Miracle-dz/UniFAR-FTUs huggingface dataset:
Federal Reserve Bank of New York Staff Reports Jump-robust Volatility Estimation Using Nearest Neighbor Truncation Jump-robust Volatility Estimation Using Nearest Neighbor Truncation

This paper presents preliminary findings and is being distributed to economists and other interested readers solely to stimulate discussion and elicit comments. The views expressed in this paper are those of the authors and are not necessarily reflective of views at the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors. Abstract We propose two new jump-robust estimators of integrated variance based on high-frequency return observations. These MinRV and MedRV estimators provide an attractive alternative to the prevailing bipower and multipower variation measures. Specifically, the MedRV estimator has better theoretical efficiency properties than the tripower variation measure and displays better finite-sample robustness to both jumps and the occurrence of " zero " returns in the sample. Unlike the bipower variation measure, the new estimators allow for the development of an asymptotic limit theory in the presence of jumps. Finally, they retain the local nature associated with the low-order multipower variation measures. This proves essential for alleviating finite sample biases arising from the pronounced intraday volatility pattern that afflicts alternative jump-robust estimators based on longer blocks of returns. An empirical investigation of the Dow Jones 30 stocks and an extensive simulation study corroborate the robustness and efficiency properties of the new estimators.
Predicting Jellyfish Strandings in the Moroccan North-West Mediterranean Coastline

In the recent years, the frequency and the geographic distribution of jellyfish blooms have been increased in the Mediterranean north-west coastline of Morocco. Since 2011 this area has been subjected to successive blooms and stranding of Pelagia noctiluca. Our research was aimed to study the reasons of jellyfish blooms and to predict their stranding in our coastline. We have evaluated three parameters that we are suspecting to be responsible of Jellyfish blooms and stranding on the northern coast of the Mediterranean Sea in Morocco. The evaluation of the relations between sea surface temperature, waves height and the wind direction causing the stranding of Jellyfish, demonstrated that the correlation scores were found to be statistically significant with r(33) =0.445, p < 0.01, for sea surface temperature (°C) and r(33) =0. 694, p< 0.001 for waves height (m). Moreover a one-way analysis of variance (ANOVA) was calculated on wind direction causing stranding of jellyfish; the analysis was found to be very significant, F (1, 31) = 25.823, p = 0.001.
A classifier training method for face detection based on AdaBoost

in this work, we proposed two novel ideas for improved Adaboost-cascade face detection. Firstly through researching the characteristic of weak classifier, we proposed a method of computing threshold which obtained high detection rate for using fewer weak classifiers. Secondly selecting discriminative weak learners to optimize the detection performance and employing the number of Haar-like features in the Adaboost training. This approach maintains the simplicity of traditional formulation as well as being more discriminative. Mostly it is more efficient and a robust detector with few features. Simulation experiments in most static face detection and a little video face detection system are conducted that including human frontal faces and clutter, our method is superior to conventional AdaBoost in computer efficiency and increase the detection accuracy of the existing classifiers.
Ultrasound-guided synovial biopsy: a safe, well-tolerated and reliable technique for obtaining high-quality synovial tissue from both large and small joints in early arthritis patients.

OBJECTIVE To determine the tolerability, safety and yield of synovial tissue in an early arthritis cohort using a minimally invasive, ultrasound (US)-guided, synovial biopsy technique in small, medium and large joints. METHODS 93 sequential biopsy procedures were assessed from a total of 57 patients (baseline and 36 repeat biopsies at 6 months) recruited as part of the 'Pathobiology of Early Arthritis Cohort' study. Patients completed a tolerability questionnaire prior to and following the synovial biopsy procedure. The synovial biopsy was performed under US guidance with US images of the joint recorded prior to each procedure. Synovial tissue was harvested for immunohistochemistry and RNA extraction. RESULTS Five different joint sites were biopsied (knee, elbow, wrist, metacarpal phalangeal and proximal interphalangeal). No significant complications were reported following the procedure. No difference in pain, swelling and stiffness of the biopsied joint from before and after the procedure was demonstrated. A median of 14 biopsy samples was retrieved from each procedure with 93% of biopsy procedures yielding good quality tissue. RNA yield was good in all joints and in repeat biopsies. Multivariant analysis demonstrated a significantly greater yield of RNA and graded tissue in relation to a high prebiopsy, grey-scale synovitis score (0-3, semiquantitative). CONCLUSIONS A minimally invasive approach to synovial tissue harvesting, using US guidance, is both safe and well-tolerated by patients. Tissue quality/RNA yield is preserved in subsequent biopsies following therapeutic intervention. A high US grey-scale synovitis score is a predictor of good quality/quantity of tissue and RNA.
Parameter Compensation for Mel-LP based Noisy Speech Recognition

