Miracle-dz/UniFAR-FTUs huggingface dataset:
The biopsychosocial model in Anglo-American psychiatry: Past, present and future?

The biopsychosocial model in Anglo-American psychiatry is appraised. Its content and history are described and its scientific and ethical strengths noted. approaches in the profession, especially an older but enduring biomedical model. The tensions provoked by the latter, in relation to 'anti-psychiatry', the users' movement and 'critical psychiatry' are explored, as a context in which the biopsychosocial model has both emerged and been constrained. At the end of the paper, reasons for the relative lack of success of the model are discussed and its future prospects assessed. It is situated in relation to competing
CROWD Dynamic Network Reconfiguration in Wireless DenseNets

Traffic in wireless access networks is expected to grow substantially in the next years, both in terms of total volume and of data rate required by individual users. The commonly agreed solution to overcome the current limitations of wireless access networks is to deploy very dense and heterogeneous wireless networks, the so-called DenseNets. Simply scaling existing networks by orders of magnitude, as required to fulfil traffic forecasts, would bring along several problems because of the limited backhaul capacity, the increased energy consumption, and the explosion of signalling. Hence, the FP7 project CROWD proposes a novel architecture for DenseNets, based on SoftwareDefined Networking (SDN), as a solution to tame the density of wireless networks. Within this architecture, dynamic controller placement/reassignment supported by dynamic backhaul reconfiguration is the crucial component to both maximize the amount of users that can be served simultaneously in high load situations and to minimize the energy consumption to reduce costs for the service providers. We present both approaches and outline how they are integrated into the overall CROWD architecture.
High Throughput Interweave Cooperative Wireless MAC Protocol for Congested Environment

CSMA/CA wireless MAC protocol is adopted in many standards. However, its performance at high contention level is less than desirable due to prolonged backoff period and collisions. We propose a novel cooperative MAC based on the Interweave Cognitive framework to combat high contention. In our setup, we incorporate a subset of self-organizing advanced nodes that opportunistically switch to a contention-free MAC protocol to ease network congestion in a cognitive and cooperative manner. Consequently, the whole network benefits from a higher and more stable throughput even at high contention level. Our theoretical and simulation results demonstrate that IC-MAC is effective in reducing the number of backlogged nodes and improving the throughput of up to 20% relative to CSMA/CA MAC.
Use behaviors and website experiences of facebook community

With the rapid growth of internet-generation, online friend-making pattern has gradually grown up, wherein, Facebook is one of the global social networking community that develops fastest in recent years. Therefore, this research attempts to investigate online friend-making pattern of Facebook users and compare the relevancy between the use behavior and website experience of Facebook users on the theoretical basis of “experiential marketing” and in view of the experience modules such as “sense experience”, “feel experience”, “think experience”, “act experience” and “relate experience. ” The research findings indicate that in the experiential marketing module, some factors among the Facebook users' demographic variables and use behaviors of Facebook really have significant differences in website experience. This research provides operation recommendations related with on-line community platforms, and hopefully provides reference about making future website strategies or planning marketing activities for website operators.
Abstract Concept Learning Approach Based on Behavioural Feature Extraction

In this paper, we propose a novel approach in which an intelligent agent can learn complex concepts in abstract forms. This approach provides a useful tool for non-episodic problems, where agent must search the environment to find special concepts; in addition, yielded abstract representation of the concepts can be used in further high level planning tasks. In order to perform concept learning process in this framework, agent utilizes its own actions according to limitations of sensory data and complexity of related analysis. It extracts required features from environment according to complexity of concepts and their distinctions. These features are composed of sequences of agent’s primitive actions. The proposed method is tested on a mobile robot benchmark, and learned concepts are used for a path planning problem. The simulation results demonstrate the capability of our approach in abstracting concepts.
Visual Global . Context : Word Image Matching in a Methodology for Degraded Text Recognition

