Miracle-dz/UniFAR-FTUs huggingface dataset:
03 05 13 5 v 1 1 5 M ay 2 00 3 New formulae for solutions of quantum Knizhnik-Zamolodchikov equations on level-4 and correlation functions

This paper is continuation of our previous papers [12] and [1 3]. We discuss in more detail a new form of solution to the quantum Knizhnik-Zamolodchiko v equation [qKZ] on level−4 obtained in the paper [13] for the Heisenberg XXX spin chain. The main advantage of this form is it’s explicit reducibility to one-dimensional integrals. We argue that the deep mathematical reason for this is some special cohomologies of deformed Jacobi var ieties. We apply this new form of solution to the correlation functions using the Jimbo-Miwa conjecture [3]. A formula (46) for the correlation functions obtained in this way is in a good agree m nt with the ansatz for the emptiness formation probability from the paper [12]. Our previous con jecture on a structure of correlation functions of the XXX model in the homogeneous limit through t he Riemann zeta functions at odd arguments is a corollary of the formula (46). 1on leave of absence from the Institute for High Energy Physic s, Protvino, 142284, Russia 2Membre du CNRS 1
Responses of Saturated Sand Surrounding A Bucket Foundations Under Horizontal Vibration Loading~!2009-07-18~!2009-08-23~!2010-02-02~!

In this paper we investigated the responses of saturated sand under horizontal vibration loading induced by a bucket foundation. It is shown that the saturated sand liquefies gradually since the vibration loading is applied on. The maximum displacement on the surface of sand layer occurs near the loading end and in this zone the sand is compressed and moves upwards. The liquefaction zone is developed from the upper part near the loading side and stopped gradually.
Micro-osmotic pumps for continuous release of the tyrosine kinase inhibitor bosutinib in juvenile rats and its impact on bone growth

Background Bosutinib is a third-generation dual tyrosine kinase inhibitor (TKI) inhibiting Abl and Src kinases. It was developed to act on up-regulated tyrosine kinases (TKs) like BCR-ABL in Philadelphia chromosome positive (Ph+) chronic myeloid leukemia (CML) when resistance to first- and second-generation TKIs developed. However, first- and second-generation TKIs show off-target effects on bone metabolism, whereas studies on skeletal adverse effects of bosutinib are still lacking. Therefore, it was the aim of this study to continuously expose juvenile rats to bosutinib and to analyze its influence on the growing bone. Material/Methods Starting after weaning, 4-week-old Wistar rats were chronically exposed over a 28-day period to varying concentrations of bosutinib, which were continuously administered subcutaneously via implanted Alzet® micro-osmotic pumps. After necropsy, the length of the femora and tibiae were analyzed. Results Continuous administration of bosutinib by micro-osmotic pumps led to serum drug levels in the lower therapeutic range, was well tolerated, and exhibited only minor adverse effects on the growing skeleton. Conclusions Micro-osmotic pumps represent a convenient system for continuous TKI release in young growing rats. Compared to first- and second-generation TKIs, bosutinib seems to exert fewer adverse effects on the growing bone.
Muscle Activation during Gait in Children with Duchenne Muscular Dystrophy

The aim of this prospective study was to investigate changes in muscle activity during gait in children with Duchenne muscular Dystrophy (DMD). Dynamic surface electromyography recordings (EMGs) of 16 children with DMD and pathological gait were compared with those of 15 control children. The activity of the rectus femoris (RF), vastus lateralis (VL), medial hamstrings (HS), tibialis anterior (TA) and gastrocnemius soleus (GAS) muscles was recorded and analysed quantitatively and qualitatively. The overall muscle activity in the children with DMD was significantly different from that of the control group. Percentage activation amplitudes of RF, HS and TA were greater throughout the gait cycle in the children with DMD and the timing of GAS activity differed from the control children. Significantly greater muscle coactivation was found in the children with DMD. There were no significant differences between sides. Since the motor command is normal in DMD, the hyper-activity and co-contractions likely compensate for gait instability and muscle weakness, however may have negative consequences on the muscles and may increase the energy cost of gait. Simple rehabilitative strategies such as targeted physical therapies may improve stability and thus the pattern of muscle activity.
The Natural Vectorial Total Variation Which Arises from Geometric Measure Theory

