Miracle-dz/UniFAR-FTUs huggingface dataset:
EMPIRICAL BAYESIAN TEST OF THE SMOOTHNESS By

In the context of adaptive nonparametric curve estimation problem, a common assumption is that a function (signal) to estimate belongs to a nested family of functional classes, parameterized by a quantity which often has a meaning of smoothness amount. It has already been realized by many that the problem of estimating the smoothness is not sensible. What then can be inferred about the smoothness? The paper attempts to answer this question. We consider the implications of our results to hypothesis testing. We also relate them to the problem of adaptive estimation. The test statistic is based on the marginalized maximum likelihood estimator of the smoothness for an appropriate prior distribution on the unknown signal.
FORECASTING OF TRIAZOLE, AMIDE, PIPEREDINYLE THIAZOL ISOXAZOLINE, OXAZOLE FUNGICIDES HAZARDOUS EFFECT ON HUMAN HEALTH IN CONSUMPTION OF VEGETABLES GROWED IN THEIR APPLICATION

The chemical plant protection products application is an integral part of intensive crop production technologies. However, scientists have proven that the increase in chemical pollution of the environment in 2 times increases the general level of morbidity of the adult population by 25%. That is why the risk assessment of pesticide-contaminated products consumption is an important part of the regulatory process. Methods. The parameters of fungicides difenoconazole, tebuconazole, cyflufenamid, oxathiapiprolin, famoxadone persistence in agricultural crops were studied in the field experiments. For the integrated assessment of the potential risk of pesticide-contaminated products consumption for human (RPCPC), a methodology developed on the basis of the Institute of Hygiene and Ecology was used. The acceptable daily intake (ADI), the half-life period (τ50) in plants and the average daily consumption of the product have been evaluated using four-graded scale. Results. According to the persistency in tomatoes and potatoes, fungicides difenoconazole, cyflufenamid, famoxadone are pertained to 3 classes of hazard (5-14 days); tebuconazole - to 2 class (15-30 days); oxathiapiprolin - to 4 class (less than 5 days). After the addition of all the obtained points, the RPCPC value (tomatoes and potatoes) for difenoconazole, cyflufenamid, famoxadone amounted 8 points (2+2+2× 2 and 2+2+4, respectively); tebuconazole - 8 points (3+1+2×2 or 3+1+4, respectively); oxathiapiprolin - 6 points (1+1+2×2 or 1+1+4, respectively). Discussion. Thus, the studied compounds belong to the 3rd class of hazard according to pesticide-contaminated products consumption for human index value. The results obtained by us correlate with the studies carried out in the previous stage. The exception was only difenoconazole, which was classified as hazard class 2 due to its high toxicity for human.
Thin randomly aligned hierarchical carbon nanotube arrays as ultrablack metamaterials

Ultrablack metamaterials are artificial materials able to harvest all the incident light regardless of wavelength, angle, or polarization. Here, we show the ultrablack properties of randomly aligned hierarchical carbon nanotube arrays with thicknesses below 200 nm. The thin coatings are realized by solution processing and dry-transfer deposition on different substrates. The hierarchical surface morphology of the coatings is biomimetic and provides a large effective area that improves the film optical absorption. Also, such a morphology is responsible for the moth-eye effect, which leads to the omnidirectional and polarization-independent suppression of optical reflection. The films exhibit an emissivity up to 99.36% typical of an ideal black body, resulting in the thinnest ultrablack metamaterial ever reported. Such a material may be exploited for thermal, optical, and optoelectronic devices such as heat sinks, optical shields, solar cells, light and thermal sensors, and light-emitting diodes.
Chiral Spin Texture in the Charge-Density-Wave Phase of the Correlated Metallic Pb = Si ð 111 Þ Monolayer

