Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Ant Colony Optimizations for Stereo Matching

The stereo matching problem, which obtains the correspondence between left and right images, can be cast as a search problem. The matching of all candidates in the same line forms a 2D optimization task, which is an NP-hard problem in nature. Two characteristics are often utilized to enhance the performance of stereo matching, i.e. concurrent optimization of several scanlines and correlations among adjacent scan-lines. Such correlations are considered to be posterior, which require several trails for their discovery. In this paper, a Multiple Ant Colony based approach is proposed for stereo matching because of the Ant Colony optimization’s inherent capability of relation discovery through parallel searching. The Multiple Ant Colony Optimization (MACO) is efficient to solve large scale problems. For stereo matching, it evaluates subsolutions and propagates the discovered information by pheromone, taking into account the ordering and uniqueness constraints of candidates in images. The proposed algorithm is proved to be able to find the optimal matched pairs theoretically and verified by experiments.
Global Estimation with Local Communication ∗

We present a distributed optimization algorithm for estimating a continuous function such as temperature or pollution over a geographic region, e.g., a road network. The estimate is generated from samples taken by sensors placed alongside roads or in cars driving along them. We employ piecewise estimation, that is, we divide the region into sectors and find an estimate for each sector, e.g., a polynomial or a line, so that their union is a continuous function that minimizes some global error function. The computation is distributed by designating a node (either virtual or physical) that is responsible for estimating the function in each sector. The estimate is then computed based on the samples taken in the sector and information from adjacent nodes. The algorithm works in networks with bounded, yet unknown, latencies. It accommodates dynamic inputs (samples) and node arrivals and departures. Our algorithm converges to the global optimum with only local communication, using a novel, distributed implementation of coordinate ascent optimization.
A Private and Scalable Authentication for RFID Systems Using Reasonable Storage

In recent years, numerous authentication protocols for radio frequency identification (RFID) systems have been proposed to protect privacy. Due to the hardware resource limitation on RFID tags, the majority of these protocols are based on symmetric-key ciphers. However, most of them lack scalability at the reader side as they require a linear or logarithmic search proportional to the number of tags in the system in order to authenticate a tag. Recently proposed scalable protocols require a constant-time search and utilize hash look-up tables. However, the storage requirement of the hash-table in these protocols is very large, therefore not practical. Moreover, previous proposals do not support dynamic resizing where the number of tags changes during the life time of the RFID system. In this paper, a new Re-Hash technique is presented to reduce the hash-table storage in constant-time authentication protocols. To the best of our knowledge, it offers the smallest storage cost in comparison to previous proposals. The Re-Hash technique is also further adapted to support the dynamic scalability for RFID systems.
Statistical power and measurement bias in multisite resting-state fMRI connectivity

Connectivity studies using resting-state functional magnetic resonance imaging are increasingly pooling data acquired at multiple sites. While this may allow investigators to speed up recruitment or increase sample size, multisite studies also potentially introduce systematic biases in connectivity measures across sites. In this work, we measure the inter-site bias in connectivity and its impact on our ability to detect individual and group differences. Our study was based on real multisite fMRI datasets collected in N=345 young, healthy subjects across 8 scanning sites with 3T scanners and heterogeneous scanning protocols. We first empirically show that typical functional networks were reliably found at the group level in all sites, and that the amplitude of the inter-site bias was small to moderate, with a Cohen's effect size below 0.5 on average across brain connections. We then implemented a series of Monte-Carlo simulations, based on real data, to evaluate the impact of the multisite bias on detection power in statistical tests comparing two groups (with and without the effect) using a general linear model, as well as on the prediction of group labels with a support-vector machine. As a reference, we also implemented the same simulations with fMRI data collected at a single site using an identical sample size. Simulations revealed that using data from heterogeneous sites only slightly decreased our ability to detect changes compared to a monosite study with the GLM, and had a more serious impact on prediction accuracy. However, the deleterious effect of multisite data pooling tended to decrease as the total sample size increased, to a point where differences between monosite and multisite simulations were small with N=120 subjects. Taken together, our results support the feasibility of multisite studies in rs-fMRI provided sample size is large enough.
Spectral partitioning for boundary estimation

We propose a spectral technique for analysing intermediate feature space of multiple classifier decisions, which enables a separable subset of patterns to be extracted. The method is applied to finding a set of patterns that are inconsistently classified, a random subset of which is left out of the training set of each expert in a multiple classifier framework.
Monkey See, Monkey Do: A Tool for TCP Tracing and Replaying

