Miracle-dz/UniFAR-FTUs huggingface dataset:
Search result presentation: supporting post-search navigation by integration of taxonomy data

As a result of additional semantic annotations and novel mining methods, Web site taxonomies are more and more available to machines, including search engines. Recent research shows that after a search result is clicked, users often continue navigating on the destination site because in many cases a single document cannot satisfy the information need. The role Web site taxonomies play in this post-search navigation phase has not yet been researched. In this paper we analyze in an empirical study of three highly-frequented Web sites how Web site taxonomies influence the next browsing steps of users arriving from a search engine. The study reveals that users not randomly explore the destination site, but proceed to the direct child nodes of the landing page with significantly higher frequency compared to the other linked pages. We conclude that the common post-search navigation strategy in taxonomies is to descend towards more specific results. The study has interesting implications for the presentation of search results. Current search engines focus on summarizing the linked document only. In doing so, search engines ignore the fact the linked documents are in many cases just the starting point for further navigation. Based on the observed post-search navigation strategy, we propose to include information about child nodes of linked documents in the presentation of search results. Users would benefit by saving clicks, because they could not only estimate whether the linked document provides useful information, but also whether post-search navigation is promising.
Strategies for regeneration of components of nervous system: scaffolds, cells and biomolecules

Nerve diseases including acute injury such as peripheral nerve injury (PNI), spinal cord injury (SCI) and traumatic brain injury (TBI), and chronic disease like neurodegeneration disease can cause various function disorders of nervous system, such as those relating to memory and voluntary movement. These nerve diseases produce great burden for individual families and the society, for which a lot of efforts have been made. Axonal pathways represent a unidirectional and aligned architecture allowing systematic axonal development within the tissue. Following a traumatic injury, the intricate architecture suffers disruption leading to inhibition of growth and loss of guidance. Due to limited capacity of the body to regenerate axonal pathways, it is desirable to have biomimetic approach that has the capacity to graft a bridge across the lesion while providing optimal mechanical and biochemical cues for tissue regeneration. And for central nervous system injury, one more extra precondition is compulsory: creating a less inhibitory surrounding for axonal growth. Electrospinning is a cost-effective and straightforward technique to fabricate extracellular matrix (ECM)-like nanofibrous structures, with various fibrous forms such as random fibers, aligned fibers, 3D fibrous scaffold and core-shell fibers from a variety of polymers. The diversity and versatility of electrospinning technique, together with functionalizing cues such as neurotrophins, ECM-based proteins and conductive polymers, have gained considerable success for the nerve tissue applications. We are convinced that in the future the stem cell therapy with the support of functionalized electrospun nerve scaffolds could be a promising therapy to cure nerve diseases.
Evolutionary Learning in the 2D Artificial Life System "Avida"

We present a new tierra-inspired artificial life system with local interactions and two-dimensional geometry, based on an update mechanism akin to that of 2D cellular automata. We find that the spatial geometry is conducive to the development of diversity and thus improves adaptive capabilities. We also demonstrate the adaptive strength of the system by breeding cells with simple computational abilities, and study the dependence of this adaptability on mutation rate and population size.
A view-dependent adaptivity metric for real time mesh tessellation

Real-time tessellation methods offer the ability to upsample 3D surface meshes on the fly during rendering. This upsampling relies on 3 major steps. First, it requires a tessellation kernel which can be implemented on GPU or may be already available as a hardware unit. Second, the surface model defines the positions of the newly inserted vertices - we focus on recent visually smooth models. And third the adaptive sampling tailors the spatially varying distribution of newly inserted vertices on the input surface. We study the last component and introduce a new view-dependent adaptivity metric which builds upon both intrinsic and extrinsic criteria of the input mesh. As a result, we obtain a better vertex distribution around important features in the tessellated mesh.
Transfusion-related acute lung injury: definition and review.

