Miracle-dz/UniFAR-FTUs huggingface dataset:
X-ray Diffraction of Water in Polyvinylpyrrolidone.

PVP is a hydrophilic polymer commonly used as an excipient in pharmaceutical formulations. Here we have performed time-resolved high-energy X-ray scattering experiments on pellets of PVP at different humidity conditions for 1-2 days. A two-phase exponential decay in water sorption is found with a peak in the differential pair distribution function at 2.85 Å, which is attributed to the average (hydrogen bonded) carbonyl oxygen-water oxygen distance. Additional scattering measurements on powders with fixed compositions ranging from 2 to 12.3 wt % H2O were modeled with Empirical Potential Structure Refinement (EPSR). The models reveal approximately linear relations between the carbonyl oxygen-water oxygen coordination number (nOC-OW) and the water oxygen-water oxygen coordination number (nOW-OW) versus water content in PVP. A stronger preference for water-water hydrogen bonding over carbonyl-water bonding is found. At all the concentrations studied the majority of water molecules were found to be randomly isolated, but a wide distribution of coordination environments of water molecules is found within the PVP polymer strands at the highest concentrations. Overall, the EPSR models indicate a continuous evolution in structure versus water content with nOW-OW=1 occurring at ∼12 wt % H2O, i.e., the composition where, on average, each water molecule is surrounded by one other water molecule.
Updates on Recent Clinical Assessment of Commercial Chronic Wound Care Products

Impaired wound healing after trauma, disorders, and surgeries impact millions of people globally every year. Dysregulation in orchestrated healing mechanisms and underlying medical complications make chronic wound management extremely challenging. Besides standard‐of‐care treatments including broad spectrum antibiotics and wound‐debridement, novel adjuvant therapies are clinically tested and commercialized. These include topical agents, skin substitutes, growth factor delivery, and stem cell therapies. With a goal to overcome factors playing pivotal role in delayed wound healing, researchers are exploring novel approaches to elicit desirable healing outcomes in chronic wounds. Although recent innovations in wound care products, therapies, and devices are extensively reviewed in past, a comprehensive review summarizing their clinical outcomes is surprisingly lacking. Herein, this work reviews the commercially available wound care products and their performance in clinical trials to provide a statistically comprehensive understanding of their safety and efficacy. The performance and suitability of various commercial wound care platforms, including xenogeneic and allogenic products, wound care devices, and novel biomaterials, are discussed for chronic wounds. The current clinical evaluation will provide a comprehensive understanding of the benefits and drawbacks of the most‐recent approaches and will enable researchers and healthcare providers to develop next‐generation technologies for chronic wound management.
Hot-carrier solar cell NEGF-based simulations

Ultra thin absorbers for the hot carrier solar cell applications are promising. Indeed, in these ultimate absorbers electronphonon scattering are reduced and thickness can be lower than the electron mean-free-path. In this case carriers reach contact ballistically. However it is important that the contact permits to extract these carriers. This theoretical study is about the extraction of the photogenerated carriers and particularly the ballistic extraction without any scattering. We show that quantum interaction between the ultra-thin absorber and the contact can be used to enhance the extraction. Particularly, a contact composed of a quantum well into a double barrier permits to increase the current compared to a simple contact. This improvement is due to a quantum resonance. This result is interesting for the hot carrier solar cells but also for all the ultra-thin cells.
Measurements of plasma rotation in tokamak LT-3

Rotation of the plasma in LT-3 has been measured from the Doppler shift of lines emitted by ionized oxygen impurities. In stable discharges, toroidal rotation of the ions in the direction of the discharge current was measured at velocities of up to 5 km · s−1, while poloidal rotation was observed in the electron diamagnetic drift direction, reaching linear velocities of 1.6 km · s−1 at a minor radius of 5 cm. In unstable discharges, the plasma rotation collapses at the disruptions, and then re-appears as the magnetic surfaces are reformed.
Evidence-Based Assessment of Developmental Disorders