The performance of popular Internet Web services is governed by a complex combination of server behavior, network characteristics and client workload-all interacting through the actions of the underlying transport control protocol (TCP). Consequently, even small changes to TCP or to the network infrastructure can have significant impact on end-to-end performance, yet at the same time it is challenging for service administrators to predict what that impact will be. In this paper we describe the implementation of a tool called Monkey that is designed to help address such questions. Monkey collects live TCP trace data near a server, distills key aspects of each connection (e.g., network delay, bottleneck bandwidth, server delays, etc.) and then is able to faithfully replay the client workload in a new setting. Using Monkey, one can easily evaluate the effects of different network implementations or protocol optimizations in a controlled fashion, without the limitations of synthetic workloads or the lack of reproducibility of live user traffic. Using realistic network traces from the Google search site, we show that Monkey is able to replay traces with a high degree of accuracy and can be used to predict the impact of changes to the TCP stack.
The Galaxy Environment of a Quasar at z=1.226: A Possible Cluster Merger

We have conducted ultra-deep optical and deep near-infrared observations of a field around the z = 1.226 radio-quiet quasar 104420.8+055739 from the ClowesCampusano LQG of 18 quasars at z ∼ 1.3 in search of associated galaxy clustering. Galaxies at these redshifts are distinguished by their extremely-red colours, with I − K > 3.75, and we find a factor ∼11 overdensity of such galaxies in a 2.25×2.25arcmin 2 field centred on the quasar. In particular, we find 15–18 galaxies with colours consistent with being a population of passively-evolving massive ellipticals at the quasar redshift. They form ‘fingers’ in the V −K/K, I −K/K colour-magnitude plots at V −K ≃ 6.9, I − K ≃ 4.3 comparable to the red sequences observed in other z ≃ 1.2 clusters. We find suggestive evidence for substructure among the red sequence galaxies in the K image, in the form of two compact groups, 40 arcsec to the north, and 60 arcsec to the south-east of the quasar. An examination of the wider optical images indicates that this substructure is significant, and that the clustering extends to form a large-scale structure 2–3 h 1 Mpc across. We find evidence for a high ( ∼50%) fraction of blue galaxies in this system, in the form of 15–20 ‘red-outlier’ galaxies with I − K > 3.75 and V − I < 2.00, which we suggest are dusty, star-forming galaxies at the quasar redshift. Within 30 arcsec of the quasar we find a concentration of blue (V − I < 1) galaxies in a band that bisects the two groups of red sequence galaxies. This band of blue galaxies is presumed to correspond to a region of enhanced star-formation. We explain this distribution of galaxies as the early-stages of a cluster merger which has triggered both the star-formation and the quasar.
Missing data imputation for individualised CVD diagnostic and treatment

Cardiac health screening standards require increasingly more clinical tests consisting of blood, urine and anthropometric measures as well as an extensive clinical and medication history. To ensure optimal screening referrals, diagnostic determinants need to be highly accurate to reduce false positives and ensuing stress to individual patients. However, the data from individual patients partaking in population screening is often incomplete. The current study provides an imputation algorithm that has been applied to patient-centered cardiac health screening. Missing values are iteratively imputed in conjunction with combinations of values on subsets of selected features. The approach was evaluated on the DiabHealth dataset containing 2800 records with over 180 attributes. The results for predicting CVD after data completion showed sensitivity and specificity of 94% and 99% respectively. Removing variables that define cardiac events and associated conditions directly, left ‘age’ followed by ‘use’ of anti-hypertensive and anti-cholesterol medication, especially statins among the best predictors.
The Impact of Hybrid Automata on System Modeling and Analysis

The use of formal methods, techniques and tools may generally guarantee a systems’ safe operation. Such a process includes system modeling with a mathematical model, which is an approximation of the physical system, and is used to make easier the next step of analysis. Formal analysis of hybrid systems, which involve mixed continuous-valued and discrete dynamics, is concerned with verifying whether a hybrid system satisfies a desired specification, like avoiding an unsafe region of the state space. In this paper, we examine the general impact of hybrid automata on modeling and analyzing hybrid systems and we discuss in particular some advantages that arise when exploiting them in the context of particular methods for verifying the reachability of states in common paradigms that have already being investigated with other classical methods. Finally, we use two case studies concerning different classes of hybrid automata to demonstrate the applicability and the efficiency of exploiting hybrid automata on system modeling and analysis.
Cycle selection and neuro-voting system for classifying heart sound recordings

