Miracle-dz/UniFAR-FTUs huggingface dataset:
Trauma Patient Transitions From Critical Care: A Survey of U.S. Trauma Centers

BACKGROUND: Transitions between clinical units are vulnerable periods for patients. A significant body of evidence describes the importance of structured transitions, but there is limited reporting of what happens. Describing transitions within a conceptual model will characterize the salient forces that interact during a patient transition and, perhaps, lead to improved outcomes. OBJECTIVE: To describe the processes and resources that trauma centers use to transition patients from critical care to nonintensive care units. METHODS: This cross-sectional study surveyed all Level I and II trauma centers listed in the American Trauma Society database from September 2020 to November 2020. Data were merged from the American Hospital Association 2018 Hospital Survey. RESULTS: A total of 567 surveys were distributed, of which 152 responded for a (27%) response rate. Results were organized in categories: capital input, organizational facets, employee behavior, employee terms/scope, and labor inputs. Resources and processes varied; the most important opportunities for transition improvement included: (1) handoff instruments were only reported at 36% (n = 27) of trauma centers, (2) mandatory resident education about transitions was only reported at 70% (n = 16) of trauma centers, and (3) only 6% (n = 4) of trauma centers reported electronic medical record applications that enact features to influence employee behavior. CONCLUSIONS: After years of focusing on transitions as a high-stake period, there remain many opportunities to develop resources and enact effective processes to address the variability in transition practice across trauma centers.
Vascular calcification progression and its association with mineral and bone disorder in kidney transplant recipients

Abstract Background The assessment and prevention of vascular calcification (VC) in kidney transplant recipients (KTRs) have not been systematically studied. We aimed to evaluate VC change one year after kidney transplantation (KT) and identify their influencing factors. Methods 95 KTRs (68 males; ages 40.2 ± 10.8 years) were followed one year after KT. Changes in bone mineral density (BMD) and bone metabolism biomarkers were assessed. Coronary artery calcification (CAC) and thoracic aortic calcification (TAC) were measured using 192-slice third-generation dual-source CT. The relationship between bone metabolism indicators and VC and the factors influencing VC were analyzed. Results Postoperative estimated glomerular filtration rate was 79.96 ± 24.18 mL/min*1.73 m2. One year after KT, serum phosphorus, intact parathyroid hormone (iPTH), osteocalcin, type I collagen N-terminal peptide (NTx), type I collagen C-terminal peptide, and BMD decreased, 25-hydroxyvitamin D remained low, and VC increased. Post-CAC and TAC were negatively correlated with pre-femoral neck BMD, and TAC was positively correlated with post-calcium. CAC and TAC change were positively correlated with post-calcium and 25-hydroxyvitamin D. Increased CAC was positively associated with hemodialysis and pre-femoral neck osteopenia. CAC change was positively associated with prediabetes, post-calcium, and pre-CAC and negatively associated with preoperative and postoperative femoral neck BMD, and NTx change. Increased TAC was positively associated with age, prediabetes, preoperative parathyroid hyperplasia/nodule, post-calcium, and post-femoral neck osteopenia. TAC change was positively associated with age, diabetes, pre-triglyceride, pre-TAC, dialysis time, post-calcium and post-iPTH, and negatively associated with post-femoral neck BMD. Conclusions Mineral and bone disorders persisted, and VC progressed after KT, showing a close relationship.
Syntheses and characterization of zeolites K-F and W type using a diatomite precursor

Abstract Zeolites K-F and W (EDI and MER types) were synthesized hydrothermally using a natural rock as raw material. Chemical treatments were carried out on a diatomitic rock (containing opaline silica) from Crotone (Calabria, Italy) in order to separate/obtain potassium silicate, a reagent necessary for synthesizing zeolites. Synthesis experiments were performed by mixing the obtained siliceous solution with potassium hydroxide and alumina in varying proportions at 150°C and room pressure. Four synthesis series were performed to form zeolite K-F (EDI) and zeolite W (MER). The chemical-physical and morphological characterization of the zeolite phases were carried out. Cell parameters were calculated using the Rietveld method. Infrared, thermal and nuclear magnetic resonance (29Si) experiments confirmed the high quality of the zeolite products. The amorphous phase in the synthesis powders was estimated with quantitative phase analysis using the combined Rietveld and reference intensity ratio methods.
Regulation of the heterochromatin spreading reaction by trans-acting factors