Several ways to generalize scalar total variation to vector-valued functions have been proposed in the past. In this paper, we give a detailed analysis of a variant we denote by $\text{TV}_J$, which has not been previously explored as a regularizer. The contributions of the manuscript are twofold: on the theoretical side, we show that $\text{TV}_J$ can be derived from the generalized Jacobians from geometric measure theory. Thus, within the context of this theory, $\text{TV}_J$ is the most natural form of a vectorial total variation. As an important feature, we derive how $\text{TV}_J$ can be written as the support functional of a convex set in $\mathcal{L}^2$. This property allows us to employ fast and stable minimization algorithms to solve inverse problems. The analysis also shows that in contrast to other total variation regularizers for color images, the proposed one penalizes across a common edge direction for all channels, which is a major theoretical advantage. Our practical contribution consist o.
Physics inspired CS based underwater acoustic channel estimation

We propose real-time channel tracking for underwater acoustic communications under dynamic sea conditions. The key idea is to employ sophisticated sparse sensing techniques that are cognizant of stable or slowly time-varying channel components against a transient background. Shallow water acoustic channel is generally challenging to track under moderate to rough sea conditions. This is primarily due to non-stationary highly transient elements within the channel delay spread resulting from rapidly fluctuating multipath arrivals from unpredictable surface wave reflections. The proposed channel estimation method exploits two channel characteristics: (i) Inherent sparsity of the time-varying channel in the two-dimensional dual (Fourier) domain; and (ii) Relative dominance of the direct arrival and slowly varying multipath arrivals against the otherwise non-stationary channel impulse response. Specifically, we utilize this apriori information to compressed sensing (CS) framework and thus, achieve channel sensing cognizant of time-frequency localization across significant channel taps. Numerical evidence based on data-driven channel ground truths are presented.
Adaptively-Branching Fuzzy Greedy K-mean Decision Forest (FGK-DF) Model for Protein Local Tertiary Structure Prediction

For the past twenty years, protein tertiary structure research has been given much attention, with solutions existing for both wet lab procedures (x-ray crystallography and NMR spectroscopy) and bioinformatics approaches (threading, homology-modeling, and de novo). Unfortunately, each approach has significant shortcomings, such as necessary time, capital, expertise (for wet lab procedures) or restrictions imposed by the method, limiting the resolution or novelty of produced tertiary structures (for bioinformatics approaches). This work propose the Adaptively-Branching Fuzzy Greedy K-means-Decision Forest (FGK-DF) model, which utilizes conserved sequential and structural motifs that transcend protein family boundaries, to predict the local tertiary structure of proteins with unknown structures. In this work, the FGK-DF model is conceptually compared against existing approaches and explicitly compared against the Super Granule Support Vector Machine approach (Super GSVM), with accuracy and coverage results highlighted.
Swarm Optimization-Based Magnetometer Calibration for Personal Handheld Devices

Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a processor that generates position and orientation solutions by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the user heading based on Earth's magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are usually corrupted by several errors, including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO)-based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometers. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. Furthermore, the proposed algorithm can help in the development of Pedestrian Navigation Devices (PNDs) when combined with inertial sensors and GPS/Wi-Fi for indoor navigation and Location Based Services (LBS) applications.
International Conference on Information Systems ( ICIS ) 1-1-2010 AMODEL OF INTERNET SHOPPER BEHAVIOR , A CROSS SECTOR ANALYSIS

This research aims to enhance our knowledge of Inte rne purchase behavior by proposing a new model which draws from three disciplines: consumer b havior, decision analysis and IS. A research methodology has been designed to capture t he dynamic process that Internet shoppers follow when they are engaged in a shopping experien ce and the influences of their interactions with the environment on their behavior. Data from a n Internet panel data provider, video recording sessions and questionnaires will be colle ted to capture the purchase behavior process and establish the contextual factors and the possibilities of cross-channel behavior in the three different sectors of banking, groceries and mobile phones. The proposed model will be revised based on the results of our study. The theoretical and empirical results will inform the three literatures of this study. Companies will also be a ble to devise winning strategies by better understanding their consumers’ Internet shopping be havior.
Cdk5 on the brain.

Mammalian brains are highly compartmentalized into groups of functionally specialized neurons. Cell migration and neurite outgrowth must be tightly orchestrated to achieve this level of organization. A small serine/threonine kinase that shows homology to cyclin-dependent kinases (Cdks) has emerged as an important regulator of neuronal migration. Cdk5, unlike other Cdks, is not regulated by cyclins, and its activity is primarily detected in postmitotic neurons in developing and adult nervous systems. This review describes work indicating that Cdk5 links extracellular signaling pathways and cytoskeletal/membrane systems to direct neuronal migration, axon growth, and possibly neurosecretion. Despite its importance, unchecked Cdk5 activity is toxic to neurons, and may underlie some of the pathologies associated with neurodegenerative disorders such as Alzheimer's disease and amyotrophic lateral sclerosis.
The effect of income on religiosity