C. Tresca, C. Brun, T. Bilgeri, G. Menard, V. Cherkez, R. Federicci, D. Longo, F. Debontridder, M. D’angelo, D. Roditchev, G. Profeta, M. Calandra, and T. Cren Department of Physical and Chemical Sciences and SPIN-CNR, University of L’Aquila, Via Vetoio 10, I-67100 L’Aquila, Italy Sorbonne Université, CNRS, Institut des Nanosciences de Paris, UMR7588, F-75252, Paris, France Laboratoire de physique et d’étude des matériaux, LPEM-UMR8213/CNRS-ESPCI ParisTech-UPMC, 10 rue Vauquelin, F-75005 Paris, France
A Dominant Strategy Truthful , Deterministic Multi-Armed Bandit Mechanism with Logarithmic Regret ( Extended Abstract )

Multi-armed bandit (MAB) algorithms [3] are widely used in sequential decision making where the decisions are modeled as arms. Mechanism design has been applied in the context where the arms are controlled by strategic agents, leading to stochastic MAB mechanisms. An immediate example is sponsored search auctions (SSA). In SSA, there are several advertisers who wish to display their ads along with the search results generated in response to a query from an internet user. There are two components that are of interest to the planner or the search engine, (1) stochastic component : click through rate (CTR) of the ads or the probability that a displayed ad receives a click (2) strategic component : valuation of the agent for every click that the agent’s ad receives. The search engine wants to allocate a slot to an ad which has the maximum social welfare (product of click through rate and valuation). However neither the CTRs nor the valuations of the agents are known. This calls for a learning algorithm to learn the stochastic component (CTR) as well as a mechanism to elicit the strategic component (valuation). For single slot SSA, it is known that any truthful, deterministic MAB mechanism suffers a regret of Ω(T ) [2] where T is the time horizon. We observe that the characterization provided by Babaioff et al. [2] targets the worst case scenario. In particular, in the lower bound proof of Ω(T ), they consider an example scenario where the separation, ∆̄, between the expected rewards of the arms is a function of T . We note that when a similar example (∆̄ = T−1) is used with the popular UCB algorithm [1], the number of pulls to the sub-optimal arm is linear, even in the non-strategic case. Hence, learning algorithms targeting such worst case scenarios are restrictive for a practical implementation, even when the arms are non-strategic. Motivated by this, our contributions are as follows.
All You Need to Le rn 3 D Human Pose is Single 2 D Joint Locations

The task of three-dimensional (3D) human pose estimation from a single image can be divided into two parts: (1) Two-dimensional (2D) human joint detection from the image and (2) estimating a 3D pose from the 2D joints. Herein, we focus on the second part, i.e., a 3D pose estimation from 2D joint locations. The problem with existing methods is that they require either (1) a 3D pose dataset or (2) 2D joint locations in consecutive frames taken from a video sequence. We aim to solve these problems. For the first time, we propose a method that learns a 3D human pose without any 3D datasets. Our method can predict a 3D pose from 2D joint locations in a single image. Our system is based on the generative adversarial networks, and the networks are trained in an unsupervised manner. Our primary idea is that, if the network can predict a 3D human pose correctly, the 3D pose that is projected onto a 2D plane should not collapse even if it is rotated perpendicularly. We evaluated the performance of our method using Human3.6M and the MPII dataset and showed that our network can predict a 3D pose well even if the 3D dataset is not available during training.
Dealing with all Aspects of Plant Biology : Official Journal of the Societa Botanica Italiana

Climate change projections forecast a warming and an associated change in the distribution and intensity of rainfalls. In the case of theMediterranean area, this will be reflected in more frequent and severe drought periods in the future. Within a longterm (9 years) manipulation experiment, we aimed to study the effect of the soil drought projected for the coming decades (an average of 10% soil moisture reduction) onto photosynthetic rates and water relations, and onto the antioxidant and antistress defense capacity of Quercus ilex, a dominant species in Mediterranean forests, in two different seasons, spring and summer. Results showed that photosynthesis was limited by stomatal closure in summer. However, a decrease in photosynthesis as a consequence of drought was observed only during spring, possibly due to a low pigment concentration and to an insufficient antioxidant protection. In summer, the increased resistance to CO2 entry reduced photosynthesis in control and drought-treated leaves, though the higher pigment content and antioxidant levels in summer leaves prevented a further decrease in photosynthesis as a consequence of drought. Also total monoterpene emission rates were higher in summer than in spring, though they did not change with drought, as happened with photosynthetic pigments. On the other hand, the antioxidant defense system was induced by drought in both studied seasons, indicating an efficient activation of defense responses aiming at scavenging reactive oxygen species produced in Q. ilex leaves under drought.
Carnivore Diet Identification Through Scat and Genetic Analysis in Namibia, Africa