A technique for the use of global context in text recognition is presented that determines equivalences between word images in a passage of text. Initial hypotheses for the identities of words are then generated by matching the word groups to language statistics that predict the frequency at which certain words will occur. This is followed by a recognition step and a relaxation-based control structure that resolves inconsistencies between several knowledge sources. This paper concentrates on the equivalence determination algorithm. A word matching technique is presented and its perfonnance . on a running text of about 1000 " word images is determined. Several levels of noise are introduced to simulate different J amounts of degradation introduced by fax machines or photocopiers. It is shown that the word matching algorithm maintains its ability to locate small groups of equivalent word images with high reliability even in the presence of noise.
Discovery of Influenza A Virus Sequence Pairs and Their Combinations for Simultaneous Heterosubtypic Targeting that Hedge against Antiviral Resistance

The multiple circulating human influenza A virus subtypes coupled with the perpetual genomic mutations and segment reassortment events challenge the development of effective therapeutics. The capacity to drug most RNAs motivates the investigation on viral RNA targets. 123,060 segment sequences from 35,938 strains of the most prevalent subtypes also infecting humans–H1N1, 2009 pandemic H1N1, H3N2, H5N1 and H7N9, were used to identify 1,183 conserved RNA target sequences (≥15-mer) in the internal segments. 100% theoretical coverage in simultaneous heterosubtypic targeting is achieved by pairing specific sequences from the same segment (“Duals”) or from two segments (“Doubles”); 1,662 Duals and 28,463 Doubles identified. By combining specific Duals and/or Doubles to form a target graph wherein an edge connecting two vertices (target sequences) represents a Dual or Double, it is possible to hedge against antiviral resistance besides maintaining 100% heterosubtypic coverage. To evaluate the hedging potential, we define the hedge-factor as the minimum number of resistant target sequences that will render the graph to become resistant i.e. eliminate all the edges therein; a target sequence or a graph is considered resistant when it cannot achieve 100% heterosubtypic coverage. In an n-vertices graph (n ≥ 3), the hedge-factor is maximal (= n– 1) when it is a complete graph i.e. every distinct pair in a graph is either a Dual or Double. Computational analyses uncover an extensive number of complete graphs of different sizes. Monte Carlo simulations show that the mutation counts and time elapsed for a target graph to become resistant increase with the hedge-factor. Incidentally, target sequences which were reported to reduce virus titre in experiments are included in our target graphs. The identity of target sequence pairs for heterosubtypic targeting and their combinations for hedging antiviral resistance are useful toolkits to construct target graphs for different therapeutic objectives.
Equivalence of Zhang – Zhang Polynomial and Cube Polynomial for Spherical Benzenoid Systems

Benzenoid systems or hexagonal systems are subgraphs of a hexagonal lattice. Open-ended carbon nanotubes alias tubulenes can be seen as an embedding of a benzenoid system to a surface of a cylinder with some perimeter edges being joined. Carbon nanotubes are interesting materials with some unusual properties and have therefore been of great interest for researchers in the last 20 years. We use a new term spherical benzenoid system for either a benzenoid system or a tubulene, since both of them can be embedded on a sphere. Zhang–Zhang polynomial (also called Clar covering polynomial) of a spherical benzenoid system is a counting polynomial of resonant structures called Clar covers. Cube polynomial is a counting polynomial of induced hypercubes in a graph. In [39] authors established the one-to-one correspondence between Clar covers of a benzenoid system G and hypercubes of its resonance graph R(G). In this paper the equality of two polynomials is extended to spherical benzenoid systems using a different method of proving in one part.
Universality of Derrida Coarsening in the Triangular Potts Model

The temperature dependence of the dynamics of the fraction F(t) of persistent spins in the triangular Q-state Potts model is investigated by large scale Monte Carlo simulations. After extending Derrida's approach of measuring the fraction of spins that remain in one phase to all Q low-temperature phases, it is shown that the exponent θ of algebraic decay of F(t) is independent of temperature.
Service Recommendation for Mashup Creation Based on Time-Aware Collaborative Domain Regression

