Miracle-dz/UniFAR-FTUs huggingface dataset:
Most balanced minimum cuts and partially ordered knapsack

We consider the problem of finding most balanced cuts among minimum st-edge cuts and minimum st-vertex cuts, for given vertices s and t, according to different balance criteria. For edge cuts [S, S] we seek to maximize min{|S|, |S|}. For vertex cuts C of G we consider the objectives of (i) maximizing min{|S|, |T |}, where {S, T} is a partition of V (G)\C with s ∈ S, t ∈ T and [S, T ] = ∅, (ii) minimizing the order of the largest component of G−C, and (iii) maximizing the order of the smallest component of G−C. All of these problems are shown to be NP-hard. We give a PTAS for the edge cut variant and for (i). We give a 2-approximation for (ii), and show that no non-trivial approximation exists for (iii) unless P=NP. To prove these results we show that we can partition the vertices of G, and define a partial order on the subsets of the partition, such that ideals of the partial order correspond bijectively to minimum st-cuts of G. This shows that the problems are closely related to Uniform Partially Ordered Knapsack (UPOK), a variant of POK where element utilities are equal to element weights. Our PTAS is also a PTAS for special types of UPOK instances.
Causal Feature Selection

This chapter reviews techniques for learning causal relationships from data, in application to the problem of feature selection. Most feature selection methods do not attempt to uncover causal relationships between feature and target and focus instead on making best predictions. We examine situations in which the knowledge of causal relationships benefits feature selection. Such benefits may include: explaining relevance in terms of causal mechanisms, distinguishing between actual features and experimental artifacts, predicting the consequences of actions performed by external agents, and making predictions in non-stationary environments. Conversely, we highlight the benefits that causal discovery may draw from recent developments in feature selection theory and algorithms.
Structural Magnetic Resonance Imaging Can Identify Trigeminal System Abnormalities in Classical Trigeminal Neuralgia

Classical trigeminal neuralgia (TN) is a chronic pain disorder that has been described as one of the most severe pains one can suffer. The most prevalent theory of TN etiology is that the trigeminal nerve is compressed at the root entry zone (REZ) by blood vessels. However, there is significant evidence showing a lack of neurovascular compression (NVC) for many cases of classical TN. Furthermore, a considerable number of patients who are asymptomatic have MR evidence of NVC. Since there is no validated animal model that reproduces the clinical features of TN, our understanding of TN pathology mainly comes from biopsy studies that have limitations. Sophisticated structural MRI techniques including diffusion tensor imaging provide new opportunities to assess the trigeminal nerves and CNS to provide insight into TN etiology and pathogenesis. Specifically, studies have used high-resolution structural MRI methods to visualize patterns of trigeminal nerve-vessel relationships and to detect subtle pathological features at the trigeminal REZ. Structural MRI has also identified CNS abnormalities in cortical and subcortical gray matter and white matter and demonstrated that effective neurosurgical treatment for TN is associated with a reversal of specific nerve and brain abnormalities. In conclusion, this review highlights the advanced structural neuroimaging methods that are valuable tools to assess the trigeminal system in TN and may inform our current understanding of TN pathology. These methods may in the future have clinical utility for the development of neuroimaging-based biomarkers of TN.
Cross-Topic Authorship Attribution: Will Out-Of-Topic Data Help?

Most previous research on authorship attribution (AA) assumes that the training and test data are drawn from same distribution. But in real scenarios, this assumption is too strong. The goal of this study is to improve the prediction results in cross-topic AA (CTAA), where the training data comes from one topic but the test data comes from another. Our proposed idea is to build a predictive model for one topic using documents from all other available topics. In addition to improving the performance of CTAA, we also make a thorough analysis of the sensitivity to changes in topic of four most commonly used feature types in AA. We empirically illustrate that our proposed framework is significantly better than the one trained on a single out-of-domain topic and is as effective, in some cases, as same-topic setting.
Novel traffic engineering for reservation services network

We propose a heuristic routing and reservation algorithm for networks providing reservation services. Important requirements of a reservation service network are to increase network resource utilization, to provide routes satisfying specified levels of quality of service (QoS), and to minimize service interruption under the situation that it is difficult to tell exactly when, how many, and what kinds of requests will arrive in the future. To satisfy these requirements, our proposal includes the following key ideas. First, it calculates rerouting targets only when a newly requested path competes with paths in service or reserved for future use, and reroutes competing path routes, or the rerouting targets, if necessary. Additionally, rerouting is executed so as to satisfy userspsila QoS demands. This ensures that the userspsila QoS demands are satisfied, network resource utilization is increased, and changes to path routes that are in service are minimized. Our algorithm can be adopted for advance reservation requests as well as immediate ones. Numerical results indicate rerouting only a few paths improves the request acceptance success rate by 50% o compared to the do-nothing approach.
A frame aggregation scheduler for QoS-sensitive applications in IEEE 802.11n WLANs