BACKGROUND Transfusion-related acute lung injury (TRALI) is now the leading cause of transfusion-associated mortality, even though it is probably still underdiagnosed and underreported. NATIONAL HEART, LUNG, AND BLOOD INSTITUTE ACTION The National Heart, Lung, and Blood Institute convened a working group to identify areas of research needed in TRALI. The working group identified the immediate need for a common definition and thus developed the clinical definition in this report. MAJOR CONCEPTS IN THE DEFINITION The major concept is that TRALI is defined as new acute lung injury occurring during or within 6 hrs after a transfusion, with a clear temporal relationship to the transfusion. Also, another important concept is that acute lung injury temporally associated with multiple transfusions can be TRALI, because each unit of blood or blood component can carry one or more of the possible causative agents: antileukocyte antibody, biologically active substances, and other yet unidentified agents. RECOMMENDATION Using the definition in this report, clinicians can diagnose and report TRALI cases to the blood bank; importantly, researchers can use this definition to determine incidence, pathophysiology, and strategies to prevent this leading cause of transfusion-associated mortality.
On Cognitive Interference Networks

We study the high-power asymptotic behavior of the sum-rate capacity of multi-user interference networks with an equal number of transmitters and receivers. We assume that each transmitter is cognizant of the message it wishes to convey to its corresponding receiver and also of the messages that a subset of the other transmitters wish to send. The receivers are assumed not to be able to cooperate in any way so that they must base their decision on the signal they receive only. We focus on the network's pre-log, which is denned as the limiting ratio of the sum-rate capacity to half the logarithm of the transmitted power. We present both upper and lower bounds on the network's pre-log. The lower bounds are based on a linear partial-cancellation scheme which entails linearly transforming Gaussian codebooks so as to eliminate the interference in a subset of the receivers. Inter alias, the bounds give a complete characterization of the networks and side-information settings that result in a full pre-log, i.e., in a pre-log that is equal to the number of transmitters (and receivers) as well as a complete characterization of networks whose pre-log is equal to the full pre-log minus one. They also fully characterize networks where the full pre-log can only be achieved if each transmitter knows the messages of all users, i.e., when the side-information is "full".
Improving model accuracy using optimal linear combinations of trained neural networks

Neural network (NN) based modeling often requires trying multiple networks with different architectures and training parameters in order to achieve an acceptable model accuracy. Typically, only one of the trained networks is selected as "best" and the rest are discarded. The authors propose using optimal linear combinations (OLC's) of the corresponding outputs on a set of NN's as an alternative to using a single network. Modeling accuracy is measured by mean squared error (MSE) with respect to the distribution of random inputs. Optimality is defined by minimizing the MSE, with the resultant combination referred to as MSE-OLC. The authors formulate the MSE-OLC problem for trained NN's and derive two closed-form expressions for the optimal combination-weights. An example that illustrates significant improvement in model accuracy as a result of using MSE-OLC's of the trained networks is included.
Genetic Variation in ABCA1 Predicts Ischemic Heart Disease in the General Population

Objective—We tested the hypothesis that 6 nonsynonymous single nucleotide polymorphisms (SNPs) in ATP-Binding-Cassette transporter A1 (ABCA1) affect risk of ischemic heart disease (IHD) in the general population. Methods and Results—We genotyped 9259 individuals from the Danish general population followed for 25 years. Two SNPs (V771M and V825I) were previously associated with increases in HDL-C, 1 (R1587K) with decreased HDL-C, whereas 3 (R219K, I883M and E1172D) did not affect HDL-C levels. Despite this, 5 out of 6 SNPs (V771M, V825I, I883M, E1172D, R1587K) predicted increased risk of IHD. Similar results were obtained in a verification sample with 932 IHD cases versus 7999 controls. A stepwise regression approach identified V771M, I883M, and E1172D as the most important predictors of IHD and additive effects on IHD risk were present for V771M/I883M and I883M/E1172D pairs. Conclusions—We show that 3 of 6 nonsynonymous SNPs in ABCA1 predict risk of IHD in the general population.
The influence of biofeedback on respiratory training effect

