Miracle-dz/UniFAR-FTUs huggingface dataset:
Renormalization of Dirac's Polarized Vacuum

We review recent results on a mean-field model for relativistic electrons in atoms and molecules, which allows to describe at the same time the self-consistent behavior of the polarized Dirac sea. We quickly derive this model from Quantum Electrodynamics and state the existence of solutions, imposing an ultraviolet cut-off $\Lambda$. We then discuss the limit $\Lambda\to\infty$ in detail, by resorting to charge renormalization.
Design and comparative evaluation of a BCI-based upper extremity robotic rehabilitation protocol

We advocate online modification of robot-assisted task speed, based on continuously inferred motor imagery as an effective rehabilitation protocol for increasing the involvement levels of the patients in physical rehabilitation exercises. To study efficacy of such Brain-Computer Interface (BCI) based physical rehabilitation protocols, we conduct human subject experiments on healthy volunteers, comparing several BCI-based protocols with haptic and visual feedback with each other and with conventional robot-assisted rehabilitation protocols, in terms of intensity and sustainability of motor imagery. Our results provide evidence that the online adjusted BCI-based robotic protocol helps subjects produce stronger and more sustained motor imagery throughout the motor task, compared to other BCI-based protocols. We also show that BCI-assisted robotic therapy can enable a level of motor cortical activity that is similar to a scenario in which the subjects could actually execute the motion. These results suggest that BCI-assisted rehabilitation methods that provide online modification of the task speed based on continuously inferred motor imagery have potential in increasing the level of involvement of patients during exercises and may lead to more effective recovery.
Structured dirty-paper coding using low-density lattices

This paper studies dirty-paper coding in a Gaussian broadcast channel with two receivers. It finds that an approximate version of dirty-paper coding using low-density lattices can be implemented with a complexity that is polynomial-time on average in the block length. The main difference between this paper and prior work is that a non-binary LDPC-based lattice codebook is used for each user, and one codebook is aligned with the other. The low-density nature enables tractable encoding and decoding algorithms, and the alignment gives structure to the overall signal transmitted and it enables us to perform the encoding and decoding efficiently.1
Prohibitin Attenuates Oxidative Stress and Extracellular Matrix Accumulation in Renal Interstitial Fibrosis Disease

Prohibitin is an evolutionary conserved and pleiotropic protein that has been implicated in various cellular functions, including proliferation, tumour suppression, apoptosis, transcription, and mitochondrial protein folding. Both prohibitin over- and under-expression have been implicated in various diseases and cell types. We recently demonstrated that prohibitin down-regulation results in increased renal interstitial fibrosis (RIF). Here we investigated the role of oxidative stress and prohibitin expression in RIF in unilateral ureteral obstructed rats. Lentivirus-based delivery vectors were used to knockdown or over-express prohibitin. Our results show that increased prohibitin expression was negatively correlated with the RIF index, reactive oxygen species, malon dialdehyde, transforming growth factor β1, collagen IV, fibronectin, and cell apoptosis index. In conclusion, we postulate that prohibitin acts as a positive regulator of mechanisms that counteract oxidative stress and extracellular matrix accumulation and therefore has an antioxidative effect.
Bulk and intra-tooth enamel stable isotopes of waterbuck kobus ellipsiprymnus from Queen Elizabeth National Park, Uganda

Bulk and intra-tooth enamel stable isotopes of waterbuck Kobus ellipsiprymnus from Queen Elizabeth National Park, Uganda Sandi R. Copeland*, Matt Sponheimer, Clive A. Spinage and Julia A. Lee-Thorp Max Planck Institute for Evolutionary Anthropology, Department of Human Evolution, Deutscher Platz 6, D-04103, Leipzig, Germany, Department of Anthropology, University of Colorado at Boulder, 233 UCB, Boulder, CO 80309, U.S.A. , Wickwood House, Stanford Road, Faringdon, Oxon SN7 8EZ, U.K. and Department of Archaeological Sciences, University of Bradford, Bradford BD1 7DP, U.K.
Lessons Learned from Disaster Management

The DEFACTO system is a multiagent based tool for training incident commanders of large scale disasters. In this paper, we highlight some of the lessons that we have learned from our interaction with the Los Angeles Fire Department (LAFD) and how they have affected the way that we continued the design of our disaster management training system. These lessons were gleaned from LAFD feedback and initial training exercises and they include: system design, visualization, improving trainee situational awareness, adjusting training level of difficulty and situation scale. We have taken these lessons and used them to improve the DEFACTO system’s training capabilities. We have conducted initial training exercises to illustrate the utility of the system in terms of providing useful feedback to the trainee.
AN ARTIFICIAL IMMUNE ALGORITHM FOR SPECTRUM RECOGNITION OF HYPERSPECTRAL DATA