Developmental disorders are diverse, common, and impairing; still, many clinicians lack comprehensive training in their assessment. This review presents thorough guidelines for the evaluation and diagnosis of common communication, sleeping, feeding, and elimination disorders that often onset in the early developmental period and that are commonly encountered in clinical practice. Thorough guidance on the evidence-based assessment of developmental disorders is critical, as they are prevalent, impairing, and commonly comorbid with other psychiatric disorders of childhood. This review is the first of its kind—providing critically needed, step-by-step guidance on the existing evidence-based methodologies and assessment tools available for diagnosis. This review also makes clear the dire need for further development and validation of relevant screening and diagnostic measure and calls for specific attention to the development of specific screening and diagnostic assessment measures for feeding disorders and elimination disorders in particular. Clinicians and researchers alike may find this article useful in guiding diagnostic, treatment, and research procedures.
Kalman Filter Based Channel Estimation

The paper addresses the problem of channel tracking for time varying channels. The accurate knowledge of the channel aids equalization and symbol detection. The Kalman filter is proposed to address the problem of channel tracking in fading environment. It is due to the fact that Kalman filter is optimal estimator. The random data bits are BPSK-modulated and transmitted over the channel. The channels under consideration are AWGN, Ricean and Rayleigh. The Kalman filter operates over the data bits to estimate the channel. At the receiver end demodulation and detection is performed. The above model is implemented in MATLAB. The simulation results prove that Kalman filter is an excellent tracker and predictor. The mean square error converges quickly. The results are compared on the basis of SNR and MMSE for the three mentioned channels. The Kalman filter finds its application in channel tracking in OFDM systems. Keywords— Kalman Filter; Channel Estimation; Semi-Blind; MIM; MSE.
Controlling Photons in Superconducting Electrical Circuits

Controlling Photons in Superconducting Electrical Circuits Blake Robert Johnson 2011 Circuit quantum electrodynamics (circuit QED) is a system that allows for strong coupling between microwave photons in transmission line cavities and superconducting qubits or artificial atoms. While circuit QED is often studied in the context of quantum information processing, it also provides an attractive platform for performing quantum optics experiments on-a-chip, because the level of control and coupling strengths available in circuit QED opens a vast array of possibilities for the creation, manipulation, and detection of quantum states of light. In this thesis, the extension of circuit QED to two cavities is examined, including design issues for cavities with very different Q-factors, and a new qubit design is proposed that couples to both cavities. The qubit-cavity interaction, while providing much of the utility of circuit QED, also introduces additional qubit relaxation. A powerful formalism for calculating this energy decay due to the classical admittance of the electromagnetic environment is presented in the context of circuit QED. Measurements of a wide range of samples validate this theory as providing an effective model for relaxation. A new circuit element, called the ‘Purcell filter’, is introduced and demonstrated to decouple the relationship between cavity Q and qubit relaxation. Finally, a new method for performing quantum nondemolition measurements of microwave photons is demonstrated. Controlling Photons in Superconducting Electrical Circuits A Dissertation Presented to the Faculty of the Graduate School of Yale University in Candidacy for the Degree of Doctor of Philosophy by Blake Robert Johnson Dissertation Director: Professor Robert J. Schoelkopf
Transcriptomic Profiling Reveals Intense Host-Pathogen Dispute Compromising Homeostasis during Acute Rift Valley Fever Virus Infection

Rift Valley fever virus (RVFV) is a mosquito-transmitted pathogen capable of causing severe disease in animals and humans. Outbreaks of RVFV pose a significant threat to public health and can result in substantial economic losses. ABSTRACT Rift Valley fever virus (RVFV) (family Phenuiviridae) can cause severe disease, and outbreaks of this mosquito-borne pathogen pose a significant threat to public and animal health. Yet many molecular aspects of RVFV pathogenesis remain incompletely understood. Natural RVFV infections are acute, characterized by a rapid onset of peak viremia during the first days post-infection, followed by a rapid decline. Although in vitro studies identified a major role of interferon (IFN) responses in counteracting the infection, a comprehensive overview of the specific host factors that play a role in RVFV pathogenesis in vivo is still lacking. Here, the host in vivo transcriptional profiles in the liver and spleen tissues of lambs exposed to RVFV are studied using RNA sequencing (RNA-seq) technology. We validate that IFN-mediated pathways are robustly activated in response to infection. We also link the observed hepatocellular necrosis with severely compromised organ function, which is reflected as a marked downregulation of multiple metabolic enzymes essential for homeostasis. Furthermore, we associate the elevated basal expression of LRP1 in the liver with RVFV tissue tropism. Collectively, the results of this study deepen the knowledge of the in vivo host response during RVFV infection and reveal new insights into the gene regulation networks underlying pathogenesis in a natural host. IMPORTANCE Rift Valley fever virus (RVFV) is a mosquito-transmitted pathogen capable of causing severe disease in animals and humans. Outbreaks of RVFV pose a significant threat to public health and can result in substantial economic losses. Little is known about the molecular basis of RVFV pathogenesis in vivo, particularly in its natural hosts. We employed RNA-seq technology to investigate genome-wide host responses in the liver and spleen of lambs during acute RVFV infection. We show that RVFV infection drastically decreases the expression of metabolic enzymes, which impairs normal liver function. Moreover, we highlight that basal expression levels of the host factor LRP1 may be a determinant of RVFV tissue tropism. This study links the typical pathological phenotype induced by RVFV infection with tissue-specific gene expression profiles, thereby improving our understanding of RVFV pathogenesis.
Happy People - Image Synthesis as Black-Box Optimization Problem in the Discrete Latent Space of Deep Generative Models

