intra coding of 3D-HEVC to preserve sharp edges and avoid ringing artifacts in a synthesized view. Meanwhile, the encoding time of depth intra coding rapidly increases due to a huge number of intra mode candidates. Based on the spatial correlation of a depth map, we find that not all of the intra modes are necessary to be considered in most cases. Hence, a fast content-dependent depth intra mode decision algorithm is raised in this paper by classifying the spatial distribution of the reference pixels. Simulation results show that the proposed adaptive fast algorithm can save 21%-35% time of the depth coding with the insignificant bit rate increase compared with the state-of-the-art algorithm.
Discovery and Segmentation of Activities in Video

Hidden Markov models (HMMs) have become the workhorses of the monitoring and event recognition literature because they bring to time-series analysis the utility of density estimation and the convenience of dynamic time warping. Once trained, the internals of these models are considered opaque; there is no effort to interpret the hidden states. We show that by minimizing the entropy of the joint distribution, an HMM's internal state machine can be made to organize observed activity into meaningful states. This has uses in video monitoring and annotation, low bit-rate coding of scene activity, and detection of anomalous behavior. We demonstrate with models of office activity and outdoor traffic, showing how the framework learns principal modes of activity and patterns of activity change. We then show how this framework can be adapted to infer hidden state from extremely ambiguous images, in particular, inferring 3D body orientation and pose from sequences of low-resolution silhouettes.
A case of obsessional fears treated by brief hypno-imagery intervention.

Abstract A young patient, with a long-standing history of 1) obsessive fears, 2) avoidant behaviors, 3) a lack of a sense of self-efficacy, and 4) a severe sense of hopelessness and vulnerability, was treated through the use of hypnosis. The client's creative imagination was used to encourage a feeling of self-efficacy. Authoritarian suggestions for relaxation and progressive but imperceptible growth modified the client's helplessness and yearning for guidance in a paradoxical, therapeutic direction. Improvement was rapid (seven sessions) and was stable at six and eighteen months follow-up. It is theorized that since the therapy was founded upon already practiced abilities (i.e., the ability to imagine and the capacity to respond to this imagination), the actual changes required of the client were minimal.
2 The Mixed Integer Rounding and Gomory Mixed Integer Inequalities

A separation heuristic for mixed integer programs is presented that theoretically allows one to derive several families of “strong” valid inequalities for specific models and computationally gives results as good as or better than those obtained from several existing separation routines including flow cover and integer cover inequalities. The heuristic is based on aggregation of constraints of the original formulation and mixed integer rounding inequalities.
Living Machinery - Advantages of Webble Technologies for Teaching and Learning

Webble technology is the most recent form of Meme Media following Richard Dawkins’ seminal ideas about memetics and relying on Yuzuru Tanaka’s interpretation of the Meme concept in the form of IntelligentPad. The reach of Webble technologies is demonstrated by means of a particular Web-based interactive laboratory. It arises the question how this novel technology is perceived and what impact it has on teaching and learning. This paper describes how to apply different learning methods to use the Webble technology for varying
Essential Role of the Disulfide-bonded Loop of Chromogranin B for Sorting to Secretory Granules Is Revealed by Expression of a Deletion Mutant in the Absence of Endogenous Granin Synthesis

Sorting of regulated secretory proteins in the TGN to immature secretory granules (ISG) is thought to involve at least two steps: their selective aggregation and their interaction with membrane components destined to ISG. Here, we have investigated the sorting of chromogranin B (CgB), a member of the granin family present in the secretory granules of many endocrine cells and neurons. Specifically, we have studied the role of a candidate structural motif implicated in the sorting of CgB, the highly conserved NH2-terminal disulfide– bonded loop. Sorting to ISG of full-length human CgB and a deletion mutant of human CgB (Δcys-hCgB) lacking the 22–amino acid residues comprising the disulfide-bonded loop was compared in the rat neuroendocrine cell line PC12. Upon transfection, i.e., with ongoing synthesis of endogenous granins, the sorting of the deletion mutant was only slightly impaired compared to full-length CgB. To investigate whether this sorting was due to coaggregation of the deletion mutant with endogenous granins, we expressed human CgB using recombinant vaccinia viruses, under conditions in which the synthesis of endogenous granins in the infected PC12 cells was shut off. In these conditions, Δcys-hCgB, in contrast to full-length hCgB, was no longer sorted to ISG, but exited from the TGN in constitutive secretory vesicles. Coexpression of full-length hCgB together with Δcys-hCgB by double infection, using the respective recombinant vaccinia viruses, rescued the sorting of the deletion mutant to ISG. In conclusion, our data show that (a) the disulfide-bonded loop is essential for sorting of CgB to ISG and (b) the lack of this structural motif can be compensated by coexpression of loop-bearing CgB. Furthermore, comparison of the two expression systems, transfection and vaccinia virus–mediated expression, reveals that analyses under conditions in which host cell secretory protein synthesis is blocked greatly facilitate the identification of sequence motifs required for sorting of regulated secretory proteins to secretory granules.
Flexible Design of Finite Blocklength Wiretap Codes by Autoencoders