Mash up has emerged as a promising way to compose web APIs and create value-added compositions. The increasing of APIs demands more accurate recommendation algorithms. However, service domain evolution, mash up-side cold-start and information evaporation are somehow overlooked by existing work. In this paper, by extending the collaborative topic regression (CTR) model, the procedure of service selection is modeled with a generative process, and the mash up-side cold-start problem that cannot be dealt with by naïve CTR is resolved. By learning the maximum a posteriori estimates of the whole generative process, both content information and historical usage are taken into consideration to extract service domains, thus the service domains can evolve with the evaluation of historical usage pattern. Meanwhile, information evaporation is also considered by giving time-related confidence levels to historical usage to track the evolution of service ecosystem. Experiments on the real-world Programmable Web data set show that compared with the state-of-the-art methods, our approach gains a 6.8% improvement in terms of recommendation accuracy.
Boreal forest leaf area index from optical satellite images: model simulations and empirical analyses using data from central Finland

A key variable controlling the global carbon, water and energy cycles is the leaf area index (LAI), which thus has an important role in current biosphere-atmosphere research. Several international networks promote the development of remote sensing techniques for global assessment of LAI. We present results from the VALERI (VAlidation of Land European Remote sensing Instruments) network's test site at Hirsikangas in central Finland which represents one of the very few boreal forests in the global networks. In this study, three years of LAI data from Hirsikangas together with medium-resolution satellite images (SPOT HRVIR, Landsat 5 TM and IRS) over the site were used (1) to study the relationships between LAI and different spectral vegetation indices (SVIs), and (2) to test the performance of a physically-based forest reflectance model (PARAS). Simulations with PARAS showed good agreement with empirical data. Results indicated that inclusion of the SWIR band improves the performance of SVIs used for LAI estimation in the boreal forests.
Housing Collateral and Entrepreneurship

This paper shows that collateral constraints restrict firm entry and post-entry growth, even in the long-run. We use French administrative data and exploit cross-sectional variation in local house-price appreciation as shocks to the value of collateral available to homeowners. We control for local demand shocks by comparing homeowners to two control groups that live in the same region but do not experience collateral shocks: (i) renters and (ii) homeowners with a mortgage outstanding, who - in France - cannot take out a second mortgage on their house. In both comparisons, we find that an increase in collateral value leads to a higher probability of becoming an entrepreneur. Conditional on entry, entrepreneurs with access to more valuable collateral use more debt, start larger firms, and remain significantly larger even six years after creation.
Measuring the Benefits of Summarizing Quantitative Information in Pervasive Computing Systems

Ubiquitous computing systems continuously sense the environment and collect different kind of data used to provide un-obtrusive, proactive services to users. The amount of data collected grows tremendously consuming considerable storage space, slowing down the query processing and data analysis tasks and negatively effects the distributed data management and data transfer among various nodes. We have proposed earlier that a compact, summarized and aggregate representation of historical data can be used to overcome these problems. In this paper, we analyze the effectiveness of this concept by summarizing the quantitative information using various techniques and calculating different parameters as storage space consumption, time taken to answer queries and the precision of the results provided
A Sparse Plus Low-Rank Exponential Language Model for Limited Resource Scenarios

This paper describes a new exponential language model that decomposes the model parameters into one or more low-rank matrices that learn regularities in the training data and one or more sparse matrices that learn exceptions (e.g., keywords). The low-rank matrices induce continuous-space representations of words and histories. The sparse matrices learn multi-word lexical items and topic/domain idiosyncrasies. This model generalizes the standard ℓ1-regularized exponential language model, and has an efficient accelerated first-order training algorithm. Language modeling experiments show that the approach is useful in scenarios with limited training data, including low resource languages and domain adaptation.
Wide-frequency-range low-power variable-length ring oscillator in UMC CMOS 0.18 µm

