Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk-Aware Linear Bandits: Theory and Applications in Smart Order Routing

Motivated by practical considerations in machine learning for financial decision-making, such as risk aversion and large action space, we consider risk-aware bandits optimization with applications in smart order routing (SOR). Specifically, based on preliminary observations of linear price impacts made from the NASDAQ ITCH dataset, we initiate the study of risk-aware linear bandits. In this setting, we aim at minimizing regret, which measures our performance deficit compared to the optimum's, under the mean-variance metric when facing a set of actions whose rewards are linear functions of (initially) unknown parameters. Driven by the variance-minimizing globally-optimal (G-optimal) design, we propose the novel instance-independent Risk-Aware Explore-then-Commit (RISE) algorithm and the instance-dependent Risk-Aware Successive Elimination (RISE++) algorithm. Then, we rigorously analyze their near-optimal regret upper bounds to show that, by leveraging the linear structure, our algorithms can dramatically reduce the regret when compared to existing methods. Finally, we demonstrate the performance of the algorithms by conducting extensive numerical experiments in the SOR setup using both synthetic datasets and the NASDAQ ITCH dataset. Our results reveal that 1) The linear structure assumption can indeed be well supported by the Nasdaq dataset; and more importantly 2) Both RISE and RISE++ can significantly outperform the competing methods, in terms of regret, especially in complex decision-making scenarios.
Surgical Skill Assessment via Video Semantic Aggregation

Automated video-based assessment of surgical skills is a promising task in assisting young surgical trainees, especially in poor-resource areas. Existing works often resort to a CNN-LSTM joint framework that models long-term relationships by LSTMs on spatially pooled short-term CNN features. However, this practice would inevitably neglect the dif-ference among semantic concepts such as tools, tissues, and background in the spatial dimension, impeding the subsequent temporal relationship modeling. In this paper, we propose a novel skill assessment framework, Vi deo S emantic A ggregation (ViSA), which discovers diﬀerent semantic parts and aggregates them across spatiotemporal dimensions. The explicit discovery of semantic parts provides an explanatory visualization that helps understand the neural network’s decisions. It also enables us to further incorporate auxiliary information such as the kinematic data to improve representation learning and performance. The experiments on two datasets show the competitiveness of ViSA compared to state-of-the-art methods. Source code is available at: bit.ly/MICCAI2022ViSA.
A Primal–Dual-Based Power Control Approach for Capacitated Edge Servers

The intensity of radio waves decays rapidly with increasing propagation distance, and an edge server’s antenna needs more power to form a larger signal coverage area. Therefore, the power of the edge server should be controlled to reduce energy consumption. In addition, edge servers with capacitated resources provide services for only a limited number of users to ensure the quality of service (QoS). We set the signal transmission power for the antenna of each edge server and formed a signal disk, ensuring that all users were covered by the edge server signal and minimizing the total power of the system. This scenario is a typical geometric set covering problem, and even simple cases without capacity limits are NP-hard problems. In this paper, we propose a primal–dual-based algorithm and obtain an m-approximation result. We compare our algorithm with two other algorithms through simulation experiments. The results show that our algorithm obtains a result close to the optimal value in polynomial time.
Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction

Prediction of annual crop yields at a county granularity is important for national food production and price stability. In this paper, towards the goal of better crop yield prediction, leveraging recent graph signal processing (GSP) tools to exploit spatial correlation among neighboring counties, we denoise relevant features via graph spectral filtering that are inputs to a deep learning prediction model. Specifically, we first construct a combinatorial graph with edge weights that encode county-to-county similarities in soil and location features via metric learning. We then denoise features via a maximum a posteriori (MAP) formulation with a graph Laplacian regularizer (GLR). We focus on the challenge to estimate the crucial weight parameter $\mu$, trading off the fidelity term and GLR, that is a function of noise variance in an unsupervised manner. We first estimate noise variance directly from noise-corrupted graph signals using a graph clique detection (GCD) procedure that discovers locally constant regions. We then compute an optimal $\mu$ minimizing an approximate mean square error function via bias-variance analysis. Experimental results from collected USDA data show that using denoised features as input, performance of a crop yield prediction model can be improved noticeably.
Optical Reading of Nanoscale Magnetic Bits in an Integrated Photonic Platform