This study deals with a noise robust distributed speech recognizer for real-world applications by deploying feature parameter compensation technique. To realize this objective, Mel-LP based speech analysis has been used in speech coding on the linear frequency scale by applying a first-order all-pass filter instead of a unit delay. To minimize the mismatch between training and test phases, Cepstral Mean Normalization (CMN) and Blind Equalization (BEQ) have been applied to enhance Mel-LP cepstral coefficients as an effort to reduce the effect of additive noise and channel distortion. The performance of the proposed system has been evaluated on Aurora-2 database which is a subset of TIDigits database contaminated by additive noises and channel effects. The baseline performance, that is, for Mel-LPC the average word accuracy for test set A has found to be 59.05%. By applying the CMN and BEQ with the Mel-LP cepstral coefficients, the performance has been improved to 68.02 and 65.65%, respectively.
Modeling of MEMS-type Devices for Microprocessor Cooling

This paper discusses the use of MEMS-type devices for the cooling of microprocessors. Vapor compression refrigerators and circulated-coolant heat-exchange systems are analyzed. Water and commercial refrigerants are considered as working fluids. Calculations indicate that although vapor compression systems are capable of removing greater amounts of heat, circulated-coolant systems are far more practical in terms of size and power requirements. The use of a circulated-coolant system can remove up to 400 W of heat from microprocessors at 120°C to ambient air at 35°C, about three times the heat removal current heat-sink systems achieve.
Image Content Classification by Using Visual Terms

We propose a technique for automatic recognition of content in images. Our technique uses machine learning methods to build classifiers able to decide about the presence of semantic concepts in images. Our classifiers exploits a representation of images in terms of vectors of visual terms. A visual term represents a set of visually similar regions that can be found in images. An image is indexed by first using a segmentation algorithm to extract regions, then extracted regions are replaced by the visual terms that represent them. We discuss how the set of visual terms is generated and how weights are assigned to visual terms to assess their relevance in images. A learning algorithm for Support Vector Machine is used to obtain a classifiers using training sets of images represented by using visual terms. The proposes technique offers very good performance as demonstrated by the experiments that we performed.
Las lomas de Lima: enfocando ecosistemas desérticos como espacios abiertos en Lima metropolitana

Lima’s coastal lomas are fog oasis whose verdure and biodiversity challenge our imagery of the desert. Due to their extension and importance within an arid environment, they could constitute the biggest environmental referent for a city such as Metropolitan Lima, which faces current environmental and urban challenges. Today, Lima's fog oasis are open spaces and as such they present an opportunity to rethink the relationship they maintain with the city, either as natural reserves or spaces for public usage. This article aims to approach the lomas ecosystems from a geo-urban perspective to analyze the potential they offer both as an alternative and as an opportunity to overcome certain environmental challenges.
Characterization and Strain Improvement of a Hypercellulytic Variant, Trichoderma reesei SN1, by Genetic Engineering for Optimized Cellulase Production in Biomass Conversion Improvement