Heterochromatin is a gene-repressive protein–nucleic acid ultrastructure that is initially nucleated by DNA sequences. However, following nucleation, heterochromatin can then propagate along the chromatin template in a sequence-independent manner in a reaction termed spreading. At the heart of this process are enzymes that deposit chemical information on chromatin, which attracts the factors that execute chromatin compaction and transcriptional or co/post-transcriptional gene silencing. Given that these enzymes deposit guiding chemical information on chromatin they are commonly termed ‘writers’. While the processes of nucleation and central actions of writers have been extensively studied and reviewed, less is understood about how the spreading process is regulated. We discuss how the chromatin substrate is prepared for heterochromatic spreading, and how trans-acting factors beyond writer enzymes regulate it. We examine mechanisms by which trans-acting factors in Suv39, PRC2, SETDB1 and SIR writer systems regulate spreading of the respective heterochromatic marks across chromatin. While these systems are in some cases evolutionarily and mechanistically quite distant, common mechanisms emerge which these trans-acting factors exploit to tune the spreading reaction.
A Viscous Ergodic Problem with Unbounded and Measurable Ingredients, Part 1: HJB Equation

We address the problem of existence and uniqueness of solutions $(c,u(\cdot))$ to ergodic Hamilton-Jacobi-Bellman (HJB) equations of the form $H(x,\nabla u(x), D^{2}u(x)) = c$ in the whole space $\mathbb{R}^{m}$ with unbounded and merely measurable data and where $H$ is a Bellman Hamiltonian. The method we use is different from classical approaches. It relies on duality theory and optimization in abstract Banach spaces together with maximal dissipativity of the diffusion operator.
Superimposed Chirp Waveforms for SWIPT with Diplexer-based Integrated Receivers

In this paper, we present the superposition of chirp waveforms for simultaneous wireless information and power transfer (SWIPT) applications. Exploiting the chirp waveform characteristics enables us to superimpose multiple chirps, thereby allowing transmission of the same number of waveforms over less bandwidth. This enables us to perform subband selection when operating over set of orthogonal subbands. Furthermore, we consider a user equipped with a diplexer-based integrated receiver (DIR), which enables to extract radio frequency power and decode information from the same signal without splitting. Thereby, incorporating chirp superposition and subband selection, a transmission scheme is proposed to exploit both the diode's nonlinearity and frequency diversity. We derive novel closed-form analytical expressions of the average harvested energy (HE) via transmission of superimposed chirp over selected subbands based on tools from order statistics. We also analyze the downlink information rate achieved at the user. Through our analytical and numerical results, for the considered system setup, we show that superimposed chirp-based SWIPT provides an improvement of 30$\%$ in average HE performance as compared to multisine waveforms consisting of a set of fixed-frequency cosine signals, improves the minimum level of HE in a multiuser network, and extends the operating range of energy transfer as compared to fixed-frequency waveforms. Furthermore, we illustrate that the inclusion of DIR at the receiver for SWIPT enlarges the energy-information transfer region when compared to the widely considered power splitting receiver.
Bayesian Approach for Radial Kernel Parameter Tuning

In this paper we present a new fast and accurate method for Radial Basis Function (RBF) approximation, including interpolation as a special case, which enables us to effectively find the optimal value of the RBF shape parameter. In particular, we propose a statistical technique, called Bayesian optimization, that consists in modelling the error function with a Gaussian process, by which, through an iterative process, the optimal shape parameter is selected. The process is step by step self-updated resulting in a relevant decrease in search time with respect to the classical leave one out cross validation technique. Numerical results deriving from some test examples and an application to real data show the performance of the proposed method.
Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities

