Miracle-dz/UniFAR-FTUs huggingface dataset:
Leptons from dark matter annihilation in Milky Way subhalos

Numerical simulations of dark matter collapse and structure formation show that in addition to a large halo surrounding the baryonic component of our galaxy, there also exists a significant number of subhalos that extend hundreds of kiloparsecs beyond the edge of the observable Milky Way. We find that for dark matter (DM) annihilation models, galactic subhalos can significantly modify the spectrum of electrons and positrons as measured at our galactic position. Using data from the recent Via Lactea II simulation we include the subhalo contribution of electrons and positrons as boundary source terms for simulations of high energy cosmic ray propagation with a modified version of the publicly available GALPROP code. Focusing on the DM $\mathrm{DM}\ensuremath{\rightarrow}4e$ annihilation channel, we show that including subhalos leads to a better fit to both the Fermi and PAMELA data. The best fit gives a dark matter particle mass of 1.2 TeV, for boost factors of ${B}_{\mathrm{MH}}=90$ in the main halo and ${B}_{\mathrm{SH}}=1950\ensuremath{-}3800$ in the subhalos (depending on assumptions about the background), in contrast to the 0.85 TeV mass that gives the best fit in the main halo-only scenario. These fits suggest that at least a third of the observed electron cosmic rays from DM annihilation could come from subhalos, opening up the possibility of a relaxation of recent stringent constraints from inverse Compton gamma rays originating from the high-energy leptons.
Transitioning with confidence during contact/non-contact scenarios

In this work, we propose a probabilistic-based approach for detecting transitions in hybrid control systems with limited sensing. Detecting the transition moment is a particularly challenging problem as (i) multiple sources of sensory information are usually not available in robotic systems and (ii) the sensory information is noisy and requires calibrations. The challenge significantly increases if the robot makes physical contact as it causes discontinuities in the dynamics. The proposed transition criterion addresses these shortcomings by studying the behavior of the robot and the environment during a short horizon of time. We empirically validate our approach while detecting contact transitions in a hand-over scenario where a human operator brings a large object and hands it over to a pair of robotic arms which are not equipped with a force or tactile sensors.
Radiation Therapy for Orbital Mucosa-Associated Lymphoid Tissue Lymphoma : What is the Optimal Radiation Dose ?

Primary orbital lymphoma is a rare malignancy that accounts for only 1% of all lymphomas, and more than half of orbital lymphomas present histology of marginal zone B-cell lymphoma of mucosa-associated lymphoid tissue (MALT). MALT lymphoma is an indolent and slow growing disease that is very radiosensitive. Radiation therapy is thus the first choice of curative treatment for MALT lymphoma. The total dose of approximately 30 Gy in conventional fractions has been most frequently used for the treatment of MALT lymphoma, and has achieved an excellent local control rate of ≥ 95%. Recently, the total dose has been reduced to 24 Gy to 25 Gy in some institutions. Furthermore, low dose irradiation with 4 Gy in 2 fractions is also given, but the long-term efficacy is still unclear even though the initial response is good. In contrast, higher dose irradiation with >30 Gy is not recommendable, because there is no evidence that such higher doses enhance the efficacy of local control, and because higher doses can cause unacceptable toxicities, such as severe retinopathy.
PC-Based PCM Telemetry Data Reduction System Hardware

The Solar Energy Research Institute's (SERI) Wind Research Program is using pulse code modulation (PCM) telemetry systems to study horizontal-axis wind turbines. SERI has developed a low-cost PC-based PCM data acquisition system to facilitate quick PCM data analysis in the field. The SERI PC-PCM system consists of AT­ compatible hardware boards for decoding and combining PCM data streams and DOS software for control and management of data acquisition. Up to four boards can be installed in a single PC, providing the capability to combine data from four PCM streams direct to disk or memory. This paper describes the SERI PC-PCM system hardware, focusing on the practicality of · PC-based PCM data reduction. A related paper highlights our comprehensive PCM data management software program which can be used in conjunction with this hardware to provide full "quick-look" data processing and display. The PC-PCM hardware boards support a subset of the Inter-Range Instrumentation Group (IRIG) PCM standard, designed to synchronize and decommutate NRZ or Bi-Phase L PCM streams in the range of 1 to 800 Kbitsfsec at 8 to 12 bits per word and 2 to 64 words per frame. Multiple PCM streams (at various rates) can be combined and interleaved into a contiguous digital time series. Maximum data throughput depends on characteristics of the PC hardware, such as CPU rate and disk access speed.
A Survey Paper of Bellman-Ford Algorithm and Dijkstra Algorithm for Finding Shortest Path in GIS Application