In this paper, we propose a compact integrated hybrid plasmonic-photonic device for optical reading of nanoscale magnetic bits with perpendicular magnetic anisotropy in a magnetic racetrack on top of a photonic waveguide on the indium phosphide membrane on silicon platform. The hybrid device is constructed by coupling a doublet of V-shaped gold plasmonic nanoantennas on top of the indium phosphide waveguide. By taking advantage of the localized surface plasmons, our hybrid device can enable detection of the magnetization state in magnetic bits beyond the diffraction limit of light and enhance the polar magneto-optical Kerr effect (PMOKE). We further illustrate how combining the hybrid device with a plasmonic polarization rotator provides magneto-optical read-out by transforming the PMOKE-induced polarization change into an intensity variation of the waveguide mode. According to the simulation results based on a three-dimensional finite-difference time-domain method, the hybrid device can detect the magnetization states in targeted bits in a magnetic racetrack medium down to $\sim 100\times 100$ nm2, regardless of the magnetization state of the rest of the racetrack with a relative intensity contrast of greater than 0.5% for a $\sim 200\times 100$ nm2 magnetic bit. We believe our hybrid device can be an enabling technology that can connect integrated photonics with nanoscale spintronics, paving the way toward ultrafast and energy efficient advanced on-chip applications.
A novel method of combining blood oxygenation and blood flow sensitive magnetic resonance imaging techniques to measure the cerebral blood flow and oxygen responses to an neural stimulus.

Simultaneous implementation of magnetic resonance imaging methods for Arterial Spin Labeling (ASL) and Blood Oxygenation Level Dependent (BOLD) imaging makes it possible to quantitatively measure the changes in cerebral blood flow (CBF) and cerebral oxygen metabolism (CMRO 2 ) that occur in response to neural stimuli. To date, however, the range of neural stimuli amenable to quantitative analysis is limited to those that may be presented in a simple block or event related design such that measurements may be repeated and averaged to improve precision. Here we examined the feasibility of using the relationship between cerebral blood flow and the BOLD signal to improve dynamic estimates of blood flow fluctuations as well as to estimate metabolic-hemodynamic coupling under conditions where a stimulus pattern is unknown. We found that by combining the information contained in simultaneously acquired BOLD and ASL signals through a method we term BOLD Constrained Perfusion (BCP) estimation, we could significantly improve the precision of our estimates of the hemodynamic response to a visual stimulus and, under the conditions of a calibrated BOLD experiment, accurately determine the ratio of the oxygen metabolic response to the hemodynamic response. Importantly we were able to accomplish this without utilizing a priori knowledge of the temporal nature of the neural stimulus, suggesting that BOLD Constrained Perfusion estimation may make it feasible to quantitatively study the cerebral metabolic and hemodynamic responses to more natural stimuli that cannot be easily repeated or averaged.
PII: S1010-7940(02)00296-8

