Miracle-dz/UniFAR-FTUs huggingface dataset:
Topic Models for Corpus-centric Knowledge Generalization

Many of the previous efforts in generalizing over knowledge extracted from text have relied on the use of manually created word sense hierarchies, such as WordNet. We present initial results on generalizing over textually derived knowledge, through the use of the LDA topic model framework, as the first step towards automatically building corpus specific ontologies. This work was funded by a 2008 Provost’s Multidisciplinary Award from the University of Rochester, and NSF grants IIS-0328849 and IIS-0535105.
User-centric resource management strategies for IPTV services

With the advent of higher bandwidth internet services, video over IP is becoming more common with the video transfer mechanisms currently being standardized by the Open IPTV Forum. IPTV in the home is starting to dominate the market and is set to replace the way we watch our media content from watching it when broadcast to watching it on demand. Existing home gateway solutions or access points can allocate bandwidth to users depending on traffic priority or on a first come first served basis. This however might not be an optimal choice of allocation since it does not take into account user privileges and user perceived quality. This paper proposes a Cognitive Resource Manager, which takes into account these factors for managing the resources in the home. The paper suggests methods for learning user preferences by employing simple machine learning techniques and defines a user satisfaction utility, then optimizes it, choosing the optimal configuration for resource allocation. The paper also proposes a more lightweight heuristic approach which approximates the optimal configuration for resource allocation at a lower computational cost. This method of resource allocation compares favorably to a number of alternative strategies with respect to fairness.
Determinants of Antidepressant Treatment Compliance: Implications for Policy

Depression is among the most prevalent, devastating, and undertreated disorders in our society. Treatment with antidepressant medications is effective in controlling symptoms, but treatment beyond the point of symptom resolution is necessary to restore functional status and prevent recurrent episodes. An important step in improving compliance is to identify the determinants of antidepressant treatment compliance. A broader motivation for our study is to examine compliance by patients with a chronic but treatable disease. With claims data between 1990 and 1993, this study uses logistic regression analysis to examine the determinants of compliance among 2,012 antidepressant recipients. The results show that initiating treatment with a tricyclic antidepressant reduces the probability of antidepressant treatment compliance. Initiating treatment with a selective serotonin reuptake inhibitor and undergoing family, group, or individual psychotherapy treatments increase the probability of compliance. Case management does not meaningfully affect compliance. Implications for policy and clinical practice are discussed.
Comparative analysis of reflection photoelasticity in mandible’s segment with or without base

Introduction and Objective: To analyze if axial loads of 50 N and 100 N, applied on an implant of 3.75 X 10 mm (Conexão-Jaú, SP, Brazil) fixed on the central portion of a prototype of the mandible with and without support base, generate isochromatic fringes of different intensity and ways. Material and methods: The sample was a segment of the mandible with 115 mm in length, 30 mm height,and 12 mm in thickness, from a block of #7 rose wax. This matrix was adapted to a modified articulator in such way that its base could or not keep contact with the lower arm of the articulator, simply by modifying the support axis. An implant was put perpendicular to the segment of the mandible’s body and then the photoelastic model obtained. The healing abutment was screwed to the implant to receive loads from 50 N to 100 N, with the model with or withoutcontact of its base with the inferior arm of the articulator. Results: Alterations in the reflections of the colors was observed when the implants had been submitted to loads of 50 N and 100 N with or without supported base. Conclusion: Based on the results, it can be concluded that during the photoelasticity analyses alterations in the color standards occurred depending on the support of the sample and the applied load.
Bounded Quadrant System: Error-bounded trajectory compression on the go