The paper describes structure and simulation results of the novel ring oscillator designed in UMC CMOS 0.18 μm (1.8 V). Frequency generated by the oscillator is tuned by scaling the supply voltage, additionally ring length is digitally controlled. Presented ring oscillator has very wide tuning range (250 MHz-2.1 GHz) with small current consumption (34-689 μA).
Relevance of Morning and Evening Energy and Macronutrient Intake during Childhood for Body Composition in Early Adolescence

(1) Background: This study investigated the relevance of morning and evening energy and macronutrient intake during childhood for body composition in early adolescence; (2) Methods: Analyses were based on data from 372 DONALD (DOrtmund Nutritional and Anthropometric Longitudinally Designed study) participants. Explorative life-course plots were performed to examine whether morning or evening energy and macronutrient intake at 3/4 years, 5/6 years, or 7/8 years is critical for fat mass index (FMI [kg/m2]) and fat free mass index (FFMI [kg/m2]) in early adolescence (10/11 years). Subsequently, exposures in periods identified as consistently critical were examined in depth using adjusted regression models; (3) Results: Life-course plots identified morning fat and carbohydrate (CHO) intake at 3/4 years and 7/8 years as well as changes in these intakes between 3/4 years and 7/8 years as potentially critical for FMI at 10/11 years. Adjusted regression models corroborated higher FMI values at 10/11 years among those who had consumed less fat (p = 0.01) and more CHO (p = 0.01) in the morning at 7/8 years as well as among those who had decreased their morning fat intake (p = 0.02) and increased their morning CHO intake (p = 0.05) between 3/4 years and 7/8 years; (4) Conclusion: During childhood, adherence to a low fat, high CHO intake in the morning may have unfavorable consequences for FMI in early adolescence.
Capn5 Is Expressed in a Subset of T Cells and Is Dispensable for Development

ABSTRACT The Capn5 gene was inactivated by homologous recombination in ES cells that subsequently colonized the germ line of mice. The targeted mutation integrated a lacZ expression cassette into the Capn5 gene, allowing the expression of Capn5 mRNA to be examined in detail in heterozygous animals. Expression was observed in embryonic and newborn thymuses, in various epithelial tissues, and in tissues of the central nervous system. In the thymus, Capn5 was expressed mainly in relatively immature CD25+ embryonic thymocytes. Despite the numerous expression sites of Capn5, the majority of Capn5-null mice were viable and fertile and appeared healthy. Histopathological analysis did not reveal any differences between Capn5-null and wild-type mice. There were no defects in the major T- or B-cell populations in the thymus, spleen, bone marrow, or peritoneum, nor did apoptosis appear abnormal in Capn5-null T cells. There was no evidence for the development of autoimmune disease in Capn5-null animals. However, a small proportion of homozygous null offspring from heterozygous matings were runted and most often did not survive to adulthood.
Imaging dose in breast radiotherapy: does breast size affect the dose to the organs at risk and the risk of secondary cancer to the contralateral breast?

Correct target positioning is crucial for accurate dose delivery in breast radiotherapy resulting in utilisation of daily imaging. However, the radiation dose from daily imaging is associated with increased probability of secondary induced cancer. The aim of this study was to quantify doses associated with three imaging modalities and investigate the correlation of dose and varying breast size in breast radiotherapy.
Adaptive combinations of classifiers with application to on-line handwritten character recognition

