Miracle-dz/UniFAR-FTUs huggingface dataset:
Keep Your Eye on the Best: Contrastive Regression Transformer for Skill Assessment in Robotic Surgery

This letter proposes a novel video-based, contrastive regression architecture, Contra-Sformer, for automated surgical skill assessment in robot-assisted surgery. The proposed framework is structured to capture the differences in the surgical performance, between a test video and a reference video which represents optimal surgical execution. A feature extractor combining a spatial component (ResNet-18), supervised on frame-level with gesture labels, and a temporal component (TCN), generates spatio-temporal feature matrices of the test and reference videos. These are then fed into an action-aware Transformer with multi-head attention that produces inter-video contrastive features at frame level, representative of the skill similarity/deviation between the two videos. Moments of sub-optimal performance can be identified and temporally localized in the obtained feature vectors, which are ultimately used to regress the manually assigned skill scores. Validated on the JIGSAWS dataset, Contra-Sformer achieves competitive performance (Spearman 0.65–0.89), with a normalized mean absolute error between 5.8%-13.4% on all tasks and across validation setups.
Symmetry-based algorithms to relate partial differential equations: II. Linearization by nonlocal symmetries

An algorithm is presented to linearize nonlinear partial differential equations by non-invertible mappings. The algorithm depends on finding nonlocal symmetries of the given equations which are realized as appropriate local symmetries of a related auxiliary system. Examples include the Hopf-Cole transformation and the linearizations of a nonlinear heat conduction equation, a nonlinear telegraph equation, and the Thomas equations.
Lightweight Network For Video Motion Magnification

Video motion magnification provides information to understand the subtle changes present in objects for applications like industrial, healthcare, sports, etc. Most state-of- the-art (SOTA) methods use hand-crafted bandpass filters, which require prior information for the motion magnification, produces ringing artifacts, and small magnification etc. While others use deep-learning based techniques for higher magnification, but their output suffers from artificially induced motion, distortions, blurriness, etc. Further, SOTA methods are computationally complex, which makes them less suitable for real-time applications. To address these problems, we proposed deep learning based simple yet effective solution for motion magnification. The proposed method uses a feature sharing and appearance encoder for better motion magnification with fewer distortions, artifacts etc. Additionally, for reducing magnification of noise and other unwanted changes, proxy-model based training is proposed. A computationally lightweight model (~0.12 M parameters) is proposed along with the base model. The performance of the proposed models is tested qualitatively and quantitatively, with the SOTA methods. Results demonstrate the effectiveness of the proposed lightweight and base model over the existing SOTA methods.
Synthesis and Anti-Melanoma Activity of L-Cysteine-Coated Iron Oxide Nanoparticles Loaded with Doxorubicin

In this study, we report on the synthesis of L-Cysteine (L-Cys)-coated magnetic iron oxide nanoparticles (NPs) loaded with doxorubicin (Dox). The Fe3O4-L-Cys-Dox NPs were extensively characterized for their compositional and morpho-structural features using EDS, SAED, XRD, FTIR and TEM. XPS, Mössbauer spectroscopy and SQUID measurements were also performed to determine the electronic and magnetic properties of the Fe3O4-L-Cys-Dox nanoparticles. Moreover, by means of a FO-SPR sensor, we evidenced and confirmed the binding of Dox to L-Cys. Biological tests on mouse (B16F10) and human (A375) metastatic melanoma cells evidenced the internalization of magnetic nanoparticles delivering Dox. Half maximum inhibitory concentration IC50 values of Fe3O4-L-Cys-Dox were determined for both cell lines: 4.26 µg/mL for A375 and 2.74 µg/mL for B16F10, as compared to 60.74 and 98.75 µg/mL, respectively, for unloaded controls. Incubation of cells with Fe3O4-L-Cys-Dox modulated MAPK signaling pathway activity 3 h post-treatment and produced cell cycle arrest and increased apoptosis by 48 h. We show that within the first 2 h of incubation in physiological (pH = 7.4) media, ~10–15 µM Dox/h was released from a 200 µg/mL Fe3O4-L-Cys-Dox solution, as compared to double upon incubation in citrate solution (pH = 3), which resembles acidic environment conditions. Our results highlight the potential of Fe3O4-L-Cys-Dox NPs as efficient drug delivery vehicles in melanoma therapy.
The C-FEWS framework: Supporting studies of climate-induced extremes on food, energy, and water systems at the regional scale

