Miracle-dz/UniFAR-FTUs huggingface dataset:
Accessible Color Sequences for Data Visualization

Color sequences, ordered sets of colors for data visualization, that balance aesthetics with accessibility considerations are presented. In order to model aesthetic preference, data were collected with an online survey, and the results were used to train a machine-learning model. To ensure accessibility, this model was combined with minimum-perceptual-distance constraints, including for simulated color-vision deficiencies, as well as with minimum-lightness-distance constraints for grayscale printing, maximum-lightness constraints for maintaining contrast with a white background, and scores from a color-saliency model for ease of use of the colors in verbal and written descriptions. Optimal color sequences containing six, eight, and ten colors were generated using the data-driven aesthetic-preference model and accessibility constraints. Due to the balance of aesthetics and accessibility considerations, the resulting color sequences can serve as reasonable defaults in data-plotting codes, e.g., for use in scatter plots and line plots.
An Optimal potential-based hedging algorithm

Bounded regret algorithms have been a subject of intensive study. Several algorithms have been shown to have regret bounds of the form C √ T ln N where T is The backwards induction method due to Bellman [3] is a popular approach to solving problems in optimiztion, optimal control, and many other areas of applied math. In this paper we analyze the backwords induction approach, under min/max conditions. We show that if the value function is has strictly positive derivatives of order 1-4 then the optimal strategy for the adversary is Brownian motion. Using that fact we analyze diﬀerent potential functions and show that the Normal-Hedge potential is optimal.
Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models

Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge. There have been several proposals for efficient and robust visual representation learning among vision research practitioners, especially in the sensitive and critical biomedical domain. In this paper, we propose an idea for out-of-distribution generalization of chest X-ray pathologies that uses a simple balanced batch sampling technique. We observed that balanced sampling between the multiple training datasets improves the performance over baseline models trained without balancing.
MedShift: identifying shift data for medical dataset curation

To curate a high-quality dataset, identifying data variance between the internal and external sources is a fundamental and crucial step. However, methods to detect shift or variance in data have not been significantly researched. Challenges to this are the lack of effective approaches to learn dense representation of a dataset and difficulties of sharing private data across medical institutions. To overcome the problems, we propose a unified pipeline called MedShift to detect the top-level shift samples and thus facilitate the medical curation. Given an internal dataset A as the base source, we first train anomaly detectors for each class of dataset A to learn internal distributions in an unsupervised way. Second, without exchanging data across sources, we run the trained anomaly detectors on an external dataset B for each class. The data samples with high anomaly scores are identified as shift data. To quantify the shiftness of the external dataset, we cluster B's data into groups class-wise based on the obtained scores. We then train a multi-class classifier on A and measure the shiftness with the classifier's performance variance on B by gradually dropping the group with the largest anomaly score for each class. Additionally, we adapt a dataset quality metric to help inspect the distribution differences for multiple medical sources. We verify the efficacy of MedShift with musculoskeletal radiographs (MURA) and chest X-rays datasets from more than one external source. Experiments show our proposed shift data detection pipeline can be beneficial for medical centers to curate high-quality datasets more efficiently. An interface introduction video to visualize our results is available at https://youtu.be/V3BF0P1sxQE.
Towards a conjecture of Birmelé–Bondy–Reed on the Erdős–Pósa property of long cycles

A conjecture of Birmelé, Bondy, and Reed states that for any integer ℓ ≥ 3 $\ell \ge 3$ , every graph G $G$ without two vertex‐disjoint cycles of length at least ℓ $\ell $ contains a set of at most ℓ $\ell $ vertices which meets all cycles of length at least ℓ $\ell $ . They showed the existence of such a set of at most 2 ℓ + 3 $2\ell +3$ vertices. This was improved by Meierling, Rautenbach, and Sasse to 5 ℓ ∕ 3 + 29 ∕ 2 $5\ell \unicode{x02215}3+29\unicode{x02215}2$ . Here we present a proof showing that at most 3 ℓ ∕ 2 + 7 ∕ 2 $3\ell \unicode{x02215}2+7\unicode{x02215}2$ vertices suffice.
Literature Review of the Pioneering Approaches in Cloud-based Search Engines Powered by LETOR Techniques

Search engines play an essential role in our daily lives. Nonetheless, they are also very crucial in enterprise domain to access documents from various information sources. Since traditional search systems index the documents mainly by looking at the frequency of the occurring words in these documents, they are barely able to support natural language search, but rather keyword search. It seems that keyword based search will not be sufficient for enterprise data which is growing extremely fast. Thus, enterprise search becomes increasingly critical in corporate domain. In this report, we present an overview of the state-of-the-art technologies in literature for three main purposes: i) to increase the retrieval performance of a search engine, ii) to deploy a search platform to a cloud environment, and iii) to select the best terms in expanding queries for achieving even a higher retrieval performance as well as to provide good query suggestions to its users for a better user experience.
Determining the impact of distributed generation on power systems. I. Radial distribution systems

