Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploratory Data Analysis by the Self-Organizing Map: Structures of Welfare and Poverty in the World

The self organizing map SOM is a method that represents statistical data sets in an ordered fashion as a natural groundwork on which the distributions of the individual indicators in the set can be displayed and analyzed As a case study that instructs how to use the SOM to compare states of economic systems the standard of living of dif ferent countries is analyzed using the SOM Based on a great number of welfare indicators the SOM illustrates rather re ned relation ships between the countries two dimensionally This method is directly applicable to the nancial grading of companies too
Protecting against evaluation overfitting in empirical reinforcement learning

Empirical evaluations play an important role in machine learning. However, the usefulness of any evaluation depends on the empirical methodology employed. Designing good empirical methodologies is difficult in part because agents can overfit test evaluations and thereby obtain misleadingly high scores. We argue that reinforcement learning is particularly vulnerable to environment overfitting and propose as a remedy generalized methodologies, in which evaluations are based on multiple environments sampled from a distribution. In addition, we consider how to summarize performance when scores from different environments may not have commensurate values. Finally, we present proof-of-concept results demonstrating how these methodologies can validate an intuitively useful range-adaptive tile coding method.
On identifying the causative network of an epidemic

The history of infections and epidemics holds famous examples where understanding, containing and ultimately treating an outbreak began with understanding its mode of spread. The key question then, is: which network of interactions is the main cause of the spread? And can we determine the causative network without any knowledge of the epidemic, other than the identify of a minuscule subsample of infected nodes? This comes down to understanding the diagnostic power of network information. Specifically, in this paper we consider an epidemic that spreads on one of two networks. At some point in time, we see a small random subsample (perhaps a vanishingly small fraction) of those infected. We derive sufficient conditions two networks must have for this problem to be identifiable. We provide an efficient algorithm that solves the hypothesis testing problem on such graphs, and we characterize a regime in which our algorithm succeeds. Finally, we show that the condition we need for this identifiability property is fairly mild, and in particular, is satisfied by two common graph topologies: the grid, and the Erdös-Renyi graphs.
Efficacy and toxicity of concurrent chemoradiotherapy with nedaplatin and S-1 for head and neck cancer.

OBJECTIVE We investigated the efficacy and toxicity of concurrent chemoradiotherapy with nedaplatin and S-1 for head and neck cancer, as an alternative to cisplatin and 5-fluorouracil. METHODS A total of 31 patients were enrolled in this study. S-1 was administered orally twice a day for 14 days followed by a 2-week rest. Nedaplatin was intravenously administered on day 4. If possible, two courses of chemotherapy were performed. The radiotherapy was started concurrently with the administration of S-1. RESULTS The overall complete response rate was 81%. The 2-year overall survival rate was 96%. The 2-year relapse-free survival rate was 94%. The main adverse events were hematological toxicity, mucositis and dermatitis. CONCLUSIONS Our findings suggest that this therapeutic regimen has either an equal or lower toxicity than the conventional cisplatin and 5-fluorouracil, and that it has equal efficacy with regard to the clinical response and short-term outcome. Moreover, it is possible to successfully perform this treatment in an outpatient setting.
Efficient Processing of Large RDF Streams Using Memory Management Algorithms

As more RDF streaming applications are being developed, there is a growing need for an efficient mechanism for storing and performing inference over these streams. In this poster, we present a tool that stores these streams in a unified model by combining memory and disk based mechanisms. We explore various memory management algorithms and disk-persistence strategies to optimize query performance. Our unified model produces an optimized query execution and inference performance for RDF streams that benefit from the advantages of using both, memory and disk.
On the Visualization of Social and other Scale-Free Networks

This paper proposes novel methods for visualizing specifically the large power-law graphs that arise in sociology and the sciences. In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g. cliques) to more clearly reveal the networkpsilas underlying connection pathways. This simplification approach deterministically filters (instead of clustering) the graph to retain important node and edge semantics, and works both automatically and interactively. The improved graph filtering and layout is combined with a novel computer graphics anisotropic shading of the dense crisscrossing array of edges to yield a full social network and scale-free graph visualization system. Both quantitative analysis and visual results demonstrate the effectiveness of this approach.
Money Walks: Implicit Mobility Behavior and Financial Well-Being