Long-term location tracking, where trajectory compression is commonly used, has gained high interest for many applications in transport, ecology, and wearable computing. However, state-of-the-art compression methods involve high space-time complexity or achieve unsatisfactory compression rate, leading to rapid exhaustion of memory, computation, storage and energy resources. We propose a novel online algorithm for error-bounded trajectory compression called the Bounded Quadrant System (BQS), which compresses trajectories with extremely small costs in space and time using convex-hulls. In this algorithm, we build a virtual coordinate system centered at a start point, and establish a rectangular bounding box as well as two bounding lines in each of its quadrants. In each quadrant, the points to be assessed are bounded by the convex-hull formed by the box and lines. Various compression error-bounds are therefore derived to quickly draw compression decisions without expensive error computations. In addition, we also propose a light version of the BQS version that achieves O(1) complexity in both time and space for processing each point to suit the most constrained computation environments. Furthermore, we briefly demonstrate how this algorithm can be naturally extended to the 3-D case. Using empirical GPS traces from flying foxes, cars and simulation, we demonstrate the effectiveness of our algorithm in significantly reducing the time and space complexity of trajectory compression, while greatly improving the compression rates of the state-of-the-art algorithms (up to 47%). We then show that with this algorithm, the operational time of the target resource-constrained hardware platform can be prolonged by up to 41%.
Behavior Ever Follows Intention?: A Validation of the Security Behavior Intentions Scale (SeBIS)

The Security Behavior Intentions Scale (SeBIS) measures the computer security attitudes of end-users. Because intentions are a prerequisite for planned behavior, the scale could therefore be useful for predicting users' computer security behaviors. We performed three experiments to identify correlations between each of SeBIS's four sub-scales and relevant computer security behaviors. We found that testing high on the awareness sub-scale correlated with correctly identifying a phishing website; testing high on the passwords sub-scale correlated with creating passwords that could not be quickly cracked; testing high on the updating sub-scale correlated with applying software updates; and testing high on the securement sub-scale correlated with smartphone lock screen usage (e.g., PINs). Our results indicate that SeBIS predicts certain computer security behaviors and that it is a reliable and valid tool that should be used in future research.
UNIFORM FRACTIONAL FACTORIAL DESIGN TABLES FOR ENERGY PILES WITH MAXIMUM THERMAL CONDUCTANCE

Energy Piles are the heat exchangers of Ground Source Heat Pumps (GSHP) that transfer the buildings heat to the lower temperature shallow ground reducing the energy consumption in the cooling of buildings. These piles are designed with main objective of lowest thermal resistance. In this paper, nine factors influencing the thermal resistance of the energy pile are defined and statistically evaluated. These nine factors are; number of tubes, pile diameter, tube diameter, tube thickness, tube location, pile conductivity, tube conductivity, soil conductivity, and water flow rate. The thermal resistance of the energy pile is calculated using the line source analytical model. The significance of these factors is evaluated using fractional factorial uniform design tables. The results show significant decrease in the pile thermal resistivity with the increase of the tube diameter, number of tubes, water flow rate, and tube and pile thermal conductivities. On the other hand, decrease of the tube thickness, and pile diameter slightly decrease the pile thermal resistivity. Furthermore, the tubes located near the piles outer surface show significant decrease in the pile thermal resistivity. Also, the soil thermal conductivity has shown insignificant effects on the pile thermal resistivity.
Identifying Synapses Using Deep and Wide Multiscale Recursive Networks

In this work, we propose a learning framework for identifying synapses using a deep and wide multi-scale recursive (DAWMR) network, previously considered in image segmentation applications. We apply this approach on electron microscopy data from invertebrate fly brain tissue. By learning features directly from the data, we are able to achieve considerable improvements over existing techniques that rely on a small set of hand-designed features. We show that this system can reduce the amount of manual annotation required, in both acquisition of training data as well as verification of inferred detections.
Straight skeletons for binary shapes