Religion is an important factor that influences decisions. Researches predicted that income plays a significant role in this process and religiosity. In this paper an empirical research on the relationship between income and religiosity among Dutch individuals is conducted. Using data from the LISS panel on around 4000 individuals in the Netherlands between 2007 and 2012, the effect of income on church attendance and praying frequency has been measured. The relationship is tested against the plausible effect of region and education and unemployment is used to discover a causal effect. Outcomes show that, against expectations from theories and other researches, income is a poor prediction of religiosity
Living Hand to Mouth: Psychological Theories about Speech and Gesture in Interactive Dialogue Systems

In this paper we discuss the application of aspects of a psychological theory about the relationship between speech and gesture to the implementation of interactive dialogue systems. We first lay out some uncontroversial facts about the interaction of speech and gesture in conversation and describe some psychological theories put forth to explain those data, settling on one theory as being the most interesting for interactive dialogue systems. We then lay out our implementation of an interactive dialogue system that is informed by the theory— concentrating on two particular claims of the theory: that gesture and speech reflect a common conceptual source; and that the content and form of gesture is tuned to the communicative context and the actor’s communicative intentions. We compare our work to some other similar interactive systems, and conclude with some thoughts about how both implementation and theory can benefit from this kind of close partnership.
Toward Energy-Efficient 5G Wireless Communications Technologies

The densification and expansion of wireless networks pose new challenges on energy efficiency. With a drastic increase of infrastructure nodes (e.g. ultra-dense deployment of small cells), the total energy consumption may easily exceed an acceptable level. While most studies focus on the energy radiated by the antennas, the bigger part of the total energy budget is actually consumed by the hardware (e.g., coolers and circuit energy consumption). The ability to shutdown infrastructure nodes (or parts of it) or to adapt the transmission strategy according to the traffic will therefore become an important design aspect of future wireless architectures. Network infrastructure should be regarded as a resource that can be occupied or released on demand. However, the modeling and optimization of such systems are complicated by the potential interference coupling between active nodes. In this article, we give an overview on different aspects of this problem. We show how prior knowledge of traffic patterns can be exploited for optimization. Then, we discuss the framework of interference functions, which has proved a useful tool for various types of coupled systems in the literature. Finally, we introduce different classes of algorithms that have the objective of improving the energy efficiency by adapting the network configuration to the traffic load.
Data flow analysis techniques for test data selection

This paper examines a family of program test data selection criteria derived from data flow analysis techniques similar to those used in compiler optimization. It is argued that currently used path selection criteria which examine only the control flow of a program are inadequate. Our procedure associates with each point in a program at which a variable is defined, those points at which the value is used. Several related path criteria, which differ in the number of these associations needed to adequately test the program, are defined and compared.
Tolerant compressed sensing with partially coherent sensing matrices

Most of compressed sensing (CS) theory to date is focused on incoherent sensing, that is, columns from the sensing matrix are highly uncorrelated. However, sensing systems with naturally occurring correlations arise in many applications, such as signal detection, motion detection and radar. Moreover, in these applications it is often not necessary to know the support of the signal exactly, but instead small errors in the support and signal are tolerable. Despite the abundance of work utilizing incoherent sensing matrices, for this type of tolerant recovery we suggest that coherence is actually beneficial . We promote the use of coherent sampling when tolerant support recovery is acceptable, and demonstrate its advantages empirically. In addition, we provide a first step towards theoretical analysis by considering a specific reconstruction method for selected signal classes.
Designing KDD-Workflows via HTN-Planning

Knowledge Discovery in Databases (KDD) has evolved a lot during the last years and reached a mature stage offering plenty of operators to solve complex data analysis tasks. However, the user support for building workflows has not progressed accordingly. The large number of operators currently available in KDD systems makes it difficult for users to successfully analyze data. In addition, the correctness of workflows is not checked before execution. This demo presents our tools, eProPlan and eIDA, which solve the above problems by supporting the whole cycle of (semi-) automatic workflow generation. Our modeling tool eProPlan, allows to describe operators and build a task/method decomposition grammar to specify the desired workflows. Additionally, our Intelligent Discovery Assistant, eIDA, allows to place workflows into data mining (DM) suites or workflow engines for execution.
USING A COGNITIVE BEHAVIORAL FRAMEWORK TO TRAIN STAFF SERVING INDIVIDUALS WHO EXPERIENCE CHRONIC HOMELESSNESS