GIS application is very useful in transportation management. A Road network analysis is an important function of GIS, and the shortest path analysis is the key issue of network analyses. In Most of GIS application, Dijkstra algorithm is useful. The functionality of Dijkstra's original algorithm can be extended with a variety of modifications. This paper represents the survey of two different algorithms dijkstra algorithm and bellman-ford algorithm. The purpose of the paper is to select one best algorithm from the two and. This algorithm can be used in government sector, emergency system, Business sector etc.
A Linguistically Based Approach to Discourse Relations Recognition

We present an unsupervised linguistically-based approach to discourse relations recognition, which uses publicly available resources like manually annotated corpora (Discourse Graphbank, Penn Discourse Treebank, RSTDT), as well as empirically derived data from “causally” annotated lexica like LCS, to produce a rule-based algorithm. In our approach we use the subdivision of Discourse Relations into four subsets – CONTRAST, CAUSE, CONDITION, ELABORATION, proposed by [7] in their paper, where they report results obtained with a machine-learning approach from a similar experiment, against which we compare our results. Our approach is fully symbolic and is partially derived from the system called GETARUNS, for text understanding, adapted to a specific task: recognition of Causality Relations in free text. We show that in order to achieve better accuracy, both in the general task and in the specific one, semantic information needs to be used besides syntactic structural information. Our approach outperforms results reported in previous papers [9].
Applying a real‐coded multi‐population genetic algorithm to multi‐reservoir operation

The primary objective of this study is to propose a real‐coded hypercubic distributed genetic algorithm (HDGA) for optimizing reservoir operation system. A conventional genetic algorithm (GA) is often trapped into local optimums during the optimization procedure. To prevent premature convergence and to obtain near‐global optimal solutions, the HDGA is designed to have various subpopulations that are processed using separate and parallel GAs. The hypercubic topology with a small diameter spreads good solutions rapidly throughout all of the subpopulations, and a migration mechanism, which exchanges chromosomes among the subpopulations, exchanges information during the joint optimization to maintain diversity and thus avoid a systematic premature convergence toward a single local optimum. Three genetic operators, i.e. linear ranking selection, blend‐α crossover and Gaussian mutation, are applied to search for the optimal reservoir releases. First, a benchmark problem, the four‐reservoir operation system, is considered to investigate the applicability and effectiveness of the proposed approach. The results show that the known global optimal solution can be effectively and stably achieved by the HDGA. The HDGA is then applied in the planning of a multi‐reservoir system in northern Taiwan, considering a water reservoir development scenario to the year 2021. The results searched by an HDGA minimize the water deficit of this reservoir system and provide much better performance than the conventional GA in terms of obtaining lower values of the objective function and avoiding local optimal solutions. Copyright © 2006 John Wiley & Sons, Ltd.
RE: Revision of manuscript number JAD 14-0315R1