Traditional financial decision systems (e.g. credit) had to rely on explicit individual traits like age, gender, job type, and marital status, while being oblivious to spatio-temporal mobility or the habits of the individual involved. Emerging trends in geo-aware and mobile payment systems, and the resulting “big data,” present an opportunity to study human consumption patterns across space and time. Taking inspiration from animal behavior studies that have reported significant interconnections between animal spatio-temporal “foraging” behavior and their life outcomes, we analyzed a corpus of hundreds of thousands of human economic transactions and found that financial outcomes for individuals are intricately linked with their spatio-temporal traits like exploration, engagement, and elasticity. Such features yield models that are 30% to 49% better at predicting future financial difficulties than the comparable demographic models.
Citywide mobile internet access using dense urban WiFi coverage

We investigate if it is feasible to use the WiFi coverage in urban areas for mobile Internet access and which type of applications can benefit from the Internet access provided by the already deployed WiFi Access Points (APs). Nowadays, most smartphones and other mobile handsets are WiFi-enabled. Moreover, mobile Internet data traffic is expected to grow significantly in the next few years. WiFi access is an interesting alternative to cellular networks, as it is a widespread wireless technology, provides high data rates and has a low deployment cost. In this paper, we analyze the characteristics of WiFi coverage and connectivity of mobile users using different mobility speeds and varying some AP settings. These results allow us to analyze different applications that can be supported and the challenges to face to create a citywide WiFi network given the current infrastructure composed of residential WiFi APs and hotspots.
Performance Evaluation of a Non-Blocking Multithreaded Architecture for Embedded, Real-Time and DSP Applications

This paper presents the evaluation of a non-blocking, decoupled memory/execution, multithreaded architecture known as the Scheduled Dataflow (SDF). Our research explores a simple, yet powerful execution paradigm that is based on non-blocking threads, and decoupling of memory accesses from execution pipeline. This paper compares the execution cycles required for programs on SDF with the execution cycles required by programs on Superscalar and VLIW architectures.
Rivaroxaban vs dabigatran for thromboprophylaxis after joint-replacement surgery: exploratory indirect comparison based on meta-analysis of pivotal clinical trials.

AIM To indirectly compare rivaroxaban and dabigatran for prevention of venous thromboembolism (VTE) after total hip or knee arthroplasty (THA, TKA) based on their pivotal efficacy/safety trials embracing a total of 20618 patients. METHODS Pooled risk differences (RD) for rivaroxaban vs enoxaparin and dabigatran vs enoxaparin obtained from separate meta-analyses of two sets of trials were used to indirectly estimate RDs for rivaroxaban vs dabigatran. RESULTS Primary efficacy (any VTE+all-cause mortality) and safety (major bleeding) outcomes in enoxaparin arms largely differed across similarly designed rivaroxaban and dabigatran trials (differences in venography adjudication and bleeding events definitions). However, incidence of symptomatic VTE and incidence of major/non-major clinically relevant bleeding (including surgical site) were consistent in this respect. RDs (as percentages) for symptomatic VTE were: rivaroxaban-enoxaparin=-0.4% (95% confidence interval [CI], -0.9 to 0.05); dabigatran-enoxaparin=-0.09% (95% CI, -1.0 to 0.8); rivaroxaban-dabigatran=-0.3% (95% CI, -1.3 to 0.7; P=0.275). RDs for major/clinically relevant bleeding were rivaroxaban-enoxaparin=0.99% (95%CI, 0.29 to 1.69); dabigatran-enoxaparin=0.02% (95% CI, -1.0 to 1.0); rivaroxaban-dabigatran=0.97 (95% CI, -0.43 to 2.37; P=0.085). Mortality rates (all-cause, VTE-related, bleeding-related) were very low not indicating differences between any two of the three treatments. CONCLUSION Methodological differences disable indirect comparisons of rivaroxaban vs dabigatran that would be based on major efficacy/safety outcomes of their pivotal trials. The two drugs do not seem to differ regarding incidence of symptomatic VTE. Risk of a relevant bleeding is higher with rivaroxaban than with enoxaparin and the same tendency exists also vs dabigatran. Direct rivaroxaban vs dabigatran comparisons in this setting are needed.
A Human Body Model for Efficient Numerical Characterization of UWB Signal Propagation in Wireless Body Area Networks

Wireless body area network (WBAN) is a new enabling system with promising applications in areas such as remote health monitoring and interpersonal communication. Reliable and optimum design of a WBAN system relies on a good understanding and in-depth studies of the wave propagation around a human body. However, the human body is a very complex structure and is computationally demanding to model. This paper aims to investigate the effects of the numerical model's structure complexity and feature details on the simulation results. Depending on the application, a simplified numerical model that meets desired simulation accuracy can be employed for efficient simulations. Measurements of ultra wideband (UWB) signal propagation along a human arm are performed and compared to the simulation results obtained with numerical arm models of different complexity levels. The influence of the arm shape and size, as well as tissue composition and complexity is investigated.
Religiosity, identity, and depression in late adolescence: A longitudinal study