Classifier combining is an effective way of improving classification performance. User adaptation is clearly another valid approach for improving performance in a user-dependent system, and even though adaptation is usually performed on the classifier level, also adaptive committees can be very effective. Adaptive committees have the distinct ability of performing adaptation without detailed knowledge of the classifiers. Adaptation can therefore be used even with classification systems that intrinsically are not suited for adaptation, whether that be due to lack of access to the workings of the classifier or simply a classification scheme not suitable for continuous learning. This thesis proposes methods for adaptive combination of classifiers in the setting of on-line handwritten character recognition. The focal part of the work introduces adaptive classifier combination schemes, of which the two most prominent ones are the Dynamically Expanding Context (DEC) committee and the ClassConfidence Critic Combining (CCCC) committee. Both have been shown to be capable of successful adaptation to the user in the task of on-line handwritten character recognition. Particularly the highly modular CCCC framework has shown impressive performance also in a doubly-adaptive setting of combining adaptive classifiers by using an adaptive committee. In support of this main topic of the thesis, some discussion on a methodology for deducing correct character labeling from user actions is presented. Proper labeling is paramount for effective adaptation, and deducing the labels from the user’s actions is necessary to perform adaptation transparently to the user. In that way, the user does not need to give explicit feedback on the correctness of the recognition results. Also, an overview is presented of adaptive classification methods for singleclassifier adaptation in handwritten character recognition developed at the Laboratory of Computer and Information Science of the Helsinki University of Technology, CIS-HCR. Classifiers based on the CIS-HCR system have been used in
Computing Boundaries for Reasoning in Sub-Ontologies

Consider an ontology T where every axiom is labeled with an element of a lattice (L, ≤). Then every element l of L determines a sub-ontology Tl, which consists of the axioms of T whose labels are greater or equal to l. These labels may be interpreted as required access rights, in which case Tl is the sub-ontology that a user with access right l is allowed to see, or as trust levels, in which case Tl consists of those axioms that we trust with level at least l. Given a consequence α (such as a subsumption relationship between concepts) that follows from the whole ontology T, we want to know from which of the sub-ontologies Tl determined by lattice elements l the consequence α still follows. However, instead of reasoning with Tl in the deployment phase of the ontology, we want to pre-compute this information during the development phase. More precisely, we want to compute what we call a boundary for α, i.e., an element μα of L such that α follows from T l iff l ≤ μα. In this paper we show that, under certain restrictions on the elements l used to define the sub-ontologies, such a boundary always exists, and we describe black-box approaches for computing it that are generalizations of approaches for axiom pinpointing in description logics. We also present first experimental results that compare the efficiency of these approaches on real-world ontologies.
FUZZY LOGIC COURSE PROJECT FUZZY ROC CURVES FOR THE ONE-CLASS SVM : APPLICATION TO INTRUSION DETECTION

A novel method for receiver operating characteristic (ROC) curve analysis and anomaly detection is proposed. The ROC curve provides a measure of effectiveness for binary classification problems, and this paper specifically addresses unbalanced, unsupervised, binary classification problems. Furthermore, this work explores techniques in fusing decision values from classifiers and using ROC curves to illustrate the effectiveness of the fusion techniques. In describing an unbalanced classification problem, I am addressing a problem that has a low occurrence of the positive class (generally less than 10%). Since the problem is unsupervised, the one-class SVM is utilized. I discuss the curse of dimensionality experienced with the oneclass SVM, and to overcome this problem I create subspaces of our variables. For each subspace created, the one class SVM produces a decision value. The aggregation of the decision values occurs through the use of fuzzy logic, creating the fuzzy ROC curve. The primary source of data for this research is a host based computer intrusion detection dataset. Experimental results supported with theoretical discussion of ROC curves and fuzzy logic indicates that synergistic ROC curves emerge when subspaces are orthogonal and T-conorms are utilized for fusion.
Size and book to market effects: further evidence from the French case

The objective of this paper is to study the market, SMB, HML and The leverage factors inexplaining cross-sectional returns.We provide the first empirical analysis of Ferguson and Shockley (2003) theoretical frame work on the French stock market. Book to market and size, variables which a recorrelated with leverage, will appear to explain returns.Our main result is that the leverage factor doesn’t subsume the SMB and HML factors. Incross-sectional regressions, only the size premium is statistically significant and help explaining returns.In time- series regressions, the three factors (SMB, HML andleverage),with the market portfolio,do a good job. This result suggests that the leverage portfolio has an additional improvement of the model.