Objectives: Minimally invasive saphenous vein harvesting is advocated to reduce wound morbidity. Our early experience with minimally invasive techniques, however, suggested that increased tissue traction and trauma might follow. We aimed to test the hypothesis that minimally invasive harvesting reduces post-operative pain and inflammation. A secondary objective was to determine if minimally invasive harvesting could be performed efficiently. Methods: Forty patients were prospectively randomised into minimally invasive harvesting (Minimal, n 1⁄4 22) and traditional open harvesting (Open, n 1⁄4 18). A modified bridging technique was used for minimally invasive harvesting (SaphLITEe, Genzyme Surgical Products, Cambridge, MA, USA). One surgeon performed all operations. Primary end points were signs of impaired healing (a composite score) and pain (visual analogue score). Secondary end-points (operation variables) were also collected. Continuous variables were analysed by Student’s t-test and categorical variables were analysed by Mann–Whitney U-test. Results: There were no significant demographic differences between the two groups (height, weight, albumin, diabetes, and peripheral vascular disease). In the early post-operative period, Minimal group had significantly less leg wound pain (P 1⁄4 0:04) and wound sepsis scores (P 1⁄4 0:01). Sternal pain was the same in both groups. After 6 weeks, wound scores and leg pain scores were not significantly different. There were no significant differences in rate of harvest (1.1 cm/min in each group). In Minimal group, 4 cm veins were harvested for each 1 cm skin incision compared with 1 cm in Open group (P , 0:01). Conclusions: Minimally invasive saphenous vein harvesting significantly reduces early post-operative leg pain and wound sepsis. Our study demonstrates that minimally invasive harvesting can be performed at a satisfactory speed and should be considered to help reduce early post-operative morbidity. q 2002 Elsevier Science B.V. All rights reserved.
Title : Siderophore product ornibactin is required for the bactericidal activity of 1 Burkholderia contaminans MS 14 2 Running tile : Ornibactin is required for the bactericidal activity of strain MS 14 3 4

Burkholderia contaminans MS14 was isolated from soil in Mississippi. When cultivated on 25 nutrient broth–yeast extract (NBY) agar medium, the colonies exhibit bactericidal activity 26 against a wide range of plant pathogenic bacteria. Only a bacteriostatic compound with 27 siderophore activity was successfully purified, which was determined by nuclear magnetic 28 resonance (NMR) to be ornibactin. The isolation of the bactericidal compound has not yet been 29 achieved, thus, the exact nature of the bactericidal compound is still unknown. In an attempt to 30 isolate bactericidal compound, an interesting relationship between the production of ornibactin 31 and the bactericidal activity of MS14 was characterized. Transposon mutagenesis resulted in two 32 strains that lost bactericidal activity; an insertional mutation in a nonribosomal peptide 33 synthetase (NRPS) gene for ornibactin biosynthesis and a luxR family transcriptional regulatory 34 gene. Co-culture of these two mutant strains resulted in the restoration of the bactericidal activity. 35 Furthermore, the addition of ornibactin to the NRPS mutant restored the bactericidal phenotype. 36 In MS14, it has been demonstrated that ornibactin has an alternative function aside from iron 37 sequestration. Comparison of the ornibactin biosynthetic genes in Burkholderia species show 38 diversity among the regulatory elements, while the gene products for ornibactin synthesis are 39 conserved. This is an interesting observation given that ornibactin is supposed to have the same 40 defined function within Burkholderia species. Ornibactin is produced by most Burkholderia 41 species and further consideration for its role in regulating secondary metabolites production 42 should be investigated. series experiments bactericidal phenotype of MS14. This observation provides evidence for an alternative function 47 for ornibactin aside from iron sequestration. Ornibactin should be further evaluated for its role in 48 regulating secondary metabolite biosynthesis in other Burkholderia species. 49
fMRI Repetition Suppression During Generalized Social Categorization article openly available. Please share how this access benefits you. Your story matters