An intelligent approach based on artificial immune systems (AIS) is proposed in this paper to perform the task of spectrum recognition in hyperspectral data analysis. Although traditional spectral matching techniques have provided some confirmatory information to aid the interpretation of hyperspectral data, the improvement is yet to be made because of the complexity of the spectra. The immunological algorithm for spectral reactions is described in which a population of memory cells for each of the possible laboratory-derived spectral is evolved using artificial immune operators, such as, clone, mutation, and selection. In specially, the clonal and the mutation operators are two foremost processes. The clonal process can draw the evolutionary process closer to the goal. It raises the average affinity value and gives the following steps a good change to further move towards the solution, i.e. the known spectra. The mutation step generates random changes of single features to the individual solutions and helps the proposed algorithm to avoid local optimal value. By the above training process, a small well-trained specialist library is established for testing their pattern recognition ability. The recognition in the proposed algorithm is the automatic process to find all possible spectral responsible for the observed spectrum, analogous to the antibody’s recognizing antigen in the natural immune system. Whenever a spectrum is recognized for the first time, a copy of it is reserved as a new memory cell for the spectrum. Therefore, when it appears a second time, it can be easily recognized by the antibodies created during its first appearance. Consequently, the proposed method provides a learning methodology for pattern recognition. The proposed algorithm is compared with two well known spectral matching algorithms: binary coding and spectral angle mapper algorithms using simulated and real hyperspectral data. Experimental results demonstrate that the proposed approach can better recognize the unknown spectra than traditional algorithms based on a wellestablished specialist library obtained by different immune operators, and hence provide an effective option for spectrum recognition of hyperspectral data. * Corresponding author.
Fractal features of dark, maintained, and driven neural discharges in the cat visual system.

We employ a number of statistical measures to characterize neural discharge activity in cat retinal ganglion cells (RGCs) and in their target lateral geniculate nucleus (LGN) neurons under various stimulus conditions, and we develop a new measure to examine correlations in fractal activity between spike-train pairs. In the absence of stimulation (i.e., in the dark), RGC and LGN discharges exhibit similar properties. The presentation of a constant, uniform luminance to the eye reduces the fractal fluctuations in the RGC maintained discharge but enhances them in the target LGN discharge, so that neural activities in the pair cease to be mirror images of each other. A drifting-grating stimulus yields RGC and LGN driven spike trains similar in character to those observed in the maintained discharge, with two notable distinctions: action potentials are reorganized along the time axis so that they occur only during certain phases of the stimulus waveform, and fractal activity is suppressed. Under both uniform-luminance and drifting-grating stimulus conditions (but not in the dark), the discharges of pairs of LGN cells are highly correlated over long time scales; in contrast discharges of RGCs are nearly uncorrelated with each other. This indicates that action-potential activity at the LGN is subject to a common fractal modulation to which the RGCs are not subjected.
Visual attention guided images abstraction using sparse coding

Given the increasing number of mobile platforms, a key technical challenge is how to provide efficient image processing application on resource-limited mobile devices. This paper proposes a novel technique for mobile image abstraction on mobile platform. In order to reduce the computation complexity, the image abstraction process is conducted on a cloud computing system. In this technique, captured images are analyzed to detect visual salient area, which is then provided for adaptive detail preservation of abstraction. To further reduce the image size for efficient transmission between mobile device and cloud computing system while maintaining the visual quality of abstracted image, the derived algorithm regards the salient regions as the dictionary in sparse representation, and selects the salient regions that minimize the residual output error iteratively, thus the resulting regions have a direct correspondence to the performance requirements of the given problem. Experimental results obtained using extensive datasets captured under uncontrolled conditions show the efficacy of the proposed system for conducting image abstraction on mobile platform using sparse representation.
IMPLEMENTATION OF METHODS OF COMPUTATIONAL INTELLIGENCE

Nonlinear processes are difficult to control because there can be so many variations of the nonlinear behavior. Traditionally, a nonlinear process has to be linearized first before an automatic controller can be effectively applied. This is typically achieved by adding a reverse nonlinear function to compensate for the nonlinear behavior so the overall process input-output relationship becomes somewhat linear. The adaptive systems are best handled with methods of computational intelligence such as neural networks and fuzzy systems. This presentation will focus on several methods of developing close to optimal architectures and on finding efficient learning algorithms. The problem becomes even more complex if the methods of computational intelligence have to be implemented in hardware. Various practical solutions will be presented and compared.
Estimation of Target Location Via Likelihood Approximation in Sensor Networks