The filamentous fungus Trichoderma reesei is a widely used strain for cellulolytic enzyme production. A hypercellulolytic T. reesei variant SN1 was identified in this study and found to be different from the well-known cellulase producers QM9414 and RUT-C30. The cellulose-degrading enzymes of T. reesei SN1 show higher endoglucanase (EG) activity but lower β-glucosidase (BGL) activity than those of the others. A uracil auxotroph strain, SP4, was constructed by pyr4 deletion in SN1 to improve transformation efficiency. The BGL1-encoding gene bgl1 under the control of a modified cbh1 promoter was overexpressed in SP4. A transformant, SPB2, with four additional copies of bgl1 exhibited a 17.1-fold increase in BGL activity and a 30.0% increase in filter paper activity. Saccharification of corncob residues with crude enzyme showed that the glucose yield of SPB2 is 65.0% higher than that of SP4. These results reveal the feasibility of strain improvement through the development of an efficient genetic transformation platform to construct a balanced cellulase system for biomass conversion.
Spectrum and energy efficient cognitive relay for spectrum leasing

In this paper, we propose a spectrum and energy efficient spectrum leasing scheme based on a two-path successive relay technique. The proposed scheme aims to minimize the primary energy consumption to support a primary target rate while maximizing the secondary rate under an energy budget. Two secondary users are employed to alternately forward the primary signal so as to reduce the transmission time and energy of the primary user. As a reward, after completing the primary signal transmission, the spectrum is leased to secondary users in the remaining time. A utility function that reflects the tradeoff between transmission rate and energy consumption is maximized to determine the optimal time and power allocation. A distributed secondary user selection algorithm is further proposed to select two best secondary users that maximize the utility and then serve as relays for primary system. Simulation results demonstrate that the proposed spectrum leasing scheme significantly reduces the energy cost of primary user and improves the rate of secondary user compared to the conventional relay based spectrum leasing.
Observations of Flux Ropes With Strong Energy Dissipation in the Magnetotail

An ion‐scale flux rope (FR), embedded in a high‐speed electron flow (possibly an electron vortex), is investigated in the magnetotail using observations from the Magnetospheric Multiscale (MMS) spacecraft. Intense electric field and current and abundant waves are observed in the exterior and interior regions of the FR. Comparable parallel and perpendicular currents in the interior region imply that the FR has a non‐force‐free configuration. Electron demagnetization occurs in some subregions of the FR. It is surprising that strong dissipation (J × E' up to 2,000 pW/m3) occurs in the center of the FR without signatures of secondary reconnection or coalescence of two FRs, implying that FR may provide another important channel for energy dissipation in space plasmas. These features indicate that the observed FR is still highly dynamical, and hosts multiscale coupling processes, even though the FR has a very large scale and is far away from the reconnection site.
Numerical Simulation of Air Pollution over Kanto area in Japan using the MM5/CMAQ Model -Comparison of Air Pollution Concentration between two different climatic days

Urban air pollution is still an important recognized environmental problem. The primary pollutants, such as NOx, VOC, are the emissions from the industrial facilities, motor vehicles and heating systems. These emissions contribute to the formation of secondary pollutants like ozone and other oxidants through complex photochemistry in the atmosphere near ground level. The significant decrease of air pollution concentration is not observed in recent years though there are severe environmental standards. Due to the rapid increases in land covering and artificial heat release, the effects on the urban climate is very evident. It is found that meteorological factors, such as temperature, wind, precipitation, cloud, fog especially urban heat island (UHI), have close relationships with the air pollution concentrations. (Chao, 1990; Cuhadaroglu, et al, 1977; Escourrou, et al, 1990; Miyazaki, et al, 1991; Lacour, et al, 2006;). Conversely, atmospheric pollution also has important effects in modifying urban climate in various ways such as by increasing long-wave radiation from the sky in the canopy layer, and increasing absorption of short-wave radiation in the boundary layer. It is found that UHI can raise the rate of chemical reaction between nitrogen oxides (NOx) and Volatile Organic Compounds (VOCs), this lead to significantly increase surface ozone concentrations in city areas(Dewent, et al, 2003). In this paper, we would like to investigate influence of climatic change on the atmospheric pollution over Kanto area in Japan using two case studies; (1) simulation period with mild weather, and (2) the period that it’s the hot and clear weather pattern associated with climatic change. 2. MODLE DESCRIPTION
The paradox of the binomial Ixodes ricinus activity and the observed unimodal Lyme borreliosis season in Hungary