Climate change continues to challenge food, energy, and water systems (FEWS) across the globe and will figure prominently in shaping future decisions on how best to manage this nexus. In turn, traditionally engineered and natural infrastructures jointly support and hence determine FEWS performance, their vulnerabilities, and their resilience in light of extreme climate events. We present here a research framework to advance the modeling, data integration, and assessment capabilities that support hypothesis-driven research on FEWS dynamics cast at the macro-regional scale. The framework was developed to support studies on climate-induced extremes on food, energy, and water systems (C-FEWS) and designed to identify and evaluate response options to extreme climate events in the context of managing traditionally engineered (TEI) and nature-based infrastructures (NBI). This paper presents our strategy for a first stage of research using the framework to analyze contemporary FEWS and their sensitivity to climate drivers shaped by historical conditions (1980–2019). We offer a description of the computational framework, working definitions of the climate extremes analyzed, and example configurations of numerical experiments aimed at evaluating the importance of individual and combined driving variables. Single and multiple factor experiments involving the historical time series enable two categories of outputs to be analyzed: the first involving biogeophysical entities (e.g., crop production, carbon sequestered, nutrient and thermal pollution loads) and the second reflecting a portfolio of services provided by the region’s TEI and NBI, evaluated in economic terms. The framework is exercised in a series of companion papers in this special issue that focus on the Northeast and Midwest regions of the United States. Use of the C-FEWS framework to simulate historical conditions facilitates research to better identify existing FEWS linkages and how they function. The framework also enables a next stage of analysis to be pursued using future scenario pathways that will vary land use, technology deployments, regulatory objectives, and climate trends and extremes. It also supports a stakeholder engagement effort to co-design scenarios of interest beyond the research domain.
CMOS Radio Frequency Energy Harvester (RFEH) with Fully On-Chip Tunable Voltage-Booster for Wideband Sensitivity Enhancement

Radio frequency energy harvesting (RFEH) is one form of renewable energy harvesting currently seeing widespread popularity because many wireless electronic devices can coordinate their communications via RFEH, especially in CMOS technology. For RFEH, the sensitivity of detecting low-power ambient RF signals is the utmost priority. The voltage boosting mechanisms at the input of the RFEH are typically applied to enhance its sensitivity. However, the bandwidth in which its sensitivity is maintained is very poor. This work implements a tunable voltage boosting (TVB) mechanism fully on-chip in a 3-stage cross-coupled differential drive rectifier (CCDD). The TVB is designed with an interleaved transformer architecture where the primary winding is implemented to the rectifier, while the secondary winding is connected to a MOSFET switch that tunes the inductance of the network. The TVB enables the sensitivity of the rectifier to be maintained at 1V DC output voltage with a minimum deviation of −2 dBm across a wide bandwidth of 3 to 6 GHz of 5G New Radio frequency (5GNR) bands. A DC output voltage of 1 V and a peak PCE of 83% at 3 GHz for −23 dBm input power are achieved. A PCE of more than 50% can be maintained at the sensitivity point of 1 V with the aid of TVB. The proposed CCDD-TVB mechanism enables the CMOS RFEH to be operated for wideband applications with optimum sensitivity, DC output voltage, and efficiency.
Multi-Agent Credit Assignment and Bankruptcy Game for Improving Resource Allocation in Smart Cities