A fully decentralized sensor network, without fusion center, is deployed to estimate the position of a target. Taking advantage of the limited communication range of the nodes, and exploiting their (unknown) location inside the surveyed area, the likelihood profile is approximately reconstructed. A distributed ML-like estimator is, therefore, proposed and its asymptotic performance is investigated analytically, while computer experiments assess the behavior of the estimator in nonasymptotic regimes. The differences between one- and two-dimensional scenarios are also discussed.
Grounded theory of productive practices for algebraic thinking

ABSTRACT In response to students’ difficulties with algebra, many have advocated for algebraic thinking in elementary schools as a route to algebra. However, many studies show students’ difficulties with algebraic thinking and teachers’ challenges with creating a context that supports reasoning. This article presents a constructivist-grounded theory of interdependent practices that supports algebraic thinking based on a longitudinal study. The theory emerged from data collected from an after-school mathematical reasoning project with six elementary schools.
Sequencing of the smallest Apicomplexan genome from the human pathogen Babesia microti

We have sequenced the genome of the emerging human pathogen Babesia microti and compared it with that of other protozoa. B. microti has the smallest nuclear genome among all Apicomplexan parasites sequenced to date with three chromosomes encoding ∼3500 polypeptides, several of which are species specific. Genome-wide phylogenetic analyses indicate that B. microti is significantly distant from all species of Babesidae and Theileridae and defines a new clade in the phylum Apicomplexa. Furthermore, unlike all other Apicomplexa, its mitochondrial genome is circular. Genome-scale reconstruction of functional networks revealed that B. microti has the minimal metabolic requirement for intraerythrocytic protozoan parasitism. B. microti multigene families differ from those of other protozoa in both the copy number and organization. Two lateral transfer events with significant metabolic implications occurred during the evolution of this parasite. The genomic sequencing of B. microti identified several targets suitable for the development of diagnostic assays and novel therapies for human babesiosis.
Indian sign language recognition: An approach based on fuzzy-symbolic data

In this paper, the task of recognizing signs made by hearing impaired people at sentence level has been addressed. A novel method of detecting sign boundaries in a video of continuous signs has been proposed and extraction of spatial features to capture hand movements of a signer through fuzzy membership functions has been proposed. Frames of a given video of a sign are preprocessed to extract face and hand components of a signer. The centroids of the extracted components are exploited to extract spatial features. The concept of interval valued type symbolic data has been explored to capture variations in the same sign made by the different signers at different instances of time. A suitable symbolic similarity measure is studied to establish matching between reference and test signs and a simple nearest neighbor classifier is used to recognize an unknown sign as one among the known signs by specifying a desired level of threshold. An extensive experimentation is conducted on a significantly large corpus of Indian regional signs created by us during the course of our research work in order to evaluate the performance of the proposed system.
Visual classification: an interactive approach to decision tree construction

Satisfying the basic requirements of accuracy and understandability of a classifier, decision tree classifiers have become very popular. Instead of constructing the decision tree by a so F histicated al orithm, in eractive met od K we introduce a fully based on a multidimensional visualization techni interaction capabilities. T R ue and appropriate us domam knowledge of an expert can be prof&ably included in the tree construction phase. Furthermore, after the interactive construction of a decision tree the user has a much deeper understandin o$ the data than just knowm f the decision f ree generated by an arbitrary a active a gorithm. The interof most ii preach also overcomes the limitation ecision trees which are fixed to binary s a low to backtrack in the tree construction Ii lits for numeric attributes and which do not phase. Our performance evaluation with several well-known datasets demonstrates that even users with no a priori knowledge of the data construct a decision tree with an accuracy similar to the tree algorithms. enerated by state of the art Addi tonally, visual interactive F classification significant1 reduces the tree size and improves the un erstandibility of the B resulting decision tree.
Swoogle: Searching for Knowledge on the Semantic Web

Swoogle is an implemented system that discovers, analyzes and indexes knowledge encoded in semantic web documents on the Web. Swoogle reasons about these documents and their constituent parts (e.g., terms and triples) and records meaningful metadata about them. Swoogle provides webscale semantic web data access service, which helps human users and software systems to find relevant documents, terms and triples, via its search and navigation services. Swoogle also provides a customizable algorithm inspired by Google's PageRank algorithm but adapted to the semantics and use patterns found in semantic web documents.
Infrared behavior of the gluon propagator on a large volume lattice