The IEEE 802.11n standard promises to extend todays most popular WLAN standard by significantly increasing reach, reliability, and throughput. Ratified on september 2009, this standard defines many new physical and medium access control (MAC) layer enhancements. These enhancements aim to provide a data transmission rate of up to 600 Mbps. The frame aggregation mechanism of 802.11n MAC layer can improve the efficiency of channel utilization by reducing the protocol overheads. In this paper we investigate the effect of frame aggregation on the support of voice and video applications in wireless networks. We also propose a new frame aggregation scheduler that considers specific QoS requirements for multimedia applications. We dynamically adjust the aggregated frame size based on frame's access category defined in 802.11e standard.
A high frequency resonant power converter for high power RF applications

This paper considers the design of a "long-pulse" modulator supply rated at 25 kV, 10 A (250 kW peak power, duty ratio 10%, 25 kW average power, pulse length ap 1-2 ms). The supply is based on direct modulation of a multi-phase resonant power supply, fed by an active rectifier. The objectives of the development are to produce a compact power supply, with low stored energy and with high power quality at the utility supply. The paper provides a brief overview of the technology, followed by a discussion of the design choices
Phonological processes of Japanese based on the theory of phonology as human behavior

By analyzing speech errors (normal and pathological) and loanwords of Japanese within the theory of Phonology as Human Behavior, we seek to account for why processes such as substitution occur as they do by referring to the “struggle” between speakers’ desire for maximum communication (the communication factor) and minimal effort (the human factor). We conclude that (1) the error patterns or the processes observed in loanword adaptations are not random but motivated and that clinical phonology represents a more extreme version of the “mini-max” struggle where the human factor overrides the communication factor; (2) the communicative forces found within different word positions have a great influence on how and where the phonological processes of loanwords occur.
Directionally-structured dictionary learning and sparse representation based on subspace projections

This paper presents a new strategy for directionally-structured dictionary learning and component-wise sparse representation. The signal space is divided into directional subspace triplets. Directionally-selective projection operators are designed for this purpose. Each triplet contains two orthogonal subspaces along with a remainder one. For each triplet, a compact dictionary is learned. Sparse representation is done in an analogous manner. The most-fitting dictionary triplet is selected for each signal based on its directional structure. Using the designed projection operators, the signal is decomposed into three subspace components living in the three triplet subspaces. The signal's sparse approximation is obtained as the direct summation of the sparse approximations of these three components, each coded over its subspace dictionary. Experiments conducted over a set of natural images show that the proposed strategy improves the sparse representation coding quality over standard methods, as tested in the problem of image representation.
A process framework for securing an e-Learning ecosystem

e-Learning is a pedagogy empowered by Information and Communication technologies supporting education / training. While e-Learning exists over the past decade and a half, it is receiving considerable attention only in the recent times. Both Industry and Academia are heavily depending on e-Learning in streamlining their teaching process and also in meeting the just-in-time learning requirements of individual learners. Though huge budget is spent in creating, maintaining and offering the courses through e-Learning, security is still not considered as an important aspect in e-Learning. This leads to unauthorized access to course content, thread of discussions and evaluation reports of the learners. Any security compromise in e-Learning Ecosystem will have a serious impact on credibility of the institution. While security tools are used to tackle various issues independently, these alone will not suffice but process should be in place in order to address the continuously growing security threat. Through this paper, various security & privacy risks associated with e-Learning are enumerated and a process framework is proposed for securing an e-Learning Ecosystem, which helps to address the security problem in a systematic way in order to foster the benefits of e-Learning.
Collision detection algorithm based on AABB for Minimally Invasive Surgery

Minimally Invasive Surgery (MIS) is a specialized surgical technique that can reduce patients' pain and permit a faster recovery compared with conventional open surgery. But surgeons require extensive training to obtain the expertise due to its complexity. In the previous research, we have developed a virtual reality system to train surgeons to obtain the expertise to correctly perform the interventions. In the system we have reconstructed 3D vessel model based on CT images and designed a 3D catheter model based on finite element method. In this paper, in order to provide the surgeon with a sense of touch, we work on force feedback and collision detection algorithm. We design a force feedback which contains three kinds of force: viscous force, frictional force and collision force. The viscous force stands for the force between the catheter and blood; the frictional force means the force between catheter body and vessels; the collision force is the force between the catheter tip and vessel. Then we develop a fast collision detection algorithm based on AABB (Axis-Aligned Bounding Boxes) method. In the algorithm, there are two key areas: Area I - the area around the catheter tip and Area II - the previous collision area. Area I is a dynamic area and it will change with the position of the catheter tip. Area II is some static areas but the amount of the areas is changeable. By this way, we can reduce the collision detection time and achieve real time training.
Ultimate luminosities and energies of photon colliders