Training in evidence-based practices may better prepare providers to address the effect of behavioral health concerns on chronic homelessness. Through the Beck Initiative, a community–academic partnership, case managers (n = 19) were selected to incorporate a cognitive-behavioral therapy (CBT) framework into services for chronic homelessness at seven sites in Philadelphia, PA. Details of the training components and development are presented. This training program demonstrated feasibility within these low-demand shelter settings (79% completed training, 10% left employment during training, 10% did not complete). Feedback measures indicated positive feedback regarding acceptability, usefulness, and quality of training. Planned discharges from the shelters were significantly more frequent in the 6 months posttraining compared with the 6 months before training. The CBT framework shows early promise for improving services for people experiencing chronic homelessness. This program also demonstrates the benefit of involving community stakeholders and academic partners to address one aspect of chronic homelessness.
A COSPAL Subsystem : Solving a Shape-Sorter Puzzle

To program a robot to solve a simple shape-sorter puzzle is trivial. To devise a Cognitive System Architecture, which allows the system to find out by itself how to go about a solution, is less than trivial. The development of such an architecture is one of the aims of the COSPAL project, leading to new techniques in vision based Artificial Cognitive Systems, which allow the development of robust systems for real dynamic environments. The systems developed under the project itself remain however in simplified scenarios, likewise the shape-sorter problem described in the present paper. The key property of the described system is its robustness. Since we apply association strategies of local features, the system behaves robustly under a wide range of distortions, as occlusion, colour and intensity changes. The segmentation step which is applied in many systems known from literature is replaced with local associations and view-based hypothesis validation. The hypotheses used in our system are based on the anticipated state of the visual percepts. This state replaces explicit modeling of shapes. The current state is chosen by a voting system and verified against the true visual percepts. The anticipated state is obtained from the association to the manipulator actions, where reinforcement learning replaces the explicit calculation of actions. These three differences to classical schemes allow the design of a much more generic and flexible system with a high level of robustness. On the technical side, the channel representation of information and associative learning in terms of the channel learning architecture are essential ingredients for the system. It is the properties of locality, smoothness, and non-negativity which make these techniques suitable for this kind of application. The paper gives brief descriptions of how different system parts have been implemented and show some examples from our tests.
The Relationship between Pain Beliefs and Physical and Mental Health Outcome Measures in Chronic Low Back Pain: Direct and Indirect Effects

Low back pain remains a major health problem with huge societal cost. Biomedical models fail to explain the disability seen in response to reported back pain and therefore patients’ beliefs, cognitions and related behaviours have become a focus for both research and practice. This study used the Pain Beliefs Questionnaire and had two aims: To examine the extent to which pain beliefs are related to disability, anxiety and depression; and to assess whether those relationships are mediated by pain self-efficacy and locus of control. In a sample of 341 chronic low back pain patients, organic and psychological pain beliefs were related to disability, anxiety and depression. However, organic pain beliefs were more strongly related to disability and depression than psychological pain beliefs. Regression analyses revealed that these relationships were in part independent of pain self-efficacy and locus of control. Further, mediation analyses revealed indirect pathways involving self-efficacy and, to a lesser extent chance locus of control, between organic pain beliefs, on the one hand, and disability, anxiety and depression, on the other. In contrast, psychological pain beliefs were only directly related to disability, anxiety and depression. Although longitudinal data are needed to corroborate our findings, this study illustrates the importance of beliefs about the nature of pain and beliefs in one’s ability to cope with pain in determining both physical and mental health outcomes in chronic low back pain patients.
Improved kernel canonical variate analysis for process monitoring

This paper proposes a kernel canonical variate analysis (KCVA) approach for process fault detection. The technique employs the kernel principle to map the original process observations to a high dimensional feature space on which canonical variate analysis is performed. The aim is to obtain an effective monitoring technique that accounts for non-linearity and process dynamics simultaneously. The kernel principle accounts for non-linearity while the CVA accounts for serial correlations widely encountered in dynamic processes. The kernel CVA algorithm proposed in this work is based on QR decomposition in order to avoid singularity problems associated with kernel matrices which require a regularisation step. The technique is evaluated using the Tennessee Eastman Challenge process. Tests show the effectiveness of the proposed kernel CVA approach.
Deformed Kernel Based Extreme Learning Machine

The extreme learning machine (ELM) is a newly emerging supervised learning method. In order to use the information provided by unlabeled samples and improve the performance of the ELM, we deformed the kernel in the ELM by modeling the marginal distribution with the graph Laplacian, which is built with both labeled and unlabeled samples. We further approximated the deformed kernel by means of random feature mapping. The experimental results showed that the proposed semi-supervised extreme learning machine tends to achieve outstanding generalization performance at a relatively faster learning speed than traditional semi-supervised learning algorithms.