With an increasing number of wireless devices, the risk of being eavesdropped increases as well. From information theory, it is well known that wiretap codes can asymptotically achieve vanishing decoding error probability at the legitimate receiver while also achieving vanishing leakage to eavesdroppers. However, under finite blocklength, there exists a tradeoff among different parameters of the transmission. In this work, we propose a flexible wiretap code design for Gaussian wiretap channels under finite blocklength by neural network autoencoders. We show that the proposed scheme has higher flexibility in terms of the error rate and leakage tradeoff, compared to the traditional codes.
Reverse Atrial Remodeling and Resolution of Mitral Regurgitation after Rhythm Control in Atrial Fibrillation: A Case Report

Atrial fibrillation is a common cardiac arrhythmia worldwide. In patients with hyperthyroidism, atrial fibrillation is the most common comorbid cardiac condition. Here, the authors report a case of a 47-year-old female with no significant medical history who presented with heart failure symptoms. Further analysis confirmed atrial fibrillation with a dilated atria and severe mitral regurgitation. In addition, she was found to be hyperthyroid. Accordingly, electrical cardioversion treatment was initiated, and her hyperthyroidism was managed. This resulted in her normal sinus rhythm being restored and subsequently being maintained. A repeat echocardiography 6 months later showed resolution of mitral regurgitation, improvement of atrial size and normalization of the left ventricular systolic function. Therefore, based on this case report, the authors suggest that atrial remodeling and functional mitral regurgitation secondary to atrial dilatation can be reversed by restoring and maintaining the sinus rhythm.
Recent trends in software and hardware for GPGPU computing: A comprehensive survey

With the growth of Graphics Processor (GPU) programmability and processing power, graphics hardware has become a compelling platform for computationally demanding tasks in a wide variety of application domains. This state of art paper gives the technical motivations that underlie GPU computing and describe the hardware and software developments that have led to the recent interest in this field.
Enhanced Gradient for Training Restricted Boltzmann Machines

Restricted Boltzmann machines (RBMs) are often used as building blocks in greedy learning of deep networks. However, training this simple model can be laborious. Traditional learning algorithms often converge only with the right choice of metaparameters that specify, for example, learning rate scheduling and the scale of the initial weights. They are also sensitive to specific data representation. An equivalent RBM can be obtained by flipping some bits and changing the weights and biases accordingly, but traditional learning rules are not invariant to such transformations. Without careful tuning of these training settings, traditional algorithms can easily get stuck or even diverge. In this letter, we present an enhanced gradient that is derived to be invariant to bit-flipping transformations. We experimentally show that the enhanced gradient yields more stable training of RBMs both when used with a fixed learning rate and an adaptive one.
Hydrolysis of local anesthetics by human serum cholinesterase.

1. The special features of ultraviolet spectrophotometry in its application to the study of esterase actions on local anesthetics and benzoylcholine were described. 2. Evidence for the identity of "procainesterase" and cholinesterase of human serum was presented. Acetylcholine (or benzoylcholine) and procaine (or other local anesthetic esters) can act as competitive inhibitors for the same enzyme. Although the hydrolysis of acetylcholine is about 400 times faster than that of procaine, the affinity of procaine for this esterase is about 220 times greater. 3. Kinetic data of esterase action on procaine were given. 4. Six local anesthetics other than procaine were investigated. Piperocaine was hydrolyzed faster, all other esters more slowly than procaine. The affinities between esterase and all local anesthetics were high, in the case of tetracaine about the same as physostigmine.
An efficient decoding of CAVLC in H.264/AVC video coding standard