In recent years, optimization in the learned latent space of deep generative models has been successfully applied to black-box optimization problems such as drug design, image generation or neural architecture search. Existing models thereby leverage the ability of neural models to learn the data distribution from a limited amount of samples such that new samples from the distribution can be drawn. In this work, we propose a novel image generative approach that optimizes the generated sample with respect to a continuously quantifiable property. While we anticipate absolutely no practically meaningful application for the proposed framework, it is theoretically principled and allows to quickly propose samples at the mere boundary of the training data distribution. Specifically, we propose to use tree-based ensemble models as mathematical programs over the discrete latent space of vector quantized VAEs, which can be globally solved. Subsequent weighted retraining on these queries allows to induce a distribution shift. In lack of a practically relevant problem, we consider a visually appealing application: the generation of happily smiling faces (where the training distribution only contains less happy people) - and show the principled behavior of our approach in terms of improved FID and higher smile degree over baseline approaches.
Structure of the airflow above surface waves

AbstractIn recent years, much progress has been made to quantify the momentum exchange between the atmosphere and the oceans. The role of surface waves on the airflow dynamics is known to be significant, but our physical understanding remains incomplete. The authors present detailed airflow measurements taken in the laboratory for 17 different wind wave conditions with wave ages [determined by the ratio of the speed of the peak waves Cp to the air friction velocity u* (Cp/u*)] ranging from 1.4 to 66.7. For these experiments, a combined particle image velocimetry (PIV) and laser-induced fluorescence (LIF) technique was developed. Two-dimensional airflow velocity fields were obtained as low as 100 μm above the air–water interface. Temporal and spatial wave field characteristics were also obtained. When the wind stress is too weak to generate surface waves, the mean velocity profile follows the law of the wall. With waves present, turbulent structures are directly observed in the airflow, whereby low-horizon.
Exact and Approximate Moment Derivation for Probabilistic Loops With Non-Polynomial Assignments

Many stochastic continuous-state dynamical systems can be modeled as probabilistic programs with nonlinear non-polynomial updates in non-nested loops. We present two methods, one approximate and one exact, to automatically compute, without sampling, moment-based invariants for such probabilistic programs as closed-form solutions parameterized by the loop iteration. The exact method applies to probabilistic programs with trigonometric and exponential updates and is embedded in the Polar tool. The approximate method for moment computation applies to any nonlinear random function as it exploits the theory of polynomial chaos expansion to approximate non-polynomial updates as the sum of orthogonal polynomials. This translates the dynamical system to a non-nested loop with polynomial updates, and thus renders it conformable with the Polar tool that computes the moments of any order of the state variables. We evaluate our methods on an extensive number of examples ranging from modeling monetary policy to several physical motion systems in uncertain environments. The experimental results demonstrate the advantages of our approach with respect to the current state-of-the-art.
Area-minimizing surfaces with fractal singular sets: prevalence, moduli space, and refinements on strata