Aims: To address the PhysioNet/Computing in Cardiology (CinC) Challenge 2016 [1], this study aims to discuss a new method to classify PCG signals collected from a variety of clinical or nonclinical environments. Method: The raw PCG data is pre-processed by applying a band pass finite-duration impulse response filter (FIR) and discrete wavelet transform (DWT). For PCG delineation, first (s1) and second (s2) heart sounds are detected based on springer's algorithm. In this study, three analyses are used for feature extraction. The first one is time-frequency domain analysis in which the energy of nodes of wavelet packet transform is extracted beside the features already obtained from the time domain and perceptual domain analyses. Using fisher discriminant analysis (FDA) for feature reduction, a new space to better represent features is obtained. For heart sounds classification, a combination of three classifiers of artificial neural network (ANN) are implemented. To accomplish a precise classification, the output of all classifiers are employed in a fuzzy-logic algorithm (voting system). Results: By evaluation of the proposed method by training set the sensibility of 0.96, specificity of 0.97 and total score of 0.98 were achieved. Also, evaluation on the hidden test set resulted in the sensibility, specificity and modified accuracy of 0.811, 0.872 and 0.841, respectively.
/spl Pi/-web join in a web warehouse

With the enormous amount of data stored in the World Wide Web, it is increasingly important to design and develop powerful web warehousing tools. The key objective of our web warehousing project, called WHOWEDA (Warehouse of Web Data), is to design and implement a web warehouse that materializes and manages useful information from the web. We introduce the concept of /spl Pi/-web join in the context of WHOWEDA. /spl Pi/-web join operator is a web information manipulation operator to combine relevant web information residing in two web tables. Informally, it is the combination of web join and web project operators which filter out irrelevant information from a joined web table. We show how to construct the /spl Pi/-joined web table and its schema. We also highlight the benefits of the /spl Pi/-web join operator.
Membrane-Tethered Delta-Like 1 Homolog (DLK1) Restricts Adipose Tissue Size by Inhibiting Preadipocyte Proliferation

Adipocyte renewal from preadipocytes has been shown to occur throughout life and to contribute to obesity, yet very little is known about the molecular circuits that control preadipocyte expansion. The soluble form of the preadipocyte factor (also known as pref-1) delta-like 1 homolog (DLK1S) is known to inhibit adipogenic differentiation; however, the impact of DLK1 isoforms on preadipocyte proliferation remains to be determined. We generated preadipocytes with different levels of DLK1 and examined differentially affected gene pathways, which were functionally tested in vitro and confirmed in vivo. Here, we demonstrate for the first time that only membrane-bound DLK1 (DLK1M) exhibits a substantial repression effect on preadipocyte proliferation. Thus, by independently manipulating DLK1 isoform levels, we established that DLK1M inhibits G1-to-S-phase cell cycle progression and thereby strongly inhibits preadipocyte proliferation in vitro. Adult DLK1-null mice exhibit higher fat amounts than wild-type controls, and our in vivo analysis demonstrates that this may be explained by a marked increase in preadipocyte replication. Together, these data imply a major dual inhibitory function of DLK1 on adipogenesis, which places DLK1 as a master regulator of preadipocyte homeostasis, suggesting that DLK1 manipulation may open new avenues in obesity treatment.
Data-Driven Simulation of Detailed Surface Deformations for Surgery Training Simulators

Data-driven methods have received increasing attention in recent years in order to meet real-time requirements in computationally intensive tasks. In our current work we examine the application of such approaches in soft-tissue simulation. The core idea is to split deformations into a coarse approximation and a differential part that contains the details. We employ the data-driven stamping approach to enrich a fast simulation surface with details that have been extracted from a set of example deformations obtained in offline computations. In this paper we detail our technique, and suggest further extensions over our previous work. First, we propose an improved method for correlating the current coarse approximation to the examples in the database. The new correlation metric combines Euclidean distances with cosine similarity. It allows for better example discrimination, resulting in a well-conditioned linear system. This also enables us to use a non-negative least squares solver that leads to a better regression and guarantees positive stamp blending weights. Second, we suggest a frequency-space stamp compression scheme that saves memory and, in most instances, is faster, since many operations can be done in the compressed space. Third, cutting is included by employing a physically-inspired influence map that allows for proper handling of material discontinuities that were not present in the original examples. We thoroughly evaluate our method and demonstrate its practical application in a surgical simulator prototype.
Holding On to a Shifting Substrate: Plasticity of Egg Mass Tethers and Tethering Forces in Soft Sediment for an Intertidal Gastropod