In this study, longitudinal associations among religiosity, identity style, identity commitment, and depression were examined in a sample of late adolescents. Online survey data were collected in two waves with an approximate six-week interval. Correlations demonstrated that high levels of negative aspects of religiosity, such as negative religious coping, predicted high levels of depression. Other aspects of religiosity, such as positive religious coping, did not predict depression. In addition, high levels of diffuse-avoidant identity style predicted high levels of depression, and high levels of identity commitment predicted low levels of depression. However, when a regression was performed with all the predictors of wave 2 depression and controlling for depression at wave 1, the predictors were no longer significant. Associations between identity and religiosity were also examined.
Deterministic analysis of endless tuning of polarization controllers

The authors describe a new deterministic algorithm for endless reset-free polarization control (PC) based on sequences of tunable phase shifters and directional couplers, which can be implemented in a variety of discrete (e.g., rotating waveplates) and integrated optic material platforms. They provide a novel quantitative formulation of what endless reset-free PC operation means and prove in a rigorous way that the algorithm satisfies the defined requirements.
To appear in Publications of the Astronomical Society of the Pacific THE NUCLEAR SPECTRAL ENERGY DISTRIBUTION OF NGC 4395, THE LEAST LUMINOUS TYPE 1 SEYFERT GALAXY

We present X-ray (ROSAT), infrared, and radio observations of NGC 4395, which harbors the optically least luminous type 1 Seyfert nucleus discovered thus far. In combination with published optical and ultraviolet spectra, we have used these data to assemble the broadband spectral energy distribution (SED) of the galaxy's nucleus. Interestingly, the SED of NGC 4395 differs markedly from the SEDs of both quasars and typical low-luminosity active galactic nuclei, which may be a manifestation of the different physical conditions (i.e., black hole masses, accretion rates, and/or accretion modes) that exist in these objects. The nuclear X-ray source in NGC 4395 is variable and has an observed luminosity of just ∼ 10 38 ergs s −1. Although this emission could plausibly be associated with either a weak active nucleus or a bright stellar-mass binary system, the optical and ultraviolet emission-line properties of the nucleus strongly suggest that the X-rays arise from a classical AGN.
Extremal Mechanisms for Local Differential Privacy

Local differential privacy has recently surfaced as a strong measure of privacy in contexts where personal information remains private even from data analysts. Working in a setting where the data providers and data analysts want to maximize the utility of statistical inferences performed on the released data, we study the fundamental tradeoff between local differential privacy and information theoretic utility functions. We introduce a family of extremal privatization mechanisms, which we call staircase mechanisms, and prove that it contains the optimal privatization mechanism that maximizes utility. We further show that for all information theoretic utility functions studied in this paper, maximizing utility is equivalent to solving a linear program, the outcome of which is the optimal staircase mechanism. However, solving this linear program can be computationally expensive since it has a number of variables that is exponential in the data size. To account for this, we show that two simple staircase mechanisms, the binary and randomized response mechanisms, are universally optimal in the high and low privacy regimes, respectively, and well approximate the intermediate regime.
Automatic extraction of bilingual terms from a Chinese-Japanese parallel corpus

This paper proposes a new approach for the automatic extraction of bilingual terms from a domain-specific bilingual parallel corpus. We combine existing monolingual term extractor and a word alignment tool to extract bilingual terms. Our method is different from those past studies as we simply use a word alignment tool to extract multi-words terms, and we use one monolingual term extractor for both of languages to reduce extraction imbalance. We obtained a good precision and an improved BLEU score in our experiment based on a Chinese-Japanese parallel corpus.
Real Estate Property Maintenance Optimization Based on Multiobjective Multidimensional Knapsack Problem

The real estate property maintenance problem (REPMP) deals with the problem of the maintenance of real estate property buildings’ components using a limited budget in a limited period to achieve multiple and often conflicting objectives. Maintenance of the components requires a multiannual maintenance action plan optimized simultaneously on different criteria relative to buildings’ maintenance objectives (the quality of service, client satisfaction, regulatory compliance, etc.) while respecting a predetermined budget. This article presents an innovative modeling of REPMP into a multiobjective multidimensional knapsack problem and its optimization using a generalization of the discrete multiobjective particle swarm optimization algorithm and the Pareto optimality. An application of this methodology using data from a real estate property of 22 buildings of a major French company is presented and discussed.
Decentralized optimal dispatch of photovoltaic inverters in residential distribution systems