Clinical studies have indicated that some diseases such as insomnia, anxiety, and minor melancholia all have close correlation with autonomic nervous system. The respiratory training can regulate the function of human autonomic nervous system and consequently release stress. We design a respiratory training biofeedback system which trains the subjects to perform pursed lips abdominal breathing and provide audio and visual biofeedback to them. The audio biofeedback is a pure tone and the visual biofeedback is a graphic blue bar. The frequency of pure tone and height of graphic bar are all regulated by the ratio of low frequency and high frequency components of heart rate variability (LF/HF ratio) which is considered as one indicator of autonomic nervous system activity. This paper assesses the influence of biofeedback on the respiratory training effect by comparing the variation of LF/HF ratio during respiratory training using or not using biofeedback. The results indicate that biofeedback has significant influence on the respiratory training effect but the influence shows individual variability. The visual biofeedback seems to show more significant influence than audio biofeedback.
Energy Aware Data Aggregation Technique in WSN

Wireless sensor networks consist of sensor nodes with sensing, communicating, computing & storage with battery capacity. Data aggregation is a process or scheme to eliminate redundant transmission & provide fused information to base station which improves energy efficiency & network . lifetime of energy constrained WSN.In this paper, we present a survey of data aggregation schemes in Flat & Hierarchical wireless sensor networks & compare them on the basis of metrices such as lifetime, latency and data accuracy. Our main focus on data aggregation in cluster based network using LEACH protocol which delivers 10 times more data than the minimum energy transmission routing which improves system lifetime & reliability of data transmission & energy consumption by a factor of 8 compared to direct transmission. This paper highlights some of the drawbacks and issues in LEACH & the proposed protocol LEACH Acess Point(LEACH-AP).
Survey of Techniques for Opinion Mining

Opinion mining refers to computational techniques for analyzing the opinions that are extracted from various sources. Existing research work on Opinion is based upon business and e-commerce such as product reviews and movie ratings. Opinion mining involves computational treatment of opinion and subjectivity in text. It has suddenly attracted the attention of the researcher fraternity. This survey paper describes techniques and approaches that promise to directly enable opinion-oriented information seeking systems. An attempt has been made to discuss in de tails various approaches to perform a computational treatment of sentiments and opinions. Various supervised or data-driven techniques for opinion mining like Naive Byes, Maximum Entropy, SVM are discussed and their strengths and drawbacks are touched upon.
LES FACTEURS INFLUENCANT L’UTILISATION DE LA VEILLE STRATEGIQUE PAR LES HAUTS MANAGERS : CHOIX DES ECHELLES DE MESURE

Dans l’objectif d’expliquer les facteurs influencant l’utilisation de la veille strategique par les hauts managers, ce papier cherche a passer au crible l’ensemble des instruments de mesure ayant ete utilises, lors des travaux anterieurs, pour operationnaliser les variables et les dimensions formant notre construit theorique. Appuye sur le paradigme de Chuchill (1979) qui propose une procedure systematique, ce choix des items aboutira a l’administration d’un questionnaire qui vise le test de notre modele conceptuel dans le contexte des entreprises industrielles marocaines.
Toward a Critique of the Ontological Foundations of Earth System Science