This paper reviews the concept of straight skeletons, which is well known in computational geometry, and applies it to binary shapes that are used in vision-based shape and object recognition. We devise a novel algorithm for computing discrete straight skeletons from binary input images, which is based on a polygonal approximation of the input shape and a hybrid method that combines continuous and discrete geometry. In our experiments, we analyze the potential of straight skeletons in shape recognition, by comparing their performance with medial-axis based shock graphs on the Kimia shape databases. Our discrete straight skeleton algorithm is not only outperforming typical skeleton algorithms in terms of computational complexity, it also delivers surprisingly good results in its straightforward application to shape recognition.
Antiangiogenic Effect of Ethanol Extract of Vigna angularis via Inhibition of Phosphorylation of VEGFR2, Erk, and Akt

Though dietary azuki bean (Vigna angularis) seed containing antioxidant proanthocyanidins was known to have multibiological activities including antioxidant, hypotensive, anti-inflammatory, and immunomodulatory activities, the antiangiogenic activity of ethanol extract of Vigna angularis (EVA) was never reported so far. In the present study, the antiangiogenic mechanism of EVA was examined in human umbilical vein endothelial cells (HUVECs). EVA showed weak cytotoxicity in HUVECs, while it significantly suppressed the VEGF induced proliferation of HUVECs. Consistently, wound healing assay revealed that EVA inhibited the VEGF induced migration of HUVECs. Also, EVA abrogated the VEGF induced tube formation of HUVECs in a concentration dependent fashion. Furthermore, Matrigel plug assay showed that EVA significantly reduced the hemoglobin level of Matrigel plug in mice compared to untreated control. Of note, EVA effectively attenuated the phosphorylation of VEGFR2, Erk, and Akt in VEGF-treated HUVECs. Overall, our findings suggest that EVA inhibits angiogenesis in VEGF-treated HUVECs via inhibition of phosphorylation of VEGFR2, ERK, and Akt.
A 512×512-cell associative CAM/Willshaw memory with vector arithmetic

In this paper we present a CMOS implementation of a 512×512-cell Associative Content Addressable Memory (ACAM) in 180 nm CMOS. The memory can be operated either as an associative CAM or it can be configured into a Willshaw memory for operating with sparse data. The vector matching operation can use a tunable hit threshold or the strongest hit can be selected with a winner-take-all (WTA) network. Built-in row and column circuitry can perform logic operations on the contents of the row and column memories. The operation of the circuit is verified experimentally with an example on computing with random vectors.
TiO2-based memristors and ReRAM: materials, mechanisms and models (a review)

The memristor is the fundamental nonlinear circuit element, with uses in computing and computer memory. Resistive Random Access Memory (ReRAM) is a resistive switching memory proposed as a non-volatile memory. In this review we shall summarize the state of the art for these closely-related fields, concentrating on titanium dioxide, the well-utilized and archetypal material for both. We shall cover material properties, switching mechanisms and models to demonstrate what ReRAM and memristor scientists can learn from each other and examine the outlook for these technologies.
Automatic Generation of Temporal Feature Vectors with Application to Tourism Recommender Systems

Recommender systems have been widely used in our daily life to recommend objects to users meeting the users' preference. In this paper, we focus on objects with temporally variable features such as restaurant with seasonal dishes and point-of-interests (POIs) to have seasonal attractions, and propose a method to automatically generate temporal feature vectors for those objects. The basic idea of the proposed method is: 1) to identify the vocabulary concerned with objects through Wikipedia; 2) to identify the trend over all objects through Twitter; and 3) to highlight the weight of words contained in each identified trend to obtain temporal feature vectors for each object. We built a tourism recommender system to evaluate the effectiveness of the proposed method. The result of experiments indicates that: 1) the variance of temporal feature vectors follows the Gaussian distribution, 2) those vectors certainly reflect the similarity of POIs for a designated time period, and 3) such a property of feature vectors can be effectively used for the seasonal recommendation of POIs.
Hiding a Semantic Hierarchy in a Markov Model