In recent years, the development of smart cities has accelerated. There are several issues to handle in smart cities, one of the most important of which is efficient resource allocation. For the modeling of smart cities, multi-agent systems (MASs) can be used. In this paper, an efficient approach is proposed for resource allocation in smart cities based on the multi-agent credit assignment problem (MCA) and bankruptcy game. To this end, the resource allocation problem is mapped to MCA and the bankruptcy game. To solve this problem, first, a task start threshold (TST) constraint is introduced. The MCA turns into a bankruptcy problem upon introducing such a constraint. Therefore, based on the concept of bankruptcy, three methods of TS-Only, TS + MAS, and TS + ExAg are presented to solve the MCA. In addition, this work introduces a multi-score problem (MSP) in which a different reward is offered for solving each part of the problem, and we used it in our experiments to examine the proposed methods. The proposed approach is evaluated based on the learning rate, confidence, expertness, efficiency, certainty, and correctness parameters. The results reveal the better performance of the proposed approach compared to the existing methods in five parameters.
HUSS: A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

ABSTRACT Spreadsheets contain a lot of valuable data and have many practical applications. The key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets, e.g., identifying cell function types and discovering relationships between cell pairs. Most existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells. A few studies do, but they ignore the layout structure information of spreadsheets, which affects the performance of cell function classification and the discovery of different relationship types of cell pairs. In this paper, we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets (HUSS). Specifically, for improving the cell function classification, we propose an error correction mechanism (ECM) based on an existing cell function classification model [11] and the layout features of spreadsheets. For improving the table structure analysis, we propose five types of heuristic rules to extract four different types of cell pairs, based on the cell style and spatial location information. Our experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.
Food Donations, Retail Operations, and Retail Pricing

Problem definition: For grocery retailers, managing perishable food that is nearing expiry is a major challenge. Donating food to food banks is socially responsible, as it improves local communities and reduces waste generation. It also diverts food to a secondary, quality-differentiated market. Academic/practical relevance: In this paper, we quantify the economic impacts of this secondary market for food by examining donation and pricing behaviors for competing retailers. Methodology: We use a structural model of price-discriminating oligopoly retailers to study the effect of food donations on store and category-level demand and equilibrium prices. Empirically, we estimate the food donation effect using a unique data set of food donations and sales for several categories of perishable foods across two major retail chains that compete in the same market. Results: The competitive effects of food donations follow from the price-discrimination logic. First, food donations create a direct demand effect. Donations raise the average quality of products on display, shifting demand curves inward and rotating them clockwise (e.g., more inelastic). Second, food donations create a market enhancement effect, softening price competition and raising equilibrium prices among competing retailers. Managerial implications: Food donations increase food prices and store profits, tying the socially responsible option to an economic benefit. This study contributes a new type of reuse operation to the literature on closed-loop supply chains. Funding: Funding from the Agriculture and Food Research Initiative (National Institute for Food and Agriculture, U.S. Department of Agriculture) [Grant 2020-67021-31377] is acknowledged. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022. 1185 .
Failure of global regularity of ∂b on a convex domain with only one flat point

In this paper we exhibit a bounded domain in C 2 with real analytic boundary which is strictly convex except at one point and for which the @b operator is not analytic hypoelliptic modulo its kernel. The importance of such an example is twofold: First it shows that the theorem of Boas and Straube on global C 1 regularity for @b on convex domains cannot be extended to the analytic case; secondly it is the rst example of non analytic hypoellipticity of @b on a domain with isolated weakly pseudoconvex points in the boundary.
Predicting Bike-Sharing Demand Using Random Forest