Namibia, Africa has the largest population of cheetahs in the world. However, the cheetah population worldwide is declining. During the summer of 2014, I worked at the Cheetah Conservation Fund (CCF, 2015) in Namibia for nine weeks to analyze the diets of carnivores in the area through genetic and scat analysis. CCF has used scat analysis in the past to determine the diet of cheetahs, but the diet of other carnivores in the area has never been examined. Including this additional information will give researchers insight as to how the ecosystem functions as a whole, which is crucial when managing a population. One hundred and eight various carnivore scat samples were analyzed including jackal, hyena, genet, Serval, leopard, African wildcat, caracal, civet, aardwolf and cheetah samples. This work will help CCF in their efforts to manage and protect the wild cheetah. The first step was determining the species each scat sample came from. To determine this DNA was extracted from each scat sample. The DNA was then amplified using polymerase chain reaction and then sequenced. Ultimately, the sequences were compared to a genome reference database and the species were determined by sequence similarity. After determining the species the scat belonged to, the scat samples were washed and the contents were analyzed. Microscopic analysis involved burning hairs to create imprints and looking at the patterns underneath a microscope. Results revealed that the cheetahs have the greatest degree of dietary overlap and are primarily competing for their prey with leopards. Now when CCF releases cheetahs back into the wild they will be able to take these findings into consideration to locate a release site that will ensure cheetah survival.
First principles calculations of the structural and electronic properties of the type-I semiconductor clathrate alloysBa8Ga16SixGe30−xandSr8Ga16SixGe30−x

We studied the structural and electronic properties of some of the Ba and Sr guest-containing type-I semiconductor clathrate alloys Ba8Ga16SixGe30−x and Sr8Ga16SixGe30−x for three values of the Si composition x x=0,5 ,15 . Our calculations are based on the generalized gradient approximation to density functional theory. Starting with the stable structures of the clathrate semiconductors Ba8Ga16Ge30 and Sr8Ga16Ge30 containing no Ga-Ga bonds, we constructed unit cells of Ba8Ga16SixGe30−x and Sr8Ga16SixGe30−x by replacing appropriate numbers of the framework Ge atoms with Si. For the values of the Si composition x that we considered, we found that the fundamental band gap of Ba8Ga16SixGe30−x decreases with increasing x. However, we found that the band gap of Sr8Ga16SixGe30−x increases with increasing x. Our results also show that several electronic states near the top of the valence band and near the bottom of the conduction band in both materials are modified by the Si p states. The trends in the structural and electronic properties of these materials as x is varied are discussed, and our results are compared to experiment where possible.
Belief Propagation Decoding of Polar Codes using Intelligent Post-processing

Polar codes, as only one provable capacity-achieving channel code, have become one of the 5G standards. Belief propagation (BP) algorithm has become one of popular decoding polar codes because of unique advantage of high parallelism. But compared to the successive cancellation list (SCL) algorithm, BP algorithm still has a performance gap. This paper exploits the design of polar BP decoder using intelligent post-processing to improve error performance. The post-processing is used to find the error bits and flip it as the messages of the initial frozen positions. We also use deep neural networks to improve the accuracy of finding error bits. Simulation shows that compared with the original BP decoding, the approach proposed in this work can achieve a performance improvement of about 0.3 dB with slight complexity increase.
Filtering of large signal sets: An almost blind case