In contemporary research on global environmental change, a terminology based on systems theory is widespread. One of the main instances where the notion of systems has been advanced is the Earth System Science Partnership and it's theoretical foundation in Earth System Science (ESS). The ESS approach conceives of the Earth as one single systemic object comprised at the most general level of two components: the ecosphere and the anthroposphere. Together, the ecosphere and the anthroposphere, nested as they are, constitute the socio-ecological system of the Earth and the aim of ESS is to study this system, effectively encompassing the total sum of everything existing on our planet, as a whole, as a fully integrated single entity. Underlying this holistic scientific approach is a mode of thought that equates the social with the natural, that sees fundamental similarities rather than differences between the essential characteristics of society and nature. Departing from such an ontology, the ESS community purports to develop integrated ways to act upon the socio-ecological systems of the Earth as a whole in a way that optimizes the whole of humanity's chances of a sustainable way of life. Hence, considering the important role of ESS in the study of contemporary processes of global change and the emerging field of sustainability, the ontology inherent in the approach play an important role in the quest for a sustainable society. However, from a social-theoretical perspective there are profound reasons to question the notion of society and nature as parts of an integrated whole and the undifferentiated ontology inherent in the holistic approach of ESS. This becomes evident when one attempts at providing an elaborate definition of the concept of society, a definition which is missing in the ESS approach. In this paper, I depart from sociological systems theory, especially as it has been developed by Niklas Luhmann, in order to arrive at such a definition of society, and I show that the only essential characteristic of society is meaningful symbolic communication between subjects. Since such communication only can occur between human subjects, there is in extension a fundamental and irreducible difference between society and its environment. Meaningful symbolic communication functions in a way that has no equivalence in nature, and most importantly it has no immediate systemic connection to nature. In other words, society can only communicate about nature, never with nature. Therefore, it is reasonable to treat society and nature, or …
AND METHODOLOGY The theory of symbolic interactionism takes its origin in the works of social psychologist

How do consumers evaluate the prestige of a person or an object? This paper proposes an empirical exploration of this question from a symbolic interactionist point of view. On the basis of in-depth interviews with consumers, a conceptual model is proposed. The findings highlight the key role of (1) the perception of unique human achievement; (2) the interpretation of socially shared symbols and (3) reference group influence in the formation of subjective prestige judgments. The informant's expertise, self-monitoring style and socio-cultural factors moderate the relationships between these constructs. The results point to similar mechanisms for the perceived prestige of people and inanimate objects. The paper ends with a discussion of the limits of the study and future research directions.
Therapeutic Priority of the PI3K/AKT/mTOR Pathway in Small Cell Lung Cancers as Revealed by a Comprehensive Genomic Analysis

Introduction: The information regarding therapeutically relevant genomic alterations in small cell lung cancer (SCLC) is not well developed. We analyzed the SCLC genome using an integrative approach to stratify the targetable alterations. Methods: We performed whole exon sequencing (n = 51) and copy number analysis (n =47) on surgically resected tumors and matched normal tissue samples from treatment-naive Japanese SCLC patients. Results: The demographics of the 51 patients included in this study were as follows: median age, 67 years (range, 42–86 years); female, 9 (18%); history of smoking, 50 (98%); and pathological stage I/II/III/IV, 28/13/9/1, respectively. The average number of nonsynonymous mutations was 209 (range, 41–639; standard deviation, 130). We repeatedly confirmed the high prevalence of inactivating mutations in TP53 and RB1, and the amplification of MYC family members. In addition, genetic alterations in the PI3K/AKT/mTOR pathway were detected in 36% of the tumors: PIK3CA, 6%; PTEN, 4%; AKT2, 9%; AKT3, 4%; RICTOR, 9%; and mTOR, 4%. Furthermore, the individual changes in this pathway were mutually exclusive. Importantly, the SCLC cells harboring active PIK3CA mutations were potentially targetable with currently available PI3K inhibitors. Conclusions: The PI3K/AKT/mTOR pathway is distinguishable in SCLC genomic alterations. Therefore, a sequencing-based comprehensive analysis could stratify SCLC patients by potential therapeutic targets.
Bilingual analysis of song lyrics and audio words

Thanks to the development of music audio analysis, state-of-the-art techniques can now detect musical attributes such as timbre, rhythm, and pitch with certain level of reliability and effectiveness. An emerging body of research has begun to model the high-level perceptual properties of music listening, including the mood and the preferable listening context of a music piece. Towards this goal, we propose a novel text-like feature representation that encodes the rich and time-varying information of music using a composite of features extracted from the song lyrics and audio signals. In particular, we investigate dictionary learning algorithms to optimize the generation of local feature descriptors and also probabilistic topic models to group semantically relevant text and audio words. This text-like representation leads to significant improvement in automatic mood classification over conventional audio features.
The Snow Dynamical System for Plant Pattern Formation