Correctly identifying friends and foes is integral to successful group living. Here, we use repetition suppression to examine the neural circuitry underlying generalized group categorization—the process of categorizing in-group and out-group members across multiple social categories. Participants assigned to an arbitrary team (i.e., Eagles or Rattlers) underwent fMRI while categorizing political and arbitrary in-group and out-group members. We found that frontoparietal control network exhibited repetition suppression in response to “identical in-group” (Democrat-Democrat or Eagles-Eagles) and “different in-group” (Eagles-Democrat or Democrat-Eagles) trials relative to “out-group/in-group trials” (Republican-Democrat or Rattler-Eagles). Specifically, the repetition suppression contrast map included bilateral superior parietal lobule, bilateral dorsolateral prefrontal cortex (DLPFC), and bilateral middle temporal gyrus. Participants who reported an increased tendency to join and value their social groups exhibited decreased repetition suppression in bilateral DLPFC. Comparison of our whole-brain repetition suppression map with an independently identified map of frontoparietal control network revealed 34.3% overlap. Social categorization requires recognizing both a target’s group membership but also the target’s orientation toward one’s self. Fittingly, we find that generalized social categorization engages a network that acts as a functional bridge between dorsal attentional (exogenously-oriented) and default mode (internally-oriented) networks. Group living confers significant advantages. By coordinating and cooperating with fellow in-group members, we reap numerous material and psychological benefits . Social categorization—categorizing people into their respective social groups along a given dimension 6 —is critical for the successful navigation of group life. It is perhaps not surprising then, that there is a growing literature examining the neural basis of social categorization. These previous investigations have revealed a great deal about which brain regions and networks respond more to specific in-group versus out-group targets; however, eta-squared SD
Interaction of acoustic beam with elastic structures

Xonspecular reflection from a two-layer curved elastic structure consisting of rubber bonded to the outer surface of a steel shell has been studied experimentally and theo­ retically. The reflected field, in a two-transducer experiment, is measured as a function of azimuthal scan angle. The bond rigidity between the rubber and steel is accounted for by a spring model. This model is incorporated into an asymptotic evaluation of the spectral reflection integrals, accounting for the receiver characteristics by electrome­ chanical reciprocity. Decreasing bond rigidity acts to weaken the leaky wave excited in the underlying steel shell and provides a method to assess bond integrity, while the damping loss of the rubber is found to be only a scale factor. A. disbond completely suppresses the leaky wave. In comparisons between experimental data and predictions, good agreement is generally seen, also at incident angles diflerent from phase-matched Lamb mode coupling. P.ACS Codes: 43.20Fn. 43.20Mv. 43.30Dr. 43.3. 5Zc
An automated approach to clustering with the framework suggested by Bradley, Fayyad and Reina

Clustering with the framework suggested by Bradley, Fayyad and Reina allows for great scalability. However, practical challenges appear when applying the framework. One of the challenges is to define model parameters. This includes defining the number of clusters (K). Understanding how parameter values affect the final clustering may be challenging even with insight into the algorithm. Automating the clustering would allow for a more widespread use. The research question is thus: How could an automated process for clustering with BFR be defined and what results could such a process yield? A tailored method for parameter optimization is suggested. This method is used with a new and computationally advantageous cluster validity index called population density index. Computing the widely used within set sum of squares error requires an additional pass over the data set. Computing population density index does not. The final step of the automated process is to cluster with the parameters generated in the process. The outcome of these clusterings are measured. The results present data collected over 100 identically defined automated processes. These results show that 97 % of the identified K-values falls within the range of the suggested optimal value ±2. The method for optimizing parameters clearly results in parameters that outperform randomized parameters. The suggested population density index has a correlation coefficient of 1.00 with the commonly used within set sum of square error in a 32-dimensional case. An automated process for clustering with BFR has been defined.
Short title: Resources for HTP in crops

32 High throughput phenotyping (HTP) platforms are capable of monitoring the 33 phenotypic variation of plants through multiple types of sensors such as RGB 34 cameras, hyperspectral sensors and computed tomography, which can be 35 associated with environmental and genotypic data. Because of the wide range of 36 information provided, HTP datasets represent a valuable asset to characterise crop 37 phenotypes. As HTP becomes widely employed with more tools and data being 38 released, it is important that researchers are aware of these resources and how they 39 can be applied to accelerate crop improvement. Researchers may exploit these 40 datasets either for phenotype comparison or employ them as a benchmark to assess 41 tool performance and to support the development of tools that are better at 42 generalising between different crops and environments. In this review, we describe 43 the use of image-based HTP for yield prediction, root phenotyping, development of 44 climate-resilient crops, detecting pathogen and pest infestation and quantitative trait 45 measurement. We emphasise the need for researchers to share phenotypic data, 46 and offer a comprehensive list of available datasets to assist crop breeders and tool 47 developers to leverage these resources in order to accelerate crop breeding. 48 49
SPECIFICITY OF MANUFACTURING PROCESS VALIDATION FOR DIAGNOSTIC SEROLOGICAL DEVICES