We propose a new technique which allows to estimate any random signal from a large set of noisy observed data on the basis of information on only a few reference signals. The conceptual device behind the proposed estimator is a linear interpolation of an optimal incremental estimation applied to random signal pairs interpreted an extension of the Least Squares Linear (LSL) estimator. We consider the case of observations corrupted by an arbitrary noise (not by an additive noise only) and design the estimator in terms of the Moore-Penrose pseudoinverse matrix. Therefore, it is always well defined. The proposed estimator is justified by establishing an upper bound for the associated error and by showing that this upper bound is directly related to the expected error for an incremental application of the optimal LSL estimator. It is shown that such an estimator is possible under quite unrestrictive assumptions.
UvA-DARE (Digital Academic Repository) GridPix application to dual phase TPC

GridPix is a gas-ﬁlled detector with an aluminium mesh stretched 50 µ m above the Timepix CMOS pixel chip. This deﬁnes a high electric ﬁeld where gas ampliﬁcation occurs. A feasibility study is ongoing at Nikhef for the application of the GridPix technology as a charge sensitive device in a dual phase noble gas Time Projection Chamber (TPC), within the framework of the DARWIN design study for next generation dark matter experiments. The smallness of the device and well deﬁned materials allow for high radio-purity and low outgassing. The high granularity of a pixel readout and the high detection eﬃciency of single electrons of GridPix bring beneﬁts especially in terms of energy resolution for small energy deposits. This feature is interesting also for the measurement of the scintillation yield and the ionisation yield of noble liquids. The accurate measurements of such quantities have a direct impact on the data interpretation of dark matter experiments. The application in dual phase argon or xenon TPCs implies several technological challenges, such as the survival of the device at cryogenic temperature as well as the operation in a pure noble gas atmosphere without discharges. We describe here the recent developments of the project.
A Agent Environment StatesActions Rewards Critic B Agent Internal Environment Rewards Critic External Environment Sensations StatesDecisions Actions " Organism "

Psychologists call behavior intrinsically motivated when it is engaged in for its own sake rather than as a step toward solving a specific problem of clear practical value. But what we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated reinforcement learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy.
Gamma-ray emitting supernova remnants as the origin of Galactic cosmic rays

Abstract. It is generally believed that the cosmic ray spectrum below the knee is of Galactic origin, although the exact sources making up the entire cosmic ray energy budget are still unknown. Including effects of magnetic amplification, Supernova Remnants (SNR) could be capable of accelerating cosmic rays up to a few PeV and they represent the only source class with a sufficient non-thermal energy budget to explain the cosmic ray spectrum up to the knee. Now, gamma-ray measurements of SNRs for the first time allow to derive the cosmic ray spectrum at the source, giving us a first idea of the concrete, possible individual contributions to the total cosmic ray spectrum. In this contribution, we use these features as input parameters for propagating cosmic rays from its origin to Earth using GALPROP in order to investigate if these supernova remnants reproduce the cosmic ray spectrum and if supernova remnants in general can be responsible for the observed energy budget.
Clustering of extreme events in typical stochastic models

produced by typical stochastic models and compare the results with the ones of observed data. Specifically the stochastic models that we use are the AR(1), AR(2), ARMA(1,1), as well as the Hurst-Kolmogorov model. In terms of data, we use instrumental and proxy hydroclimatic time series. To quantify clustering we study the multi scale properties of each process and in particular the variation of standard deviation with time scale as well of the frequencies of similar events (e.g. those exceeding a certain threshold with time scale). To calculate these properties we use either analytical methods when possible, or Monte Carlo simulation.
Audio-Visual Target Speaker Extraction on Multi-Talker Environment using Event-Driven Cameras

In this work, we propose a new method to address audio-visual target speaker extraction in multi-talker environments using event-driven cameras. All audio-visual speech separation approaches use a frame-based video to extract visual features. However, these frame-based cameras usually work at 30 frames per second. This limitation makes it difficult to process an audio-visual signal with low latency. In order to overcome this limitation, we propose using event-driven cameras due to their high temporal resolution and low latency. Recent work showed that the use of landmark motion features is very important in order to get good results on audio-visual speech separation. Thus, we use event-driven vision sensors from which the extraction of motion is available at lower latency computational cost. A stacked Bidirectional LSTM is trained to predict an Ideal Amplitude Mask before post-processing to get a clean audio signal. The performance of our model is close to those yielded in frame-based fashion.
Privacy-preserving traffic padding in web-based applications