Periodontal disease is a polymicrobial inflammatory disease that leads to chronic systemic inflammation and direct infiltration of bacteria/bacterial components, which may contribute to the development of Alzheimer’s disease. ApoE-/mice were orally infected (N = 12) with Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia and Fusobacterium nucleatum as monoand polymicrobial infections. ApoE-/mice were sacrificed following 12 and 24 weeks of chronic infection. Bacterial genomic DNA was isolated from all brain tissues except for the F. nucleatum mono-infected group. Polymerase chain reaction was performed using universal 16s rDNA primers and speciesspecific primer sets for each organism to determine whether the infecting pathogens accessed the brain. Sequencing amplification products confirmed the invasion of bacteria into the brain during infection. The innate immune responses were detected using antibodies against complement activation products of C3 convertase stage and the membrane attack complex. Molecular methods demonstrated that 6 out of 12 ApoE-/mice brains contained P. gingivalis genomic DNA at 12 weeks (P = 0.006), and 9 out of 12 at 24 weeks of infection (P = 0.0001). Microglia in both infected and control groups demonstrated strong intracellular labeling with C3 and C9, due to on-going biosynthesis. Tthe pyramidal neurons of the hippocampus in 4 out of 12 infected mice brains demonstrated characteristic opsonization with C3 activation fragments (P = 0.032). These results show that the oral pathogen P. gingivalis was able to access the ApoE-/mice brain and thereby contributed to complement activation with bystander neuronal injury.
A Novel Multi-objective Network Recommender Algorithm Based on PSO

This paper models the process of a recommender system as a multi-objective optimization problem, a discrete particle swarm optimization framework is established and has been integrated into multi-objective optimization, consequently, and a multi-objective discrete particle swarm optimization algorithm is proposed to solve the modeled optimization problem. Each run of the current mainstream recommender algorithms can only give one recommendation scheme, while the proposed algorithm can generate a set of schemes, and it provides the decision maker with more choices, a decision maker can make use of users' registration information to choose a personalized scheme to recommend to the user. In order to check the performance of the proposed algorithm, experiments on simulated and real-world data are tested and a comparison with the classical user based collaborative filtering recommendation method is made, experiments demonstrate the effectiveness of the proposed algorithm.
MLS and CALIOP Cloud Ice Measurements in the Upper Troposphere: A Constraint from Microwave on Cloud Microphysics

This study examines the consistency and microphysics assumptions among satellite ice water content (IWC) retrievals in the upper troposphere with collocated A-Train radiances from Microwave Limb Sounder (MLS) and lidar backscatters from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). For the cases in which IWC values are small (<10 mg m(-3)), the cloud ice retrievals are constrained by both MLS 240- and 640-GHz radiances and CALIOP 532-nm backscatter (532). From the observed relationships between MLS cloud-induced radiance T-cir and the CALIOP backscatter integrated (532) along the MLS line of sight, an empirical linear relation between cloud ice and the lidar backscatter is found: IWC/(532) = 0.58 +/- 0.11. This lidar cloud ice relation is required to satisfy the cloud ice emission signals simultaneously observed at microwave frequencies, in which ice permittivity is relatively well known. This empirical relationship also produces IWC values that agree well with the CALIOP, version 3.0, retrieval at values <10 mg m(-3). Because the microphysics assumption is critical in satellite cloud ice retrievals, the agreement found in the IWC-(532) relationships increase fidelity of the assumptions used by the lidar and microwave techniques for upper-tropospheric clouds.
Design of an ozone and nitrogen dioxide sensor unit and its long-term operation within a sensor network in the city of Zurich

Abstract. This study focuses on the investigation and quantification of low-cost sensor performance in application fields such as the extension of traditional air quality monitoring networks or the replacement of diffusion tubes. For this, sensor units consisting of two boxes featuring NO2 and O3 low-cost sensors and wireless data transfer were engineered. The sensor units were initially operated at air quality monitoring sites for 3 months for performance analysis and initial calibration. Afterwards, they were relocated and operated within a sensor network consisting of six locations for more than 1 year. Our analyses show that the employed O3 and NO2 sensors can be accurate to 2–5 and 5–7 ppb, respectively, during the first 3 months of operation. This accuracy, however, could not be maintained during their operation within the sensor network related to changes in sensor behaviour. For most of the O3 sensors a decrease in sensitivity was encountered over time, clearly impacting the data quality. The NO2 low-cost sensors in our configuration exhibited better performance but did not reach the accuracy level of NO2 diffusion tubes (∼ 2 ppb for uncorrected 14-day average concentrations). Tests in the laboratory revealed that changes in relative humidity can impact the signal of the employed NO2 sensors similarly to changes in ambient NO2 concentration. All the employed low-cost sensors need to be individually calibrated. Best performance of NO2 sensors is achieved when the calibration models also include time-dependent parameters accounting for changes in sensor response over time. Accordingly, an effective procedure for continuous data control and correction is essential for obtaining meaningful data. It is demonstrated that linking the measurements from low-cost sensors to the high-quality measurements from routine air quality monitoring stations is an effective procedure for both tasks provided that time periods can be identified when pollutant concentrations can be accurately predicted at sensor locations.
Prognostic Role of Epithelial-Mesenchymal Transition ( EMT ) Markers SNAIL-1 & Eph A 2 Expressions in Breast Carcinoma