Being able to accurately predict bike-sharing demand is important for Intelligent Transport Systems and traveler information systems. These challenges have been addressed in a number of cities worldwide. This article uses Random Forest (RF) and k-fold cross-validation to predict the hourly count of rental bikes (cnt/h) in the city of Seoul (Korea) using information related to rental hour, temperature, humidity, wind speed, visibility, dewpoint, solar radiation, snowfall, and rainfall. The performance of the proposed RF model is evaluated using three statistical measurements: root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R). The results show that the RF model has high predictive accuracy with an RMSE of 210 cnt/h, an MAE of 121 cnt/h, and an R of 0.90. The performance of the RF model is also compared with a linear regression model and shows superior accuracy.
The Effect of Watermelon Juice Supplementation on Heart Rate Variability and Metabolic Response during an Oral Glucose Challenge: A Randomized, Double-Blind, Placebo-Controlled Crossover Trial

Heart rate variability (HRV) provides a simple method to evaluate autonomic function in health and disease. A reduction in HRV may indicate autonomic dysfunction and is strongly associated with aspects of cardiometabolic disease, including hyperglycemia. Reduced nitric oxide (NO) bioavailability is also implicated in the development of cardiometabolic disease and autonomic dysfunction. Watermelons are natural sources of L-arginine and L-citrulline, substrates used for NO synthesis. Watermelon consumption can improve NO bioavailability. We conducted a randomized, double-blind, placebo-controlled crossover trial to test the effects of 2 weeks of daily watermelon juice (WMJ) supplementation on HRV in response to an oral glucose challenge (OGC) in healthy young adults. We also performed indirect calorimetry to assess if our intervention altered the metabolic response to the OGC. WMJ supplementation preserved high-frequency power (HF) (treatment effect, p = 0.03) and the percentage of successive differences that differ by more than 50 ms (pNN50) (treatment effect, p = 0.009) when compared to the placebo treatment. There was no difference in resting energy expenditure or substate oxidation according to treatment. We report that WMJ supplementation attenuates OGC-induced reductions in HRV. Future work should emphasize the importance of NO bioavailability in autonomic dysfunction in cardiometabolic disease.
Generative and non-linear phonology

Part 1 Introduction: scope of this book from classical phonemics to generative phonology phonemes or features? levels of representation aspects of a standard generative analysis of Midi French phonology within the model of grammar. Part 2 The theory of distinctive features: some general assumptions the phonetic features and their articulatory correlates universalism revisited the acoustic/auditory basis of DFs invariance and distinctive features. Part 3 Binarism, full and partial specification, markedness and gestures: binarism multivalued features contrastivity, archiphonemes and redundancy rules markedness theory gestures. Part 4 The derivational issue - aspects of the abstractness-concreteness debate: aspects of the segmental phonology of English objections to the vowel shift and velar softening natural generative phonology (NGP) in defence of the vowel shift. Part 5 Underspecification theory and lexical phonology: underspecification theory (UT) Yawelmani vowels and underspecification lexical phonology. Part 6 Metrical structures: syllable structure stress and prominence. Part 7 Autosegmental and multidimensional phonology: tone in the SPE framework the skeletal tier further geometrical extensions universal phonology and the "no rule" approach. Part 8 An outline of dependency phonology: suprasegmental representations infrasegmental representations back unrounded vowels. Appendix: phonetic symbols.
Detection of Silicates in the beta Pictoris Disk

We have detected a broad 10 μm emission feature within 3″ of the star β Pic which is known to have a circumstellar, possibly protoplanetary, disk. The observations were made using four narrow-band filters (Δλ ≃ 1 μm) in the MSFC bolometer array at the IRTF. The emission feature closely resembles silicate features observed in comets Halley and Kohoutek and in a variety of Galactic sources. The feature is strong, with the total flux density emitted by dust at 10.3 μm being at least twice the estimated continuum at that wavelength, and must be emitted by particles with characteristic radii less than 10 μm
Spatiotemporal Gait Analysis for Cardiovascular Disease