The aim of this research was to analyze recent scientific literature, as well as national and international legislature on manifacturing process validation of biopharmaceutical production, in particular devices for serological diagnostics. Technology validation in the field of medical devices for serological diagnostics is most influenced by the Technical Regulation for Medical Devices for in vitro Diagnostics State Standards of Ukraine (SSU) — SSU EN ISO 13485:2015 “Medical devices. Quality management system. Requirements for regulation”, SSU EN ISO 14971:2015 “Medical devices. Instructions for risk management”, Instruction ST-N of the Ministry of Health of Ukraine 42-4.0:2014 “Medications. Suitable industrial practice”, State Pharmacopoeia of Ukraine and Instruction ICH Q9 on risk management. Current recommendations for validations of drugs manufacturing process, including biotechnological manufacturing, can not be directly applied to medical devices for in vitro diagnostics. It was shown that the specifics of application and raw materials require individual validation parameters and process validations for serological diagnostics devices. Critical parameters to consider in validation plans were provided for every typical stage of production of in vitro diagnostics devices on the example of immunoassay kits, such as obtaining protein antigens, including recombinant ones, preparations of mono- and polyclonal antibodies, immunoenzyme conjugates and immunosorbents, chemical reagents etc. The bottlenecks of technologies for in vitro diagnostics devices were analyzed from the bioethical and biosafety points of view.
Electronic monitoring of small arthropods in closed mesocosms Detailed presentation of a practical tool

The study of small animals in habitat containing coarse substrates of similar granularity (soil, manure, dust.) is hampered by the great difficulties in monitoring the daily activity and demographic dynamics of populations in their environment. However, this is a major issue to improve the management of wingless hematophagous vectors, to progress understanding the role of microarthropods in humus formation as well as in fundamental understanding foraging behaviors and trophic interactions in social insects. The use of experimental units such as mesocosms or experimental anthills provides information on general effects of the groups’ activity, but does not allow to unravel processes. On the other hand, recently developed electronic tracking tools allow the study of animal behavior in vitro . We have designed a new mixed experimental system that allows observation of both the development of invertebrate populations in environments similar to their natural environment and continuous electronic monitoring of the activity of individuals at one or more strategic points, including over several generations. The electronic tracking system can be replicated, so as to monitor several units and/or several points per unit at the same time.
High-Throughput Genotyping for a Polymorphism Linked to Soybean Cyst Nematode Resistance Gene <i>Rhg4</i> by Using TaqMan<sup>TM</sup> Probes

An individual soybean breeder can generate over one hundred thousand new genotypes each year. The efficiency of selection in these populations could be improved if these genotypes were effectively screened with one DNA marker that identified an important gene, and if laboratory throughput was high and costs were low. Our aim was to develop a rapid genotyping procedure for resistance to the soybean cyst nematode. A high-throughput genotyping method was developed with fluorogenic probes to distinguish between two insertion polymorphisms in alleles of an AFLP marker that is located about 50 kbp from the Rhg4 gene candidate. The assay uses the 50 exonuclease activity of Taq polymerase in conjunction with fluorogenic probes for each allele. The method can be used for scoring the polymorphism in a recombinant inbred line population and for screening parent lines in a breeding program. The TaqManTM method of determining genotype was accurate in 90% of scores in the RIL population compared to 95% accuracy with electrophoresis. Among 94 cultivars that are parents in our breeding program allele 2 that is derived from the sources of resistance to SCN was common in resistant cultivars (30 of 56) but rare in M E K S E M , E T A L . , M O L E C U L A R B R E E D I N G 7 (2 0 0 1 ) 2 susceptible cultivars (3 of 38). Therefore, this method can be applied to automated largescale genotyping for soybean breeding programs.