We introduce a new model of selectional preference induction. Unlike previous approaches, we provide a stochastic generation model for the words that appear as arguments of a predicate. More specifically, we define a hidden Markov model with the general shape of a given semantic class hierarchy. This model has a number of at tract ive features, among them that selectional preference can be seen as distributions over words. Initial results are promising. However, unsupervised parameter estimation has proven problematic. A central problem is word sense ambiguity in the training corpora. We describe a t tempts to modify the forward-backward algorithm, an EM algorithm, to handle such disambiguation. Although these a t tempts were unsuccessful at improving performance, we believe they give insight into the nature of the bottlenecks and into the behavior of the EM algorithm.
Energy-Efficient Freshness Transmission Protocol in Wireless Sensor Networks

Freshness is one of the most desired security properties of sensor network communication architecture. And sequence number is a common technique that is used as a freshness identifier to achieve freshness transmission. However, due to characteristics of wireless sensor network, such as resource-constrained and attack prone, it is vulnerable and energy-expensive to transmit sequence number directly. In this paper, we propose an Energy-efficient Freshness Transmission Protocol (EFTP) which lowers overhead as well as improves the security of sensor network. In EFTP, each sequence number is disarranged via a tree-architecture and further divided into two correlative parts. Encrypted and attached to messages, sequence number can be transferred securely. The receiver reverses process of above operations to authenticate the freshness of received message. Compared with previous works, EFTP achieves the freshness requirement without any redundant overhead or keeping synchronized sequence number. Simulations show that our solution performs very well not only in freshness requirement but also in energy-efficient.
Comparison of Fracture Models to Quantify the Effects of Material Plasticity on the Ductile Fracture Propagation in Pipelines

Various numerical approaches have been developed in the last years aimed to simulate the ductile fracture propagation in pipelines transporting CO2 or natural gas. However, a reliable quantification of the influence of material plasticity on the fracture resistance is still missing. Therefore, more accurate description of the material plasticity on the ductile fracture propagation is required based on a suitable numerical methodology. In this study, different plasticity and fracture models are compared regarding the ductile fracture propagation in X100 pipeline steel with the objective to quantify the influence of plasticity parameters on the fracture resistance. The plastic behavior of the investigated material is considered by the quadratic yield surface in conjunction with a non-associated quadratic plastic flow potential. The strain hardening can be appropriately described by the mixed Swift-Voce law. The simulations of ductile fracture are conducted by an uncoupled, modified Mohr-Coulomb (MMC) and the micromechanically based Gurson-Tvergaard-Needleman (GTN) models. In contract to the original GTN model, the MMC model is capable of describing ductile failure over wide range of stress states. Thus, ductile fracture resistance can be estimated for various load and fracture scenarios. Both models are used for the simulation of fracture propagation in DWTT and 3D pressurized pipe sections. The results from the present work can serve as a basis for establishing the correlation between plasticity parameters and ductile fracture propagation.
Identification of stem-like cells and clinical significance of candidate stem cell markers in gastric cancer

The existence of gastric cancer stem cells (CSCs) has not been definitively proven and specific cell surface markers for identifying gastric CSCs have largely not been identified. Our research aimed to isolate potential gastric CSCs and clarify their clinical significance, while defining markers for GCSC identification and verification. Here, we report that spheroid cells possess stem cell-like properties, and overexpress certain stem cell markers. CD133 or CD44-positive cells also exhibit properties of CSCs. The expression of Oct4, Sox2, Gli1, CD44, CD133, p-AKT, and p-ERK was significantly higher in metastatic lesions compared to that in primary lesions. Elevated expression of some of these proteins was correlated with a more aggressive phenotype and poorer prognosis, including Oct4, Sox2, Gli1, CD44, and p-ERK. Multivariate Cox proportional hazards model analysis showed that only CD44 is an independent factor. Knockdown of CD44 down-regulated the stem cell-like properties, which was accompanied by the down-regulation of p-ERK and Oct4. Oct4 overexpression could reverse the decreased CSCs properties induced by CD44 knockdown. Taken together, our research revealed that spheroid cell culture, and CD133 or CD44-labeled FACS methods can be used to isolate gastric CSCs. Some CSC markers have clinical significance in predicting the prognosis. CD44 is an independent prognostic factor and maintains the properties of CSCs in CD44-p-ERK-Oct4 positive feedback loop.
SENSEVAL-2: Overview