In general, a large number of the memory accesses are required to decode the CAVLC in H.264/AVC. This is a serious problem for applications such as a DMB and videophone services because of the considerable amount of power that is consumed in accessing the memory. In order to overcome this problem, we propose an efficient coeff/spl I.bar/token VLD and a new run/spl I.bar/before VLD based on arithmetic operations. The simulation results show that the proposed algorithm achieves an approximate 65-88% savings in memory access without degrading video quality as compared to the conventional CAVLC decoding.
Common mode voltage reduction of quasi‐Z source indirect matrix converter

Quasi‐Z source indirect matrix converter (IMC) combines both advantages of conventional IMC and quasi‐Z source inverter, for example, no direct current (DC) link capacitor, compact all‐silicon power converter, bidirectional power flow, input power factor controllable, and high voltage gain; moreover, it does not require additional input filter, because continuous quasi‐Z source network integrates LC filter function. However, there is no literature to disclose common mode voltage (CMV) issue of quasi‐Z source IMC. In this paper, for the first time, the CMV issue and reduction of quasi‐Z source IMC are investigated. Firstly, the CMV of quasi‐Z source IMC is analyzed when using current typical modulation method, which follows the brief introduction of topology and modulation method for quasi‐Z source IMC, and the factors that affect the CMV are figured out; Secondly, referring to the CMV reduction methods of conventional IMC, two solutions named as Methods I and II to reduce the CMV for quasi‐Z source IMC are developed, which are achieved in the inverter stage; the third CMV reduction method is proposed in the rectifier stage through redefining the six sectors of the rectifier stage, which can implement zero current commutation. Experimental bench is built to test three approaches for reducing the CMV of quasi‐Z source IMC. Comparative evaluation is carried out between three methods and conventional modulation method. Experimental results verify that three methods can significantly reduce the CMV of quasi‐Z source IMC, with the CMV peak value reduction of 42%, but they present different features in terms of input and output current THDs, switching loss, CMV root mean square (RMS) value, modulation index limitation, and so on. Copyright © 2015 John Wiley & Sons, Ltd.
Bisimulation for quantum processes

Quantum cryptographic systems have been commercially available, with a striking advantage over classical systems that their security and ability to detect the presence of eavesdropping are provable based on the principles of quantum mechanics. On the other hand, quantum protocol designers may commit much more faults than classical protocol designers since human intuition is much better adapted to the classical world than the quantum world. To offer formal techniques for modeling and verification of quantum protocols, several quantum extensions of process algebra have been proposed. One of the most serious issues in quantum process algebra is to discover a quantum generalization of the notion of bisimulation, which lies in a central position in process algebra, preserved by parallel composition in the presence of quantum entanglement, which has no counterpart in classical computation. Quite a few versions of bisimulation have been defined for quantum processes in the literature, but in the best case they are only proved to be preserved by parallel composition of purely quantum processes where no classical communications are involved. Many quantum cryptographic protocols, however, employ the LOCC (Local Operations and Classical Communications) scheme, where classical communications must be explicitly specified. So, a notion of bisimulation preserved by parallel composition in the circumstance of both classical and quantum communications is crucial for process algebra approach to verification of quantum cryptographic protocols. In this paper we introduce a novel notion of bisimulation for quantum processes and prove that it is congruent with respect to various process algebra combinators including parallel composition even when both classical and quantum communications are present. We also establish some basic algebraic laws for this bisimulation. In particular, we prove uniqueness of the solutions to recursive equations of quantum processes, which provides a powerful proof technique for verifying complex quantum protocols.
Probabilistic modal logic as a foundation for knowledge management

Probabilistic modal logic has been used for knowledge management and especially for managing uncertain knowledge. In this paper the logical roots of probabilistic modal logic is analyzed. It is shown that probabilistic modal logic has its roots in a qualitative view of probability. It has been discussed the necessary conditions that such a qulaitative relation should have in order to construct a probability model as it is used in probabilistic modal logic.
Social stream marketing on Facebook: a case study