Distributed generation (DG) has much potential to improve distribution system performance and it should be encouraged. However, distribution system designs and operating practices are normally based on radial power flows and this creates a special challenge to the successful introduction of distributed generation. This paper has described a few of the issues that must be considered to insure that DG will not degrade distribution system power quality, safety or reliability. This paper focused on radial systems, although some of the issues discussed are common to low voltage distribution networks.
SurFit: Learning to Fit Surfaces Improves Few Shot Learning on Point Clouds

We present S UR F IT , a simple approach for label efﬁcient learning of 3D shape segmentation networks. S UR F IT is based on a self-supervised task of decomposing the surface of a 3D shape into geometric primitives. It can be readily applied to existing network architectures for 3D shape segmentation, and improves their performance in the few-shot setting, as we demonstrate in the widely used ShapeNet and PartNet benchmarks. S UR F IT outperforms the prior state-of-the-art in this setting, suggesting that decomposability into primitives is a useful prior for learning representations predictive of semantic parts. We present a number of experiments varying the choice of geometric primitives and down-stream tasks to demonstrate the effectiveness of the method.
Sequential Episodic Control

State of the art deep reinforcement learning algorithms are sample inefficient due to the large number of episodes they require to achieve asymptotic performance. Episodic Reinforcement Learning (ERL) algorithms, inspired by the mammalian hippocampus, typically use extended memory systems to bootstrap learning from past events to overcome this sample-inefficiency problem. However, such memory augmentations are often used as mere buffers, from which isolated past experiences are drawn to learn from in an offline fashion (e.g., replay). Here, we demonstrate that including a bias in the acquired memory content derived from the order of episodic sampling improves both the sample and memory efficiency of an episodic control algorithm. We test our Sequential Episodic Control (SEC) model in a foraging task to show that storing and using integrated episodes as event sequences leads to faster learning with fewer memory requirements as opposed to a standard ERL benchmark, Model-Free Episodic Control, that buffers isolated events only. We also study the effect of memory constraints and forgetting on the sequential and non-sequential version of the SEC algorithm. Furthermore, we discuss how a hippocampal-like fast memory system could bootstrap slow cortical and subcortical learning subserving habit formation in the mammalian brain.
The full rank condition for sparse random matrices

We derive a sufficient condition for a sparse random matrix with given numbers of non-zero entries in the rows and columns having full row rank. The result covers both matrices over finite fields with independent non-zero entries and $\{0,1\}$-matrices over the rationals. The sufficient condition is generally necessary as well.
Socially-Optimal Mechanism Design for Incentivized Online Learning

Multi-arm bandit (MAB) is a classic online learning framework that studies the sequential decision-making in an uncertain environment. The MAB framework, however, overlooks the scenario where the decision-maker cannot take actions (e.g., pulling arms) directly. It is a practically important scenario in many applications such as spectrum sharing, crowdsensing, and edge computing. In these applications, the decision-maker would incentivize other selfish agents to carry out desired actions (i.e., pulling arms on the decision-maker’s behalf). This paper establishes the incentivized online learning (IOL) framework for this scenario. The key challenge to design the IOL framework lies in the tight coupling of the unknown environment learning and asymmetric information revelation. To address this, we construct a special Lagrangian function based on which we propose a socially-optimal mechanism for the IOL framework. Our mechanism satisfies various desirable properties such as agent fairness, incentive compatibility, and voluntary participation. It achieves the same asymptotic performance as the state-of-art benchmark that requires extra information. Our analysis also unveils the power of crowd in the IOL framework: a larger agent crowd enables our mechanism to approach more closely the theoretical upper bound of social performance. Numerical results demonstrate the advantages of our mechanism in large-scale edge computing.
Random Thinning of Segmented Annular Arrrays

Two-dimensional (2D) array designs based on a squared matrix (S-M) distribution of elements require half wavelength (λ/2) pitch for reducing grating lobes. From this condition, a 2D array for ultrasonic imaging will tipically contain several thousand of elements, which are much higher than the number of channels of present image systems. In contrast, segmented-annular (S-A) arrays have lower periodicity than squared patterns and allow increasing the inter-element distance surpassing one wavelength. Thus, the number of elements of the S-A array can be reduced by a factor of six with respect to the S-M array of equal size. However, for large apertures, this number is still a challenge for the existent technology, and thinning techniques are required. In this work, the results of applying the random thinning method to S-A arrays are analysed based on two different approaches: the array factor on the one hand, which considers omni-directional elements vibrating in continuous wave, and the real aperture in wide band on the other hand. Moreover, a comparison with the conventional S-M array of equivalent characteristics is also included. It is shown that the S-A array with pitch d=1. 2λ overcomes the equivalent S-M array, producing beams which can be acceptable for ultrasonic image.
Restricting the parameter space of type-II two-Higgs-doublet models with 
CP
 violation