Gait analysis is a way of analyzing the subjects' walking pattern which has been discovered as an indicator for the identification of symptoms of the disease after among all the other clinical parameters. Gait disorder having no apparent reason is associated with cardiovascular disease and gait imbalance is the major factor for fall and fall related accidents. This study puts an effort to perform quantitative measurements of spatiotemporal gait parameters for the patients of cardiovascular disease using wearable sensor and explores the clinical symptoms from gait pattern. The research is carried out on 16 Healthy controls and 53 Cardiovascular patients. The result of the analysis presented that the gait parameters of the patients are greatly varied than that of Healthy Controls (HCs) which may serve as early indication of the symptoms of cardiovascular disease. This is a new idea in analyzing walking pattern of population suffering from different disease with different age-group and sex based on the various case studies in the same domain.
An LMI approach to the design of fault detection filter for time-delay LTI systems with unknown inputs

The robust fault detection problems for time-delay LTI systems with unknown inputs are studied. The basic idea of our study is to express the robustness as well as the sensitivity of residual signals to the unknown inputs as well as to the faults in terms of L/sub 2-/gain and, based on it, to formulate the design of fault detection filters as an optimization problem. Applying the H/sub /spl infin// optimization technique, an LMI approach to the solution of the optimization problem is then proposed. The main results consist of the derivation of the existence conditions of the fault detection filter for time-delay LTI systems and further the solution of the optimal design problem.
Local probabilistic descriptors for image categorisation

Image categorisation involves the well known difficulties with different visual appearances of a single object, but also introduces the problem of within-category variation. This within-category variation makes highly distinctive local descriptors less appropriate for categorisation. Difficulties because of the within category variation and clutter are tackled by modelling image fragments in a new manner. The authors propose a family of local image descriptors, called probabilistic patch descriptors (PPDs). PPDs encode the appearance of image fragments as well as their variability within a category. PPDs extend the usual local descriptors by also modelling the variance of the descriptors' elements, for example pixels or bins in a histogram. To compare two PPDs, and a PPD with an image, a new similarity measure called PPD matching score is introduced. For each object category, a set of representative PPDs is learnt. Images are represented as feature vectors of the best matching scores obtained for representative PPDs in images. Support vector machine classifiers are then trained on the feature vectors. PPDs are applied to image categorisation using machine learning where the features are the matching scores between images and PPDs. The authors experiment with two variants of PPDs that are based on complementary local descriptors. An interesting observation is that combining the two PPD variants improves the accuracy of categorisation. Experiments indicate that the benefits of modelling the within-category variation give results that are comparable with the state-of-the-art categorisation methods, and show good robustness with respect to noise and occlusions.
Origin of Retinal Oscillatory Potentials in the Mouse, a Tool to Specifically Locate Retinal Damage

To determine the origin of oscillatory potentials (OPs), binocular electroretinogram (ERG) recordings were performed under light and dark adaptation on adult healthy C57BL/6J mice. In the experimental group, 1 μL of PBS was injected into the left eye, while the right eye was injected with 1 μL of PBS containing different agents: APB, GABA, Bicuculline, TPMPA, Glutamate, DNQX, Glycine, Strychnine, or HEPES. The OP response depends on the type of photoreceptors involved, showing their maximum response amplitude in the ERG induced by mixed rod/cone stimulation. The oscillatory components of the OPs were affected by the injected agents, with some drugs inducing the complete abolition of oscillations (APB, GABA, Glutamate, or DNQX), whereas other drugs merely reduced the oscillatory amplitudes (Bicuculline, Glycine, Strychnine, or HEPES) or did not even affect the oscillations (TPMPA). Assuming that rod bipolar cells (RBC) express metabotropic Glutamate receptors, GABAA, GABAC, and Glycine receptors and that they release glutamate mainly on Glycinergic AII amacrine cells and GABAergic A17 amacrine cells, which are differently affected by the mentioned drugs, we propose that RBC-AII/A17 reciprocal synapses are responsible for the OP generation in the ERG recordings in the mice. We conclude that the reciprocal synapses between RBC and AII/A17 are the basis of the ERG OP oscillations of the light response, and this fact must be taken into consideration in any ERG test that shows a decrease in the OPs’ amplitude.