Summary form only given. Decentralized methods for computing optimal real and reactive power setpoints for residential photovoltaic (PV) inverters are developed in this paper. It is known that conventional PV inverter controllers, which are designed to extract maximum power at unity power factor, cannot address secondary performance objectives such as voltage regulation and network loss minimization. Optimal power flow techniques can be utilized to select which inverters will provide ancillary services, and to compute their optimal real and reactive power setpoints according to well-defined performance criteria and economic objectives. Leveraging advances in sparsity-promoting regularization techniques and semidefinite relaxation, this paper shows how such problems can be solved with reduced computational burden and optimality guarantees. To enable large-scale implementation, a novel algorithmic framework is introduced - based on the so-called alternating direction method of multipliers - by which optimal power flow-type problems in this setting can be systematically decomposed into sub-problems that can be solved in a decentralized fashion by the utility and customer-owned PV systems with limited exchanges of information. Since the computational burden is shared among multiple devices and the requirement of all-to-all communication can be circumvented, the proposed optimization approach scales favorably to large distribution networks.
MAJORANA DARK MATTER

In this letter in the framework of a simple see-saw scenario with two (or three) quasi degenerate Majorana neutrinos we propose that one of these neutrinos can be very weakly coupled, yet there is a mechanism of the generation of the abundance of such "dark" neutrino in the early universe. The mechanism of production is due to oscillations between "dark" Majorana neutrino and one of the Majorana's which has relatively large Yukawa couplings ("bright" Majorana neutrino). The transition of "bright" Majorana into a "dark" one is nonadiabatic. We point out on the similarity with the Landau-Zener transition regime. In our model one can explain the observed dark matter density, present matter-antimatter asymmetry and active neutrino data all at the same time
Key Roles for the Lipid Signaling Enzyme Phospholipase D1 in the Tumor Microenvironment During Tumor Angiogenesis and Metastasis

Genetic ablation or pharmacological inhibition of a lipid signaling enzyme attenuates tumor growth and metastasis. Targeting the Tumor Microenvironment Phospholipase D (PLD) is an enzyme that produces the signaling lipid phosphatidic acid and promotes the proliferation, survival, invasion, and metastasis of cancer cells. Chen et al. examined the role of the PLD isoforms PLD1 and PLD2 in the tumor environment. They found that compared to wild-type mice, mice deficient in PLD1 developed smaller, less vascularized tumors as well as fewer lung metastases in a xenograft model. Furthermore, mice treated with an inhibitor of PLD1 also developed smaller tumors and fewer metastases. These results suggest that PLD1 inhibitors could be developed to treat cancer. Angiogenesis inhibitors, which target tumor cells, confer only short-term benefits on tumor growth. We report that ablation of the lipid signaling enzyme phospholipase D1 (PLD1) in the tumor environment compromised the neovascularization and growth of tumors. PLD1 deficiency suppressed the activation of Akt and mitogen-activated protein kinase signaling pathways by vascular endothelial growth factor in vascular endothelial cells, resulting in decreased integrin-dependent cell adhesion to, and migration on, extracellular matrices, as well as reduced tumor angiogenesis in a xenograft model. In addition, mice lacking PLD1 incurred fewer lung metastases than did wild-type mice. Bone marrow transplantation and binding studies identified a platelet-derived mechanism involving decreased tumor cell–platelet interactions, in part because of impaired activation of αIIbβ3 integrin in platelets, which decreased the seeding of tumor cells into the lung parenchyma. Treatment with a small-molecule inhibitor of PLD1 phenocopied PLD1 deficiency, efficiently suppressing both tumor growth and metastasis in mice. These findings reveal that PLD1 in the tumor environment promotes tumor growth and metastasis and, taken together with previous reports on the roles of PLD in tumor cell–intrinsic adaptations to stress, suggest the potential use of PLD inhibitors as cancer therapeutics.
Random dot markers

This paper presents a novel approach for detecting and tracking markers with randomly scattered dots for augmented reality applications. Compared with traditional markers with square pattern, our random dot markers have several significant advantages for flexible marker design, robustness against occlusion and user interaction. The retrieval and tracking of these markers are based on geometric feature based keypoint matching and tracking. We experimentally demonstrate that the discriminative ability of forty random dots per marker is applicable for retrieving up to one thousand markers.
Clustering Polysemic Subcategorization Frame Distributions Semantically

Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguated corpus data. We describe a new approach which involves clustering subcategorization frame (SCF) distributions using the Information Bottleneck and nearest neighbour methods. In contrast to previous work, we particularly focus on clustering polysemic verbs. A novel evaluation scheme is proposed which accounts for the effect of polysemy on the clusters, offering us a good insight into the potential and limitations of semantically classifying undisambiguated SCF data.