The change of ambient temperature plays a key role in determining the run of the annual Lyme season. Our aim was to explain the apparent contradiction between the annual unimodal Lyme borreliosis incidence and the bimodal Ixodes ricinus tick activity run – both observed in Hungary – by distinguishing the temperature-dependent seasonal human and tick activity, the temperature-independent factors, and the multiplicative effect of human outdoor activity in summer holiday, using data from Hungary in the period of 1998–2012. This separation was verified by modeling the Lyme incidence based on the separated factors, and comparing the run of the observed and modeled incidence. We demonstrated the bimodality of tick season by using the originally unimodal Lyme incidence data. To model the outdoor human activity, the amount of camping guest nights was used, which showed an irregular run from mid-June to September. The human outdoor activity showed a similar exponential correlation with ambient temperature to that what the relative incidence did. It was proved that summer holiday has great influence on Lyme incidence.
Probabilistic Planning with Nonlinear Utility Functions

Researchers often express probabilistic planning problems as Markov decision process models and then maximize the expected total reward. However, it is often rational to maximize the expected utility of the total reward for a given nonlinear utility function, for example, to model attitudes towards risk in high-stake decision situations. In this paper, we give an overview of basic techniques for probabilistic planning with nonlinear utility functions, including functional value iteration and a backward induction method for one-switch utility functions.
Global Optimization of Robotic Grasps

This paper presents a procedure to optimize the quality of robotic grasps for objects that need to be held and manipulated in a specific way, characterized by a number of tight contact constraints. The main difficulties of the problem include that the set of feasible grasps is a manifold implicitly defined by a system of non-linear equations, the high dimension of this manifold, and the multi-modal nature of typical grasp quality indices, which make local optimization methods get trapped into local extrema. The proposed procedure finds a way around these difficulties by focussing the exploration on a relevant subset of grasps of lower dimension, which is traced out exhaustively using higher-dimensional continuation techniques. Using these techniques, a detailed atlas of the subset is obtained, on which the highest quality grasp according to any desired criterion can be readily identified. Experiments on a 3-finger planar hand and on the Schunk anthropomorphic hand validate the approach.
Primal-Dual Framework for Feature Selection using Least Squares Support Vector Machines

Least Squares Support Vector Machines (LSSVM) perform classification using L2-norm on the weight vector and a squared loss function with linear constraints. The major advantage over classical L2-norm support vector machine (SVM) is that it solves a system of linear equations rather than solving a quadratic programming problem. The L2-norm penalty on the weight vectors is known to robustly select features. The zero-norm or the number of non-zero elements in a vector is an ideal quantity for feature selection. The L0-norm minimization is a computationally intractable problem. However, a convex relaxation to the direct zero-norm minimization was proposed recently. In this paper, we propose a combination of L2-norm penalty and the convex relaxation of the L0-norm penalty for feature selection in classification problems. We propose a primal-dual framework for feature selection using the combination of L2-norm and L0-norm penalty resulting in closed form solution. A series of experiments on microarray data and UCI data demonstrates that our proposed method results in better performance.
A Combinatorial Model for ECG Interpretation

A new, combinatorial model for analyzing and interpreting an electrocardiogram (ECG) is presented. An application of the model is QRS peak detection. This is demonstrated with an online algorithm, which is shown to be space as well as time efficient. Experimental results on the MIT-BIH Arrhythmia database show that this novel approach is promising. Further uses for this approach are discussed, such as taking advantage of its small memory requirements and interpreting large amounts of pre-recorded ECG data. Keywords—Combinatorics, ECG analysis, MIT-BIH Arrhythmia Database, QRS Detection, String Algorithms
Effectiveness of landscape‐based green infrastructure for stormwater management in suburban catchments