While web-based applications are gaining popularity, they also pose new security challenges. In particular, Chen et al. recently revealed that many popular Web applications actually leak out highly sensitive data from encrypted traffic due to side-channel attacks using packet sizes and timing [1]. They further demonstrated that existing solutions usually incur a high overhead while still not guaranteeing privacy protection. In this paper, we observe a striking similarity between this issue and another well studied problem, privacy-preserving data publishing (PPDP). Based on such a similarity, we propose a formal model for privacy-preserving traffic padding (PPTP) that encompasses privacy requirement, padding cost, and padding methods.
Confidence through Attention

Both statistical (SMT) and neural (NMT) approaches to machine translation (MT) explore large search spaces to produce and score translations. It is however well known that often the top hypothesis as scored by such approaches may not be the best overall translation among those that can be produced. Previous work on SMT has extensively explored re-ranking strategies in attempts to ﬁnd the best possible translation. In this paper, we focus on NMT and provide an in-depth investigation to explore the inﬂuence of beam sizes on information content and translation quality. We gather new insights using oracle experiments on the efﬁcacy of exploiting larger beams and propose a simple, yet novel consensus-based, n -best re-ranking approach that makes use of different automatic evaluation metrics to measure consensus in n -best lists. Our results reveal that NMT is able to cover more of the information content of the references compared to SMT and that this leads to better re-ranked translations (according to human evaluation). We further show that the MT evaluation metric used for the consensus-based re-ranking plays a major role, with character-based metrics performing better than BLEU.
Multi-appearance segmentation and extended 0-1 programming for dense small object tracking

Aiming to address dense small object tracking, we propose an image-to-trajectory framework including tracking and detection, where Track-Oriented Multiple Hypothesis Tracking(TOMHT) is revised for tracking. Unlike common cases of multi-object tracking, merged detections and the greater number of objects make dense small object tracking a more challenging problem. Firstly, we handle frequent merged detections through the aspects of detection and hypothesis selection. To tackle merged detection, we revise Local Contrast Method(LCM) and propose a multi-appearance variant, which exploits tree-like topological information and realizes one threshold for one object. Meanwhile, one-to-many constraint is employed via the proposed extended 0-1 programming, which enables hypothesis selection to handle track exclusions caused by merged detections. Secondly, to alleviate the high complexity caused by dense objects, we consider batch optimization and more rigorous and precise pruning technologies. Specifically, we propose autocorrelation based motion score test and two-stage hypotheses pruning. Experimental results are presented to verify the strength of our methods, which indicates speed and performance advantages of our tracker.
Generalized plenoptic sampling

Image-based rendering (IBR) has become a very active research area in recent years. The optimal sampling problem for IBR has not been completely solved. In this paper, we show that theoretically, the optimal sampling efficiency can be achieved by employing the generalized periodic sampling theory with arbitrary geometry. When there is no occlusion in the scene and the scene is Lambertian, we show that the sampling density can be twice of that when we use rectangular sampling. We then propose a general framework for IBR sampling. Begin with an over-sampled dataset, we down-sample the data first in the discrete domain. To render an image from the down-sampled data, two approaches are proposed, i.e., to reconstruct the oversampled dataset first through up-sampling and then rendering, or to use a continuous interpolation filter to calculate the desired light rays directly. Eigenfilter method is employed to design filters during downsampling and up-sampling. We show that if the proposed approach adopts the same down-sampling density as the previous work, the reconstruction filter of the proposed approach is easier to design, and the reconstructed scene could have a higher quality. However, in practice rectangular sampling has several advantages over generalized sampling. We analyze the pros and cons for each sampling approach, and explain why in practice rectangular sampling is still more preferable.