Plant organs develop at the edge of the growing tip of the plant, called shoot apical meristem. They appear as microscopic bulges of cells called primordia and later grow and differentiate into the different organs (leaves, petals, branches etc.). The regular spacial structure often visible in the grown plant (e.g. pineapple, pinecone, sunflower.) is present at the onset of primordia, at a microscopic scale. In [10], the two botanists Snow & Snow posited that primordia form around the apex meristem when and where there is enough space left available by the previously formed primordia. This contrasts with Hofmeister’s hypothesis (that we studied in [1]) where primordia are constrained to appear at fixed intervals of time. Other authors have studied models based on the Snow hypothesis ([6], [5], [8]). We choose to implement the principle of the Snows with a simple model which enables us to extract important geometric structures underlying phyllotaxis. The assumptions we make are of a cylindrical meristem border and of circular primordia that only ”feel” their nearest neighbors. One of the main feature of this model is that it produces, apart from the usual Fibonacci lattices, periodic configurations: patterns that look like lattices, each with two sets of parastichies, but are not lattices. Note that we had also found periodic patterns in the Hofmeister model [1], but they were isolated. Here periodic configurations come in large families, which are part of a large geometric structure that we call the skeleton. The skeleton also contains the usual lattices of phyllotaxis. The skeleton might provide an explanation of the mysterious speed at which plants stabilize in regular patterns with high parastichy numbers: less precision (and thus less iteration) is needed if the target set of patterns, previously thought to only consist of lattices, is enlarged to that of periodic configurations with same parastichy numbers. Our model is highly idealized, but we conjecture that similar skeletons exist in many other systems (as invariant manifolds) and that the topology of these skeletons organizes the transitions between the different phyllotactic patterns. A crucial concept in this paper, intimately tied to that of the skeleton is that of “growth front”: roughly, the most recent layer of primordia at a given stage of the growth. A growth front determines the future of a pattern that contains it. Thus deforming growth fronts provides a parameterization for the skeleton and a handle into the connections between its different “limbs”. We show that when defined, parastichy numbers can be read off the growth fronts. Growth fronts also provide a geometric key to transitions between phyllotactic patterns. As a bonus, growth fronts may provide
2 1 N ov 2 00 5 On 3-manifolds

It is well known that a three dimensional (closed, connected and compact) manifold is obtained by identifying boundary faces from a polyhedron P . The study of (∂P )/ ∼, the boundary ∂P with the polygonal faces identified in pairs leads us to the following conclusion: either a three dimensional manifold is homeomorphic to a sphere or to a polyhedron P with its boundary faces identified in pairs so that (∂P )/ ∼ is a finite number of internally flat complexes attached to each other along the edges of a finite graph that contains at least one closed circuit. Each of those internally flat complexes is obtained from a polygon where each side may be identified with more than one different sides. Moreover, Euler characteristic of (∂P )/ ∼ is equal to one and the fundamental group of (∂P )/ ∼ is not trivial.
An IoT system to estimate personal thermal comfort

Thermal comfort in office buildings is emerging as an important variable that can be used to maximize employee productivity. In this paper we propose a new Internet of Things (IoT) based system that creates a personalized model of thermal comfort. To create this model, our system collects telemetry via an IoT network of sensors and user inputs. This data is then input into machine learning algorithms that continuously calibrate and update a personalized thermal comfort model for the user. To facilitate the individuality of our models, the system combines personal measurements from the Microsoft Band, such as biometric readings and user feedback, with environmental measurements such as temperature, humidity, and air speed. In this work, we evaluate a broad set of classification and regression algorithms. Our experimental results show that using our IoT based system improves the mean squared error of the thermal prediction by about 50% when compared to the industry standard method developed by P.O. Fanger.