Background: The breast cancer is one of the commonest cancers worldwide and the commonest malignancy of females in Egypt. The understanding of the molecular pathways of its carcinogenesis is important to discover recent therapeutic targets. Epithelialmesenchymal transition (EMT) is a recent explanation of cancer progression, metastases and the resistance to chemotherapy in breast cancer. EMT process is controlled by many pathways and markers and understanding their functions, role in metastases is needed to modify the treatment strategies for breast cancer patients. A recently discovered EMT-inducing transcription factors is snail-1, that was involved in cell proliferation, apoptosis, metastasis, drugs resistance, and several aspects of the carcinogenic process. EphA2 protein is an essential component of RTK/Ras-signaling that are involved in EMT and cooperates with Wnt/b-catenin-pathway. Aim of the work: was to evaluate EMT markers Snail1 & Eph A2 expressions in breast cancer patients correlating their expressions with clinicopathological parameters and patients outcome. Methods: EMT markers Snail-1&Eph A2 expressions were evaluated in sections from 60 paraffin blocks of breast cancer using immunohistochemistry. We analyzed correlations between the levels of their expressions, clinical, pathological parameters of the patients and patient’s outcome. Results: Snail1 over expression and Eph A2 positive expression in breast cancer was associated with higher grade (<0.03 for both markers), higher stage of the tumor, presence of lymph node metastases, distant metastasis, high KI67 labeling index, negative ER& PR hormonal receptors, high Her2 neu expression and aggressive molecular subtype like triple negative, luminal B and her2 neu amplified (p<0.001 for all of them). The expression of both markers was significantly positively correlated with each other (p<0.001). The 3-year overall survival rate was 86.5% with a mean of 35.2 ± 0.22 months (95% CI; 35.2 – 36.1 months). There was a significant correlation between the progression and Snail-1 (P<0.02) and there were highly significant correlations between combination of both markers expression and overall survival and progression free survival (P<0.001). Conclusion: Snail1&Eph A2 are markers of poor prognosis of breast cancer patients with an impact on survival.
Learning Vector Symbolic Architectures for Reactive Robot Behaviours

Vector Symbolic Architectures (VSA) combine a hypervector space and a set of operations on these vectors. Hypervectors provide powerful and noise-robust representations and VSAs are associated with promising theoretical properties for approaching high-level cognitive tasks. However, a major drawback of VSAs is the lack of opportunities to learn them from training data. Their power is merely an effect of good (and elaborate) design rather than learning. We exploit highlevel knowledge about the structure of reactive robot problems to learn a VSA based on training data. We demonstrate preliminary results on a simple navigation task. Given a successful demonstration of a navigation run by pairs of sensor input and actuator output, the system learns a single hypervector that encodes this reactive behaviour. When executing (and combining) such VSA-based behaviours, the advantages of hypervectors (i.e. the representational power and robustness to noise) are preserved. Moreover, a particular beauty of this approach is that it can learn encodings for behaviours that have exactly the same form (a hypervector) no matter how complex the sensor input or the behaviours are.
Personalized Recommendations in Police Photo Lineup Assembling Task