Deep learning has taken by storm all fields involved in data analysis, including remote sensing for Earth observation. However, despite significant advances in terms of performance, its lack of explainability and interpretability, inherent to neural networks in general since their inception, remains a major source of criticism. Hence it comes as no surprise that the expansion of deep learning methods in remote sensing is being accompanied by increasingly intensive efforts oriented towards addressing this drawback through the exploration of a wide spectrum of Explainable Artificial Intelligence techniques. This chapter, organized according to prominent Earth observation application fields, presents a panorama of the state-of-the-art in explainable remote sensing image analysis.
Determina\c{c}\~ao da Dist\^ancia \`a Grande Nuvem de Magalh\~aes Atrav\'es das Estrelas Vari\'aveis Cefeidas Dispon\'iveis no Cat\'alogo OGLE-IV

In this work, we discuss the determination of the distance to the Large Magellanic Cloud (LMC) using the Leavitt Law, utilizing the public catalog of Classical Cepheid Variable stars from the observational project OGLE-IV (The Optical Gravitational Lensing Experiment Collection of Variable Stars), consisting of 4709 stars in the Large Magellanic Cloud. To determine the pulsation period of Cepheid Variable stars, we employ the computational algorithm \textit{Lomb-Scargle periodogram} modified for our data. Additionally, with the calculation of the period, we can derive a period-luminosity relation for Cepheid Variables in the Large Magellanic Cloud and, using an independent calibration distance, deduce their distance moduli. We also discuss some general theoretical concepts of the physical mechanism behind the oscillation of variable stars.
Normalization properties of $\lambda\mu$-calculus using realizability semantics

In this paper, we present a general realizability semantics for the simply typed $\lambda\mu$-calculus. Then, based on this semantics, we derive both weak and strong normalization results for two versions of the $\lambda\mu$-calculus equipped with specific simplification rules. The novelty in our method, in addition to its more systematic approach, lies in its applicability to a broader set of reduction rules. Furthermore, we have obtained a surprising characterization of the typed $\lambda\mu$-terms with the help of our saturation properties.
Long-Term Effects of Municipal Wastewater Irrigation on Some Properties of a Semiarid Region Soil of Iran

To investigate the effect of long-term irrigation of wheat farms on the soil properties, a field study was carried out in Varamin Station located in southern Tehran, Iran. The soil samples were collected from the area with 29 years using wastewater irrigation and comparing the results with soil properties extracted from the previous control profile of the pedological study in 1981. Some soil properties of the samples such as electrical conductivity (EC), soil reaction (pH), organic carbon (OC), bulk density (pb), and saturate hydraulic conductivity (Ks) were measured for comparison. The obtained paired data were analyzed with T-test. The results showed that using wastewater significantly increased the soil organic carbon and bulk density in depth of 0-20 centimeters (p<0.05). Saturated hydraulic conductivity in depth of 0-20 centimeters and soil reaction in both depths of 0-20 and 20-40 centimeters significantly decreased (p<0.05). The reason may be due to filling of colloidal particles suspended in wastewater within the soil porosity. Wastewater quality and, possibly soil properties, appear to play an important role in the effect of using wastewater irrigation on the soil physical properties.
CloverLeaf on Intel Multi-Core CPUs: A Case Study in Write-Allocate Evasion

In this paper we analyze the MPI-only version of the CloverLeaf code from the SPEChpc 2021 benchmark suite on recent Intel Xeon"Ice Lake"and"Sapphire Rapids"server CPUs. We observe peculiar breakdowns in performance when the number of processes is prime. Investigating this effect, we create first-principles data traffic models for each of the stencil-like hotspot loops. With application measurements and microbenchmarks to study memory data traffic behavior, we can connect the breakdowns to SpecI2M, a new write-allocate evasion feature in current Intel CPUs. We identify conditions under which SpecI2M works as intended and where it fails to avoid write-allocate transfers. Write-allocate evasion works best if large arrays are written consecutively; in the CloverLeaf code, non-temporal stores can be employed on top for best results. For serial and full-node cases we are able to predict the memory data volume analytically with an error of a few percent. We find that if the number of processes is prime, SpecI2M fails to work properly, which we can attribute to short inner loops emerging from the one-dimensional domain decomposition in this case. We can also rule out other possible causes of the prime number effect, such as breaking layer conditions, MPI communication overhead, and load imbalance.
SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Store