A photon collider lumonosity and its energy are determined by the parameters of an electron-electron linear collider (energy, power, beam emittances) and collision effects. The main collision effect is the coherent e+e− pair creation. At low energies (2E 0.65)⩾1035 cm−2s−1 is possible (1033−1034 is sufficient). At higher energies the limited average beam power and coherent pair creation restrict the maximum energy of photon colliders (with sufficient luminosity) at Ecm∼5 TeV. Obtaining high luminosities requires the development of new methods of production beams with low emittances such as a laser cooling.
School-wide mediated prosocial development: Applying a sociocultural understanding to inclusive practice and character education

The issue of inclusive practice, particularly as it pertains to how school staff address antisocial behavior to improve learning and life outcomes for children has become a primary topic of discussion among educational stakeholders. Within an action research framework, this investigation used a mixed-method multiple case study approach to investigate what impact character education has on school climate and pupil behavior within five primary schools in England. Data were collected using interviews, observations, and archived records. All data-sets suggest that a multicomponent socioculturally inspired program can have positive effects on teacher talk, student on-task behavior, and decrease disruptive incidences during class and office referrals for antisocial behavior. Moreover, according to participant reports, the findings indicate that there is a positive effect on the school’s ability to meet the social, emotional, and cognitive needs of pupils within a multicultural setting following the implementation of a whole-school character education program.
SOTRIP: a self organizing protocol for traffic information

The European Union recently proposed to increase Europe's radio spectrum for technology that allows cars to communicate traffic information. By letting cars to communicate to each other, less cars will end up in a traffic jam. It is estimated that traffic jams will cost the economy around 80 billion euro by 2010. Currently, traffic news is collected by a central organ, which processes the data and broadcasts the information to navigation tools (e.g. HD traffic by TomTom), internet websites and news programs. This information flow causes the driver to receive information about a traffic jam with a delay of approximately half an hour. In this paper a solution is presented that is decentralised and which makes faster information dissemination possible. We introduce a vehicle-to-vehicle communication protocol (called sotrip) that works by letting cars on opposite lanes exchange information about the traffic situation on the road ahead for the receiving car. First experimental results confirm the better overall throughput, especially with heavy traffic.
An Alternative Approach to Needle Placement in Cervicothoracic Epidural Injections

The use of fluoroscopy guidance together with the loss of resistance technique during epidural injections has been advocated lately; moreover, epidural injections in the absence of fluoroscopic guidance have a high rate of inaccurate needle-tip placement during the injections. However, the approach to the lower cervical and upper thoracic epidural space may be challenging due to its obscure lateral fluoroscopic views from overlying normal tissue structures. In this case, we report an alternative oblique C-arm fluoroscopy guided view approach to supplement the standard anterior-posterior and lateral fluoroscopic views to facilitate successful needle placement and precise anatomical localization of the epidural space.
State estimation of moving objects based on ellipses

State estimation of moving objects in complex environments is the foundation for robot cognition and intelligence. This study focuses on targets identification and states estimation utilizing a single laser scanner, which adopts multiple ellipses to model any possible object in the field of view of the sensor. First, a nearest neighbor clustering algorithm is used to achieve the segments of the data frame. Each segment represents a potential target that consists of a group points close to each other in Euclidean distance. Then, an ellipse can be optimized with covering the group points by minimizing the ellipse area. The solution can be obtained by solving a traditional Lowner-John ellipsoid problem. The movement of the target will be replaced by the motion of the corresponding ellipse. Next, the target states are estimated by data association technique on their frame sequences. Finally, experiments with a still or moving robot mounted with a laser sensor are taken indoor and outdoor to verify the feasibility and effectiveness of our novel method.
Service Links and Wage Inequality Kong Weng Ho National University of Singapore Hian Teck Hoon Singapore Management

In our general equilibrium model, the variety of specialized service links affects international production fragmentation in manufacturing. Decreases in cost of education or fixed cost of service links raise the relative supply of skilled workers, increase service specialization, and decrease the price of aggregate services. Consequently, the market for serviceand skill-intensive component manufacturing enlarges, raising relative demand for skilled workers. Empirically, endogenous change in international outsourcing rather than skill-biased technological progress is the main reason for a modest decline in wage gap despite the rapid rise in relative supply of skilled workers in Singapore from 1978 to 2000. JEL classification: F16; J31; O53
Scientific table type classification in digital library