The first calculation of the gluon propagator using an $\mathcal{O}{(a}^{2})$ improved action with the corresponding $\mathcal{O}{(a}^{2})$ improved Landau gauge fixing condition is presented. The gluon propagator obtained from the improved action and improved Landau gauge condition is compared with earlier unimproved results on similar physical lattice volumes of ${3.2}^{3}\ifmmode\times\else\texttimes\fi{}6.4{\mathrm{fm}}^{4}. $ We find agreement between the improved propagator calculated on a coarse lattice with lattice spacing $a=0.35$ fm and the unimproved propagator calculated on a fine lattice with spacing $a=0.10$ fm. This motivates us to calculate the gluon propagator on a coarse large-volume lattice ${5.6}^{3}\ifmmode\times\else\texttimes\fi{}11.2{\mathrm{fm}}^{4}. $ The infrared behavior of previous studies is confirmed in this work. The gluon propagator is enhanced at intermediate momenta and suppressed at infrared momenta. Therefore the observed infrared suppression of the Landau gauge gluon propagator is not a finite volume effect.
Radius aware probabilistic testing of deadlocks with guarantees

Concurrency bugs only occur under certain interleaving. Existing randomized techniques are usually ineffective. PCT innovatively generates scheduling, before executing a program, based on priorities and priority change points. Hence, it provides a probabilistic guarantee to trigger concurrency bugs. PCT randomly selects priority change points among all events, which might be effective for non-deadlock concurrency bugs. However, deadlocks usually involve two or more threads and locks, and require more ordering constraints to be triggered. We interestingly observe that, every two events of a deadlock usually occur within a short range. We generally formulate this range as the bug Radius, to denote the max distance of every two events of a concurrency bug. Based on the bug radius, we propose RPro (Radius aware Probabilistic testing) for triggering deadlocks. Unlike PCT, RPro selects priority change points within the radius of the targeted deadlocks but not among all events. Hence, it guarantees larger probabilities to trigger deadlocks. We have implemented RPro and PCT and evaluated them on a set of real-world benchmarks containing 10 unique deadlocks. The experimental results show that RPro triggered all deadlocks with higher probabilities (i.e., >7.7x times larger on average) than that by PCT. We also evaluated RPro with radius varying from 1 to 150 (or 300). The result shows that the radius of a deadlock is much smaller (i.e., from 2 to 114 in our experiment) than the number of all events. This further confirms our observation and makes RPro meaningful in practice.
Operational parameters of an opto-electronic neural network employing fixed planar holographic interconnects

A prototype neutral network system of multifaceted, planar interconnection holograms and opto-electronic neurons is analyzed. This analysis shows that a hologram fabricated with electron-beam lithography has the capacity to connect 6700 neuron outputs to 6700 neuron inputs and that the encoded synaptic weights have a precision of approximately 5 bits. Higher interconnection densities can be achieved by accepting a lower synaptic weight accuracy. For systems employing laser diodes at the outputs of the neurons, processing rates in the range of 45 to 720 trillion connections per second can potentially be achieve.
Personalised Travel Recommendation based on Location Co-occurrence

We propose a new task of recommending touristic locations based on a user’s visiting history in a geographically remote region. This can be used to plan a touristic visit to a new city or country, or by travel agencies to provide personalis ed travel deals. A set of geotags is used to compute a location similarity model between two different regions. The similarity between two landmarks is derived from the number of users that have visited both places, using a Gaussian density estimation of the co-occurrence space of location visits to cluster related geotags. The standard deviation o f the kernel can be used as a scale parameter that determines the size of the recommended landmarks. A personalised recommendation based on the location similarity model is evaluated on city and country scale and is able to outperform a location ranking based on popularity. Especially when a tourist filter based on visit duration is en forced, the prediction can be accurately adapted to the preference of the user. An extensive evaluation based on manual annotations shows that more strict ranking methods like cosine similarity and a proposed RankDiff algorithm provide more serendipitous recommendations and are able to link similar locations on opposite sides of the world.
A hierarchical multiwavelet based stereo correspondence matching technique

This paper presents a hierarchical stereo correspondence matching technique based on multiwavelet transforms. A global error energy minimization technique is employed to generate a disparity map for each of the four multiwavelet approximation subband pairs. The information in the four disparity maps is then combined using a Fuzzy algorithm to generate a single disparity map. This initial disparity map is estimated at the lowest resolution and needs to be progressively passed on to higher resolution levels. Hence, the search at higher resolution levels is significantly reduced, thereby reducing the computational cost of the overall process and improving the reliability of the final disparity map. Results show that the proposed technique produces a smoother disparity map with less mismatch errors compared to applying the same method in both spatial and wavelet domains. The proposed algorithm fares very well when compared to other state of art techniques from the Middlebury database.