In this paper, we aim to present a novel application domain for recommender systems: police photo lineups. Photo lineups play a significant role in the eyewitness identification prosecution and subsequent conviction of suspects. Unfortunately, there are many cases where lineups have led to the conviction of an innocent persons. One of the key factors contributing to the incorrect identification is unfairly assembled (biased) lineups, i.e. that the suspect differs significantly from all other candidates. Although the process of assembling fair lineup is both highly important and time-consuming, only a handful of tools are available to simplify the task. We describe our work towards using recommender systems for the photo lineup assembling task. Initially, two non-personalized recommending methods were evaluated: one based on the visual descriptors of persons and the other their content-based attributes. Next, some personalized hybrid techniques combining both methods based on the feedback from forensic technicians were evaluated. Some of the personalized techniques significantly improved the results of both non-personalized techniques w.r. t. nDCG and recall@top-k.
Unsupervised Metric Learning Using Low Dimensional Embedding

Unsupervised metric learning has been generally studied as a byproduct of dimensionality reduction or manifold learning techniques. Manifold learning techniques like Diusion maps, Laplacian eigenmaps has a special property that embedded space is Euclidean. Although laplacian eigenmaps can provide us with some (dis)similarity information it does not provide with a metric which can further be used on out-of-sample data. On other hand supervised metric learning technique like ITML which can learn a metric needs labeled data for learning. In this work propose methods for incremental unsupervised metric learning. In rst approach Laplacian eigenmaps is used along with Information Theoretic Metric Learning(ITML) to form an unsupervised metric learning method. We rst project data into a low dimensional manifold using Laplacian eigenmaps, in embedded space we use euclidean distance to get an idea of similarity between points. If euclidean distance between points in embedded space is below a threshold t1 value we consider them as similar points and if it is greater than a certain threshold t2 we consider them as dissimilar points. Using this we collect a batch of similar and dissimilar points which are then used as a constraints for ITML algorithm and learn a metric. To prove this concept we have tested our approach on various UCI machine learning datasets. In second approach we propose Incremental Diusion Maps by updating SVD in a batch-wise manner.
First specimen of the white sturgeon (Acipenser transmontanus Richardson, 1836) in coastal waters of Mexico with data on its genetic identity

The chondrostean fishes of the family Acipenseridae are composed of four genera (Acipenser, Pseudoscaphirhynchus, Scaphirhynchus, and Huso) and almost 25 extant species in the marine and continental waters of the world (Birstein 1993, Nelson et al. 2006). The genus Acipenser is the most diverse taxon within the family, with 17 valid, extant species (Birstein 1993, Birstein and Bemis 1997), of which eight occur in waters of North America (Vladykov and Greeley 1963, Nelson et al. 2004). All species of chondrosteans face problems of conservation due to overfishing and destruction of habitat (Birstein and Bemis 1997), including those in North America (Jelks et al. 2008). Along the Pacific Coast of North America, the white sturgeon (Acipenser transmontanus) and the green sturgeon (A. medirostris) reach their respective southernmost ranges in northwestern Baja California, Mexico (Love et al.1995, Rosales-Casian and AlmedaJauregui 2009). The white sturgeon is known to occur from Northern Gulf of Alaska (Mecklenburg et al. 2002) to Ensenada, Baja California (Hubbs 1967, Miller and Lea 1972). This anadromous chondrostean is commonly found in estuaries and the sea as deep as 122 First specimen of the white sturgeon (Acipenser transmontanus Richardson, 1836) in coastal waters of Mexico with data on its genetic identity
A study of the characteristics of white noise using the empirical mode decomposition method

Based on numerical experiments on white noise using the empirical mode decomposition (EMD) method, we find empirically that the EMD is effectively a dyadic filter, the intrinsic mode function (IMF) components are all normally distributed, and the Fourier spectra of the IMF components are all identical and cover the same area on a semi–logarithmic period scale. Expanding from these empirical findings, we further deduce that the product of the energy density of IMF and its corresponding averaged period is a constant, and that the energy–density function is chi–squared distributed. Furthermore, we derive the energy–density spread function of the IMF components. Through these results, we establish a method of assigning statistical significance of information content for IMF components from any noisy data. Southern Oscillation Index data are used to illustrate the methodology developed here.