We explore the parameter space of the Type-II Two Higgs Doublet Model with softly broken Z 2 symmetry, allowing for CP violation in the scalar potential. Imposing theory-motivated and experimentally-driven constraints, we show that as the CP-violating phases are increased, only small regions of parameter space survive, including regions slightly away from the alignment limit. Electroweak oblique parameters and electric dipole moments emerge as most restrictive constraints. We show that imposing these constraints (as well as theoretical bounds such as perturbativity) the masses of the charged and heavy neutral Higgs, unlike in the CP conserving case, are bound from above and below. In particular, the heavy neutral Higgs masses are almost degenerate. In this parameter space region we highlight the relevant decay signals of the heavy neutral Higgs, involving both Zh and WW/ZZ , indicative of CP-violation in the model.
Geometric representation of classes of concave functions and duality

Using a natural representation of a $1/s$-concave function on $\mathbb{R}^d$ as a convex set in $\mathbb{R}^{d+1},$ we derive a simple formula for the integral of its $s$-polar. This leads to convexity properties of the integral of the $s$-polar function with respect to the center of polarity. In particular, we prove that that the reciprocal of the integral of the polar function of a log-concave function is log-concave as a function of the center of polarity. Also, we define the Santal\'o regions for $s$-concave and log-concave functions and generalize the Santal\'o inequality for them in the case the origin is not the Santal\'o point.
Expected hypervolume improvement for simultaneous multi-objective and multi-fidelity optimization

Bayesian optimization has proven to be an efﬁcient method to optimize expensive-to-evaluate systems. However, depending on the cost of single observations, multi-dimensional optimizations of one or more objectives may still be pro-hibitively expensive. Multi-ﬁdelity optimization remedies this issue by including multiple, cheaper information sources such as low-resolution approximations in numerical simulations. Acquisition functions for multi-ﬁdelity optimization are typically based on exploration-heavy algorithms that are difﬁcult to combine with optimization towards multiple objectives. Here we show that the expected hypervolume improvement policy can act in many situations as a suitable substitute. We incorporate the evaluation cost either via a two-step evaluation or within a single acquisition function with an additional ﬁdelity-related objective. This permits simultaneous multi-objective and multi-ﬁdelity optimization, which allows to accurately establish the Pareto set and front at fractional cost. Benchmarks show a cost reduction of an order of magnitude or more. Our method thus allows for Pareto optimization of extremely expansive black-box functions. The presented methods are simple and straightforward to implement in existing, optimized Bayesian optimization frameworks and can immediately be extended to batch optimization. The techniques can also be used to combine different continuous and/or discrete ﬁdelity dimensions, which makes them particularly relevant for simulation problems in plasma physics, ﬂuid dynamics and many other branches of scientiﬁc computing.
Study of disaster relief goods dispatching model and its intelligent solution approach for state reserve system of rescue goods and materials

According to practical organization of disaster relief goods and materials dispatching activities in China, a novel relief goods and materials dispatching model for multi-depot and multi-disaster area is established. This model is based on the multi-level depot priority model and is formalized as a multi-objective optimization problem with the earliest emergency start time and the least number of participated depots. To approach this model, the particle swarm optimization algorithm is adopted. We also integrated this model into the disaster relief goods dispatching application successfully and applied for programming the simulated dispatching plan for Yushu earthquake disaster. The results prove the efficiency of the proposed model and its intelligent solution approach.
Forking Around: Correlation of forking practices with the success of a project

Forking-based development has made it easier and straightforward for developers to contribute to open-source software (OSS). Developers can fork an existing project and add changes in their local version without interrupting the development process in the main project. Despite the efficiency of OSS, more than 80% of the projects are not sustainable. Identifying the elements related to OSS success can enlighten developers regarding the sustainability of a project. In our study, we explore whether or not the inefficiencies which arise due to forking-based development like redundant development, fragmented communities, lack of modularity, etc. have any relation to the outcome of a project in terms of sustainability. We formulate eight metrics to quantify attributes for projects in the ASFI dataset. To find the correlation between the metrics and the success of a project, we built a logistic regression model to metrics with significant p-values and performed backward stepwise regression analysis, using the stepAIC function in R to cross-check our findings. The findings show that modularity, centralized management index, and hard forks are consequential for the success of a project. Developers can use the outcomes of our research to plan and structure their projects to increase the probability of their success.
Microstrip antennas for SAR applications