Land cover changes associated with urbanization have negative effects on downstream ecosystems. Contemporary urban development attempts to mitigate these effects by designing stormwater infrastructure to mimic predevelopment hydrology, but their performance is highly variable. This study used in situ monitoring of recently built neighbourhoods to evaluate the catchment‐scale effectiveness of landscape decentralized stormwater control measures (SCMs) in the form of street connected vegetated swales for reducing runoff volumes and flow rates relative to curb‐and‐gutter infrastructure. Effectiveness of the SCMs was quantified by monitoring runoff for 8 months at the outlets of 4 suburban catchments (0.76–5.25 ha) in Maryland, USA. Three “grey” catchments installed curb‐and‐gutter stormwater conveyances, whereas the fourth “green” catchment built parcel‐level vegetated swales. The catchment with decentralized SCMs reduced runoff, runoff ratio, and peak runoff compared with the grey infrastructure catchments. In addition, the green catchment delayed runoff, resulting in longer precipitation–runoff lag times. Runoff ratios across the monitoring period were 0.13 at the green catchment and 0.37, 0.35, and 0.18 at the 3 grey catchments. Runoff only commenced after 6 mm of precipitation at the decentralized SCM catchment, whereas runoff occurred even during the smallest events at the grey catchments. However, as precipitation magnitudes reached 20 mm, the green catchment runoff characteristics were similar to those at the grey catchments, which made up 37% of the total precipitation in only 10 of 72 events. Therefore, volume‐based reduction goals for stormwater using decentralized SCMs such as vegetated swales require additional redundant SCMs in a treatment train as source control and/or end‐of‐pipe detention to capture a larger fraction of runoff and more effectively mimic predevelopment hydrology for the relatively rare but larger precipitation events.
Sensitivity and Specificity of Multiple Kato-Katz Thick Smears and a Circulating Cathodic Antigen Test for Schistosoma mansoni Diagnosis Pre- and Post-repeated-Praziquantel Treatment

Background Two Kato-Katz thick smears (Kato-Katzs) from a single stool are currently recommended for diagnosing Schistosoma mansoni infections to map areas for intervention. This ‘gold standard’ has low sensitivity at low infection intensities. The urine point-of-care circulating cathodic antigen test (POC-CCA) is potentially more sensitive but how accurately they detect S. mansoni after repeated praziquantel treatments, their suitability for measuring drug efficacy and their correlation with egg counts remain to be fully understood. We compared the accuracies of one to six Kato-Katzs and one POC-CCA for the diagnosis of S. mansoni in primary-school children who have received zero to ten praziquantel treatments. We determined the impact each diagnostic approach may have on monitoring and evaluation (M&E) and drug-efficacy findings. Method/Principle Findings In a high S. mansoni endemic area of Uganda, three days of consecutive stool samples were collected from primary school-aged children (six - 12 years) at five time-points in year one: baseline, one-week-post-, four-weeks-post-, six-months-post-, and six-months-one-week-post-praziquantel and three time-points in years two and three: pre-, one-week-post- and four-weeks-post-praziquantel-treatment/retreatment (n = 1065). Two Kato-Katzs were performed on each stool. In parallel, one urine sample was collected and a single POC-CCA evaluated per child at each time-point in year one (n = 367). At baseline, diagnosis by two Kato-Katzs (sensitivity = 98.6%) or one POC-CCA (sensitivity = 91.7%, specificity = 75.0%) accurately predicted S. mansoni infections. However, one year later, a minimum of three Kato-Katzs, and two years later, five Kato-Katzs were required for accurate diagnosis (sensitivity >90%) and drug-efficacy evaluation. The POC-CCA was as sensitive as six Kato-Katzs four-weeks-post and six-months-post-treatment, if trace readings were classified as positive. Conclusions/Significance Six Kato-Katzs (two/stool from three stools) and/or one POC-CCA are required for M&E or drug-efficacy studies. Although unable to measure egg reduction rates, one POC-CCA appears to be more sensitive than six Kato-Katzs at four-weeks-post-praziquantel (drug efficacy) and six-months-post-praziquantel (M&E).