Recently, social network marketing has gained tremendously in popularity. There are increasing numbers of web-based companies that focus their marketing strategies on social network platforms such as Facebook, Twitter, etc. Even if social network marketing is perceived as a huge success, little or nothing is known about how well such social network-based marketing campaigns perform. To contribute to this field of research, we present in this paper the results of an ad-driven social network-based marketing campaign centred on the social network Facebook. In particular, we demonstrate in the extent to which ads placed on the user's social stream and generated by the Facebook tools and applications can increase the number of visits, profit or ROI of a web-based platform called VirWoX. In addition to these findings, we present an analysis of a number of simple real-time measures to detect the most 'valuable' users on Facebook. To the best of our knowledge, this is the first work that provides such detailed results of a social stream marketing campaign using Facebook. This paper is relevant for engineers, researchers or teachers who are interested in the field of social media marketing.
A fingerprinting indoor localization algorithm based deep learning

Fingerprinting in essence uses a machine to infer physicals locations from radio map data. This machines are usually either probabilistic and neural networks consisting of one layer. In this propose we use deeper machines (DNN, DBN and GB-DBN) to increase the estimation accuracy and reduce generalization error on dynamic indoor environment. Also we investigated the impact of pre-training algorithm on fingerprinting indoor location systems. Experimental results demonstrate that deep models provide an efficient generalization performance on indoor environments. They have the disadvantage that demand high processing resources when they are trained on off-line phase, however, deep models are swift to predict during on-line phase.
What Does Motor Efference Copy Represent? Evidence from Speech Production

How precisely does the brain predict the sensory consequences of our actions? Efference copy is thought to reflect the predicted sensation of self-produced motor acts, such as the auditory feedback heard while speaking. Here, we use magnetoencephalographic imaging (MEG-I) in human speakers to demonstrate that efference copy prediction does not track movement variability across repetitions of the same motor task. Specifically, spoken vowels were less accurately predicted when they were less similar to a speaker's median production, even though the prediction is thought to be based on the very motor commands that generate each vowel. Auditory cortical responses to less prototypical speech productions were less suppressed, resembling responses to speech errors, and were correlated with later corrective movement, suggesting that the suppression may be functionally significant for error correction. The failure of the motor system to accurately predict less prototypical speech productions suggests that the efferent-driven suppression does not reflect a sensory prediction, but a sensory goal.
Enthalpic Efficiency of Ligand Binding

The thermodynamics of ligand-protein binding has received much attention recently. In the present contribution we focus on the enthalpic component of binding. The dissociation constant, pK(d), was decomposed into enthalpic and entropic components (pK(d) = pK(H) + pK(S)), and pK(H), defined as pK(H) = -ΔH/(2.303·RT) was used to characterize the enthalpy contribution to binding. It was found that the maximal achievable pK(H) decreases with increasing molecular size. This is in contrast to maximal pK(d) that increases with molecular size until it achieves a plateau. Size-independent enthalpic efficiency (SIHE) was defined as SIHE = pK(H)/40·HA(0.3), with HA being the number of heavy atoms. SIHE allows a size unbiased comparative binding characterization of compounds. It can find use in hit and lead selection and also in monitoring optimization in drug discovery programs. The physical background of decreasing maximal pK(H) with molecular size is discussed, and its consequences to drug discovery are analyzed. It is concluded that the feasibility of simultaneous optimization of affinity and enthalpy diminishes with increasing molecular size. Consequently, binding thermodynamics considerations are to be applied primarily in hit prioritization and hit-to-lead optimization.
Efficient and Effective Energy Balancing For Wireless Sensor Networks

A decentralized routing algorithm, called Game Theoretic Energy Balance Routing Protocol (GTEB), is proposed to extend the network lifetime by balancing energy consumption in a larger network area using geographical routing protocols (GRPs). The objective of the proposed protocol is to make sensor nodes deplete their energy at approximately the same time, which is achieved by addressing the load balance problem at both the region and node levels. In the region level, evolutionary game theory (EGT) is used to balance the traffic load to available sub-regions. At the node level, classical game theory (CGT) is used to select the best node to balance the load in the selected sub-region. This twolevel approach is shown to be an effective solution for load balancing and extending network lifetime. This paper shows the use of evolutionary and classical game theories in designing a robust protocol that offers significant improvement over existing protocols in extending network
Modelling Workflows using Petri Nets with Multiple Instances

In this paper we present a graphical language for workflow specification and some examples of its use. This graphical language is expected to be the basis for more complete formalisms, and focuses in the fact that there are some processes for which multiple instances are executed, and that these instances may interact (e. g. , by sharing resources). Each instance may represent a different actor or a different instance of a task performed by an actor (in addition, the different actors and tasks may share some resource). For this reason, we believe that the notion of multiple instances is essential to the specification of workflows and distributed systems.