This paper discusses various methods of implementing a shared-aperture dual-frequency dual-polarized array antenna for spaced-based synthetic aperture radar (SAR) applications. After evaluating the use of several potential array architecture concepts and radiating elements, a design using interlaced C-band microstrip patches and X-band printed slot elements was chosen as the best choice for the present system requirements. Layout considerations for the two arrays and their associated feed networks are addressed in terms of a practical design. A dual-frequency (C- and X-band), dual-linear polarized SAR array antenna prototype was designed, fabricated, and tested. The principal goal of this effort was to demonstrate the viability of the dual-band dual-polarized array concept, and this has been accomplished. Test results are shown with good correlation between measured and predicted results, validating the design approach used. This work demonstrates that a dual-frequency dual-polarization SAR antenna within a single aperture is a feasible approach to meeting user requirements in future SAR spacecraft.
Altitude Optimization of UAV Base Stations from Satellite Images Using Deep Neural Network

It is expected that unmanned aerial vehicles (UAVs) will play a vital role in future communication systems. Optimum positioning of UAVs, serving as base stations, can be done through extensive field measurements or ray tracing simulations when the 3D model of the region of interest is available. In this paper, we present an alternative approach to optimize UAV base station altitude for a region. The approach is based on deep learning; specifically, a 2D satellite image of the target region is input to a deep neural network to predict path loss distributions for different UAV altitudes. The predicted path distributions are used to calculate the coverage in the region; and the optimum altitude, maximizing the coverage, is determined. The neural network is designed and trained to produce multiple path loss distributions in a single inference; thus, it is not necessary to train a separate network for each altitude.
Celastrol-conjugated chitosan oligosaccharide for the treatment of pancreatic cancer

Abstract Celastrol is a promising antitumor drug candidate, but the poor water solubility and cytotoxicity limit its clinical application. Herein, we synthesized a Celastrol (Cel)-chitosan oligosaccharide (CSO) conjugate (Cel-CSO) for drug delivery. Celastrol was conjugated to a CSO backbone via amide bond formation, which was verified by infrared spectrum (IR) analyses. The Cel-CSO contained ∼10 wt% of Celastrol showed excellent aqueous solubility (18.6 mg/mL) in comparation with the parent Celastrol. Cel-CSO significantly inhibited tumor growth, induced apoptosis, and effectively suppressed tumor metastasis in human pancreatic cancer cells (BxPC-3). While the cytotoxicity of Cel-CSO in hepatic cells (HL7702) was lower than that of the free Celastrol. Cel-CSO enhanced the anticancer efficacy, promoted the circulation time of Celastrol, and reduced the subacute toxicity, which indicated that CSO can be a promising Celastrol delivery system for pancreatic cancer therapy.
The effectiveness of preoperative education interventions on improving perioperative outcomes of adult patients undergoing cardiac surgery: a systematic review and meta-analysis.

BACKGROUND Cardiac surgeries pose as an emotional experience for patients. Preoperative education is known to positively alter people's perceptions, emotions, and mitigate surgical distress. However, this intervention's effectiveness in improving perioperative outcomes among patients undergoing cardiac surgery lacked rigorous statistical synthesis and remains inconclusive. AIMS The aim was to synthesize the effectiveness of preoperative education on improving perioperative outcomes [anxiety, depression, knowledge, pain intensity, pain interference with daily activities, postoperative complications, length of hospitalization, length of intensive care unit (ICU) stay, satisfaction with the intervention and care, and health-related quality of life] among patients undergoing cardiac surgery. METHODS This systematic review and meta-analysis conducted a comprehensive search of nine electronic databases (PubMed, EMBASE, Scopus, MEDLINE, CINAHL, Cochrane CENTRAL, Web of Science, PsycINFO, and ERIC) and grey literature for randomized controlled trials examining the preoperative educational interventional effects on patients undergoing cardiac surgery from inception to 31 December 2020. The studies' quality was evaluated using Cochrane Risk-of-Bias Tool 1 (RoB1). Meta-analyses via RevMan 5.4 software synthesized interventional effects. RESULTS Twenty-two trials involving 3167 participants were included. Preoperative education had large significant effects on reducing post-intervention preoperative anxiety (P = 0.02), length of ICU stay (P = 0.02), and improving knowledge (P < 0.00001), but small significant effect sizes on lowering postoperative anxiety (P < 0.0001), depression (P = 0.03), and enhancing satisfaction (P = 0.04). CONCLUSIONS This review indicates the feasibility of preoperative education in clinical use to enhance health outcomes of patients undergoing cardiac surgery. Future studies need to explore knowledge outcomes in-depth and more innovative technologies in preoperative education delivery.
10. Cardiovascular Disease and Risk Management: Standards of Medical Care in Diabetes-2022.

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes. org/SOC.