Staying attached at a favorable site can be a major challenge for organisms in flow. Meeting this challenge depends on properties of the attachment structure and substrate, the nature of fluid flow, and the ability to adjust attachment force in response to hydrodynamic conditions. A broad taxonomic range of adult stages use adhesion or suction to attach to hard substrates in intertidal habitats, which experience flow from waves and tidal currents. We address the unique challenges of attachment to soft sediment in reproductive structures deposited on tidal flats. Egg masses of the opisthobranch mollusc Melanochlamys diomedea are anchored to the sediment by a buried tether composed of gel and sediment. In the field, populations differed in absolute tethering force and tethering force per unit size (= tenacity). Population differences in tenacity persisted for egg masses oviposited under common conditions in the laboratory. Adults exposed to greater flow produced tethers with greater tenacity but without an increase in tether size. Tethers tended to fail by slippage rather than breakage, indicating that tethering force depends more on frictional interaction with sediment than on strength of the tether axis. These results suggest that adults respond to variation in risks of embryo dislodgment by adjusting the tethering properties of egg masses, and that these adjustments involve more than simple changes in tether length or mass.
A Common Monitoring & Evaluation Framework Guided by the Collective Impact Model: Recommendations to Enhance the Tobacco Control Effort in Sub-Saharan Africa

Background: Tobacco use is one of the most ubiquitous causes of death and disability worldwide. In sub-Saharan Africa, despite the rising trend the use of tobacco in generally low among adults less than 10% in men and around 2% in women. As a result the region is viewed as being in the early stages of the four stage tobacco epidemic model. Projections suggest that the tobacco epidemic, if unchecked, can peak in Sub-Saharan Africa in the middle of this century. This offers the public health community an extraordinary opportunity – not only is the epidemic predicted so far in the future, there is knowledge on how to prevent it. The purpose of this study is to (a) research and assess case studies and theoretical frameworks used to guide global collaborative efforts in public health and development; (b) develop, administer, and summarize feedback collected from key stakeholders representing organizations critical in SSA tobacco control efforts; (c) analyze findings and identify gaps in the collective action; recommend opportunities to improve the systematic operations/capacity of all collaborating partners within SSA so that progress and collective impacts are maximized in the future. Methods: Secondary data was first sorted using a comparative, thematic approach to detect themes related to M&E practices at individual (organizational) level and at the group (collective) level. The sorted data was then analyzed using hypothesized content analysis for alignment of individual and group perceptions across the five components necessary for a collaborative effort to achieve a collective impact shared agenda, shared measurements, mutually reinforcing activities, on-going communications, and support organization. Results: Current practices of M&E are perceived as sub-optimal both at individual and group levels. Even though the secondary data was focused primarily on shared measurements, the mapping of individual and group level perceptions against the five components of collective impact indicates that attributes of the other four components were organically included in the discussion in varied depths. Analysis of perception indicates general willingness to adopt a common monitoring and evaluation framework. Conclusions: A common M&E framework remains a missing component of the collaborative effort striving to prevent the tobacco epidemic in sub-Saharan Africa. It is needed to learn from past successes and challenges and to inform strategy of current and future initiatives so that collaborating organizations are better able seize the unprecedented opportunity of preventing death and suffering from tobacco related illnesses in sub-Saharan Africa. It is important that such an M&E framework be thoughtfully conceptualized within the context of a common agenda, and supported by processes that facilitate mutually reinforcing activities and continuous communication among collaborators.
Reinforcement Learning in Conflicting Environments for Autonomous Vehicles

In this work, we investigate the application of Reinforcement Learning to two well known decision dilemmas, namely Newcomb's Problem and Prisoner's Dilemma. These problems are exemplary for dilemmas that autonomous agents are faced with when interacting with humans. Furthermore, we argue that a Newcomb-like formulation is more adequate in the human-machine interaction case and demonstrate empirically that the unmodified Reinforcement Learning algorithms end up with the well known maximum expected utility solution.
DA-IICT Submission for PDTB-styled Discourse Parser

The CONLL 2016 Shared task focusses on building a Shallow Discourse Parsing sys-tem, which is given a piece of newswire text as input and it returns all discourse relations in that text in the form of discourse connectives, its two arguments and the relation sense. We have built a parser for the same. We follow a pipeline architecture to build the system. We employ machine learning methods to train our classiﬁers for each component in the pipeline. The sys-tem achieves an overall F1 score of 0.1065 when tested on blind dataset provided by the task organisers. On the same dataset, for explicit relations, F1 score of 0.2067 is achieved, while for non explicit relations, an F1 score of 0.0112 is achieved.
Physical layer design for a broadband overlay system in the VHF band

In this paper, the physical layer design of the B-VHF system is described and discussed. B-VHF is a broadband communications system for air-traffic control based on multi-carrier technology which is intended to be applied as an overlay system to the existing aeronautical communications systems in the VHF band. Special focus in this paper is put on the physical layer design challenges which have to be solved in order to guarantee the co-existence between B-VHF and the legacy VHF systems. Especially, the interference avoidance techniques at the B-VHF transmitter as well as the required interference suppression techniques at the B-VHF receiver are discussed in detail.