In large-scale storehouses, precise instance masks are crucial for robotic bin picking but are challenging to obtain. Existing instance segmentation methods typically rely on a tedious process of scene collection, mask annotation, and network fine-tuning for every single Stock Keeping Unit (SKU). This paper presents SKU-Patch, a new patch-guided instance segmentation solution, leveraging only a few image patches for each incoming new SKU to predict accurate and robust masks, without tedious manual effort and model re-training. Technical-wise, we design a novel transformer-based network with (i) a patch-image correlation encoder to capture multi-level image features calibrated by patch information and (ii) a patch-aware transformer decoder with parallel task heads to generate instance masks. Extensive experiments on four storehouse benchmarks manifest that SKU-Patch is able to achieve the best performance over the state-of-the-art methods. Also, SKU-Patch yields an average of nearly 100% grasping success rate on more than 50 unseen SKUs in a robot-aided auto-store logistic pipeline, showing its effectiveness and practicality.
Kindness in Multi-Agent Reinforcement Learning

In human societies, people often incorporate fairness in their decisions and treat reciprocally by being kind to those who act kindly. They evaluate the kindness of others' actions not only by monitoring the outcomes but also by considering the intentions. This behavioral concept can be adapted to train cooperative agents in Multi-Agent Reinforcement Learning (MARL). We propose the KindMARL method, where agents' intentions are measured by counterfactual reasoning over the environmental impact of the actions that were available to the agents. More specifically, the current environment state is compared with the estimation of the current environment state provided that the agent had chosen another action. The difference between each agent's reward, as the outcome of its action, with that of its fellow, multiplied by the intention of the fellow is then taken as the fellow's"kindness". If the result of each reward-comparison confirms the agent's superiority, it perceives the fellow's kindness and reduces its own reward. Experimental results in the Cleanup and Harvest environments show that training based on the KindMARL method enabled the agents to earn 89\% (resp. 37\%) and 44% (resp. 43\%) more total rewards than training based on the Inequity Aversion and Social Influence methods. The effectiveness of KindMARL is further supported by experiments in a traffic light control problem.
Real-Time Recurrent Reinforcement Learning

In this paper we propose real-time recurrent reinforcement learning (RTRRL), a biologically plausible approach to solving discrete and continuous control tasks in partially-observable markov decision processes (POMDPs). RTRRL consists of three parts: (1) a Meta-RL RNN architecture, implementing on its own an actor-critic algorithm; (2) an outer reinforcement learning algorithm, exploiting temporal difference learning and dutch eligibility traces to train the Meta-RL network; and (3) random-feedback local-online (RFLO) learning, an online automatic differentiation algorithm for computing the gradients with respect to parameters of the network.Our experimental results show that by replacing the optimization algorithm in RTRRL with the biologically implausible back propagation through time (BPTT), or real-time recurrent learning (RTRL), one does not improve returns, while matching the computational complexity for BPTT, and even increasing complexity for RTRL. RTRRL thus serves as a model of learning in biological neural networks, mimicking reward pathways in the basal ganglia.
Computing pivot-minors