SENSEVAL-2: The Second International Workshop on Evaluating Word Sense Disambiguation Systems was held on July 5-6, 2001. This paper gives an overview of SENSEVAL-2, discussing the evaluation exercise, the tasks, the scoring system, and the results. It ends with some recommendations for future evaluation exercises.
Semantic Browsing of a Domain Specific Resources: The Corese-NeLI Framework

In this paper, we present the Corese-NeLI semantic Web browser dedicated to navigating resources in the infectious disease domain. We describe an overview of the semantic Web browser and outline its functionality and the knowledge organization system used as a background knowledge for both the annotation and search processes. The evaluation of the vocabulary-based annotation, essential for the semantic browser, uses the National Electronic Library of Infection as a test bed and demonstrates over 96% correct annotations.
Flexible extrinsic calibration of non-overlapping cameras using a planar mirror: Application to vision-based robotics

Multi-camera systems are used in many domains such as vision-based robotics or video surveillance. An accurate extrinsic calibration is usually required. In most of cases, this task is done by matching features through different views of the same scene. However, if the cameras' fields of view do not overlap, such a matching procedure is not feasible anymore. Despite this constraint, this article deals with a simple and flexible extrinsic calibration method. The main contribution is the use of an unknown geometry scene and a planar mirror to create an overlap between views of the different cameras. Furthermore, the impact of the mirror refraction is also studied. The aim is the calibration of two non-overlapping cameras embedded on a vehicle, for visual navigation purpose in urban environment. The proposed approaches have been validated with both synthetic and real data in a metrological experimental framework.
From hierarchical partitions to hierarchical covers: optimal fault-tolerant spanners for doubling metrics

A (1+ε)-spanner for a doubling metric (X, δ) is a subgraph H of the complete graph corresponding to (X, δ), which preserves all pairwise distances to within a factor of 1 + ε. A natural requirement from a spanner, which is essential for many applications (mainly in distributed systems or wireless networks), is to be robust against vertex and edge failures -- so that even when some vertices and edges in the network fail, we still have a (1 + ε)-spanner for what remains. The spanner H is called a k-fault-tolerant (1 + ε)-spanner, for 1 ≤ k ≤ n -- 2, if for any F ⊆ X with |F| ≤ k, the graph H -- F (obtained by removing from H the vertices of F and their incident edges) is a (1 + ε)-spanner for X -- F. In this paper we devise an optimal construction of fault-tolerant spanners for doubling metrics: For any n-point doubling metric, any ε > 0, and any integer 1 ≤ k ≤ n -- 2, our construction provides a k-fault-tolerant (1+ε)-spanner with optimal degree O(k) within optimal time O(n log n + kn). We then strengthen this result to provide near-optimal (up to a factor of log k) guarantees on the diameter and weight of our spanners, namely, diameter O(log n) and weight O(k2 + k log n) · ω(MST), while preserving the optimal guarantees on the degree O(k) and the runtime O(n log n + kn). Our result settles several fundamental open questions in this area, culminating a long line of research that started with the STOC'95 paper of Arya et al. and the STOC'98 paper of Levcopoulos et al. On the way to this result we develop a new technique for constructing spanners in doubling metrics. In particular, our spanner construction is based on a novel hierarchical cover of the metric, whereas most previous constructions of spanners for doubling and Euclidean metrics (such as the net-tree spanner) are based on hierarchical partitions of the metric. We demonstrate the power of hierarchical covers in the context of geometric spanners by improving the state-of-the-art results in this area.