Tables are ubiquitous in digital libraries and on the Web, utilized to satisfy various types of data delivery and document formatting goals. For example, tables are widely used to present experimental results or statistical data in a condensed fashion in scientific documents. Identifying and organizing tables of different types is an absolutely necessary task for better table understanding, and data sharing and reusing. This paper has a three-fold contribution: 1) We propose Introduction, Methods, Results, and Discussion (IMRAD)-based table functional classification for scientific documents; 2) A fine-grained table taxonomy is introduced based on an extensive observation and investigation of tables in digital libraries; and 3) We investigate table characteristics and classify tables automatically based on the defined taxonomy. The preliminary experimental results show that our table taxonomy with salient features can significantly improve scientific table classification performance.
Immunization of melanoma patients with BEC2-keyhole limpet hemocyanin plus BCG intradermally followed by intravenous booster immunizations with BEC2 to induce anti-GD3 ganglioside antibodies.

BEC2 is an anti-idiotypic mouse monoclonal antibody that mimics GD3 ganglioside. Previous clinical trials demonstrated that intradermal immunization using 2.5 mg of BEC2 with BCG or i.v. immunization with 10 mg of BEC2 can induce anti-GD3 antibodies in a subset of patients. We hypothesized that combining these two immunization strategies might be more effective in inducing anti-GD3 antibodies and that conjugation of BEC2 to keyhole limpet hemocyanin (KLH) would further enhance the immunogenicity of BEC2. In this clinical trial, 18 melanoma patients who were free of disease after complete surgical resection within 1-6 months received intradermal immunizations on weeks 0, 2, 4, 6, and 10 with 2.5 mg of BEC2 conjugated to KLH and mixed with BCG (BEC2-KLH/BCG). Booster immunizations of 10 mg of unconjugated BEC2 were administered i.v. on weeks 24, 37, and 50. Four of 18 patients (22%) developed IgM anti-GD3 antibodies. No IgG anti-GD3 antibodies were detected. All four responding patients developed anti-GD3 IgM during immunization with BEC2-KLH/BCG; only one patient demonstrated a reboost of the IgM anti-GD3 titer during the i.v. immunizations. Thirteen of the patients are free of melanoma (3 after undergoing re-resection for local relapse); 14 patients (78%) remain alive with a median follow-up of 28 months. These results confirm our previous trial, showing that BEC2 with BCG can induce anti-GD3 antibodies in patients. The data do not provide evidence that conjugation to KLH increases the immunogenicity of BEC2 when it is administered with BCG.
Fast hardware implementation of Gabor filter based motion estimation

Motion estimation in image sequences is a fundamental issue in many applications as for instance in artificial vision and three-dimensional scene reconstruction. Among all the existing techniques, we are particularly interested in methods using Gabor filters, which are known to furnish quality results but usually require intensive calculation. A new, fast energy-based method is presented, which combines in a direct manner the energetic responses of Gabor spatio-temporal filters organized in triads. An implementation of this technique on a general purpose Digital Signal Processor (DSP) board is described and the advantages compared with Very Large Scale Integration (VLSI) and parallel machine approaches. Our hardware implementation attains a reasonably fast output rate (several images/second) for a better resolution than in the most recent VLSI implementations. These results open interesting perspectives for real-time implementations (such as in mobile robotics) and for the obtention of higher-level results by combining different Gabor filter techniques (for motion, edge detection, texture analysis, etc.).
Predictive Performance of Weighted Relative Accuracy

Weighted relative accuracy was proposed in [4] as an alternative to classi cation accuracy typically used in inductive rule learners. Weighted relative accuracy takes into account the improvement of the accuracy relative to the default rule (i.e., the rule stating that the same class should be assigned to all examples), and also explicitly incorporates the generality of a rule (i.e., the number of examples covered). In order to measure the predictive performance of weighted relative accuracy, we implemented it in the rule induction algorithm CN2. Our main results are that weighted relative accuracy dramatically reduces the size of the rule sets induced with CN2 (on average by a factor 9 on the 23 datasets we used), at the expense of only a small average drop in classi cation accuracy.
In-training and post-training generalization methods: The case of ppar — α and ppar — γ agonists

In this paper, the effects of regularization on the generalization capabilities of a neural network model are analyzed. We compare the performance of Levenberg-Marquardt and Bayesian Regularization algorithms with and without post-training regularization. We show that although Bayesian Regularization performs slightly better than Levenberg-Marquardt, the model trained using Levenberg-Marquardt holds more information about the data set which by proper post-processing regularization can be extracted. This post-processing regularization imposes smoothness and similarity.