A graph $G$ contains a graph $H$ as a pivot-minor if $H$ can be obtained from $G$ by applying a sequence of vertex deletions and edge pivots. Pivot-minors play an important role in the study of rank-width. Pivot-minors have mainly been studied from a structural perspective. In this paper we perform the first systematic computational complexity study of pivot-minors. We first prove that the Pivot-Minor problem, which asks if a given graph $G$ contains a pivot-minor isomorphic to a given graph $H$, is NP-complete. If $H$ is not part of the input, we denote the problem by $H$-Pivot-Minor. We give a certifying polynomial-time algorithm for $H$-Pivot-Minor when (1) $H$ is an induced subgraph of $P_3+tP_1$ for some integer $t\geq 0$, (2) $H=K_{1,t}$ for some integer $t\geq 1$, or (3) $|V(H)|\leq 4$ except when $H \in \{K_4,C_3+ P_1\}$. Let ${\cal F}_H$ be the set of induced-subgraph-minimal graphs that contain a pivot-minor isomorphic to $H$. To prove the above statement, we either show that there is an integer $c_H$ such that all graphs in ${\cal F}_H$ have at most $c_H$ vertices, or we determine ${\cal F}_H$ precisely, for each of the above cases.
Bridging Dimensions: Confident Reachability for High-Dimensional Controllers

Autonomous systems are increasingly implemented using end-to-end learning-based controllers. Such controllers make decisions that are executed on the real system with images as one of the primary sensing modalities. Deep neural networks form a fundamental building block of such controllers. Unfortunately, the existing neural-network verification tools do not scale to inputs with thousands of dimensions -- especially when the individual inputs (such as pixels) are devoid of clear physical meaning. This paper takes a step towards connecting exhaustive closed-loop verification with high-dimensional controllers. Our key insight is that the behavior of a high-dimensional controller can be approximated with several low-dimensional controllers in different regions of the state space. To balance the approximation accuracy and verifiability of our low-dimensional controllers, we leverage the latest verification-aware knowledge distillation. Then, if low-dimensional reachability results are inflated with statistical approximation errors, they yield a high-confidence reachability guarantee for the high-dimensional controller. We investigate two inflation techniques -- based on trajectories and control actions -- both of which show convincing performance in two OpenAI gym benchmarks.
d-Wave Hall effect and linear magnetoconductivity in metallic collinear antiferromagnets

In this paper we theoretically predict a distinct class of anomalous Hall effects occurring in metallic collinear antiferromagnets. The effect is quadratic and $d-$wave symmetric in external magnetic field. In addition, the electric current, transverse to the current voltage drop and the magnetic field in the predicted effect are all in the same plane. Studied theoretical model consists of two-dimensional fermions interacting with the Neel order through momentum dependent exchange interaction having a $d-$wave symmetry. We also find unusual linear magnetoconductivity in this model.
On cliques in three-dimensional dense point-line arrangements

As a variant of the celebrated Szemer\'edi--Trotter theorem, Guth and Katz proved that $m$ points and $n$ lines in $\mathbb{R}^3$ with at most $\sqrt{n}$ lines in a common plane must determine at most $O(m^{1/2}n^{3/4})$ incidences for $n^{1/2}\leq m\leq n^{3/2}$. This upper bound is asymptotically tight and has an important application in Erd{\H o}s distinct distance problem. We characterize the extremal constructions towards the Guth--Katz bound by proving that such a large dense point-line arrangement must contain a $k$-clique in general position provided $m \ll n$. This is an analog of a result by Solymosi for extremal Szemer\'edi--Trotter constructions in the plane.
Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data

State-of-the-art deep learning models for tabular data have recently achieved acceptable performance to be deployed in industrial settings. However, the robustness of these models remains scarcely explored. Contrary to computer vision, there is to date no realistic protocol to properly evaluate the adversarial robustness of deep tabular models due to intrinsic properties of tabular data such as categorical features, immutability, and feature relationship constraints. To fill this gap, we propose CAA, the first efficient evasion attack for constrained tabular deep learning models. CAA is an iterative parameter-free attack that combines gradient and search attacks to generate adversarial examples under constraints. We leverage CAA to build a benchmark of deep tabular models across three popular use cases: credit scoring, phishing and botnet attacks detection. Our benchmark supports ten threat models with increasing capabilities of the attacker, and reflects real-world attack scenarios for each use case. Overall, our results demonstrate how domain knowledge, adversarial training, and attack budgets impact the robustness assessment of deep tabular models and provide security practitioners with a set of recommendations to improve the robustness of deep tabular models against various evasion attack scenarios.