This paper is a continuation of our work on a conjecture of Almgren on area-minimizing surfaces with fractal singular sets. First, we prove that area-minimizing surfaces with fractal singular sets are prevalent on the homology level on general manifolds. They exist even in metrics arbitrarily close to the flat metric on a torus. Second, we partially determine the moduli space of area-minimizing currents near the ones with fractal singular set that we constructed in this and previous work. Around strata of codimension at least three, the fractal self-intersections deform into transverse immersions under generic perturbations of metric. The result is sharp and we construct concrete examples to show that our fractal singularities do not necessarily completely dissolve under generic perturbations. Finally, we improve our previous results to give examples with different strata limiting to each other and refine the first stratum to fractal subsets of finite combinatorial graphs.
The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation

Event extraction (EE) is a crucial task aiming at extracting events from texts, which includes two subtasks: event detection (ED) and event argument extraction (EAE). In this paper, we check the reliability of EE evaluations and identify three major pitfalls: (1) The data preprocessing discrepancy makes the evaluation results on the same dataset not directly comparable, but the data preprocessing details are not widely noted and specified in papers. (2) The output space discrepancy of different model paradigms makes different-paradigm EE models lack grounds for comparison and also leads to unclear mapping issues between predictions and annotations. (3) The absence of pipeline evaluation of many EAE-only works makes them hard to be directly compared with EE works and may not well reflect the model performance in real-world pipeline scenarios. We demonstrate the significant influence of these pitfalls through comprehensive meta-analyses of recent papers and empirical experiments. To avoid these pitfalls, we suggest a series of remedies, including specifying data preprocessing, standardizing outputs, and providing pipeline evaluation results. To help implement these remedies, we develop a consistent evaluation framework OMNIEVENT, which can be obtained from https://github.com/THU-KEG/OmniEvent.
On One-Parameter Subgroups of *-Automorphisms on Operator Algebras and the Corresponding Unbounded Derivations

1. It is known that if a derivation in C*-algebras is everywhere defined, then it is bounded and is weakly inner ([8]). On the other hand, in mathematical physics we have to confront unbounded derivations which are defined as infinitesimal generators of oneparameter subgroups of *-automorphisms on C*-algebras. Under some assumptions (for example, the positivity of infinitesimal operators), we may reduce the study of those unbounded derivations to the one of bounded derivations ([9]). However there are many important derivations in mathematical physics which do not satisfy the positivity. In this paper we wish to initiate a study of unbounded derivations in C*-algebras. A main goal of the study is to show that unbounded derivations in some C*-algebras, which are important in mathematical physics can be approximated by inner derivations on them. Obviously we can not expect such results for an arbitrary C*-algebra. For example, if 9f = C0(cc, xc) is the C*-algebra of all continuous functions on (ox, xo), vanishing at infinity, then the only bounded derivation is the identically zero mapping. On the other hand, let 8 be the differential operator d/ dt corresponding to the one-parameter subgroup of translation operators; then it is an unbounded derivation, so that it can not be approximated by the identically zero mapping in any reasonable sense. Now we shall explain briefly the result obtained in this paper. Let W be a uniformly hyperfinite C*-algebra, (p(t)I ox p(t)(a) is norm-continuous for each a E X, and let 8 be the infinitesimal generator corresponding to
Towards clinical translation of deep-learning based classification of DSA image sequences for stroke treatment

In the event of stroke, a catheter-guided procedure (thrombectomy) is used to remove blood clots. Feasibility of machine learning based automatic classifications for thrombus detection on digital substraction angiography (DSA) sequences has been demonstrated. It was however not used live in the clinic, yet. We present an open-source tool for automatic thrombus classification and test it on three selected clinical cases regarding functionality and classification runtime. With our trained model all large vessel occlusions in the M1 segment were correctly classified. One small remaining M3 thrombus was not detected. Runtime was in the range from 1 to 10 seconds depending on the used hardware. We conclude that our open-source software tool enables clinical staff to classify DSA sequences in (close to) realtime and can be used for further studies in clinics.
On the Limitations of Carbon-Aware Temporal and Spatial Workload Shifting in the Cloud

Cloud platforms have been focusing on reducing their carbon emissions by shifting workloads across time and locations to when and where low-carbon energy is available. Despite the prominence of this idea, prior work has only quantified the potential of spatiotemporal workload shifting in narrow settings, i.e., for specific workloads in select regions. In particular, there has been limited work on quantifying an upper bound on the ideal and practical benefits of carbon-aware spatiotemporal workload shifting for a wide range of cloud workloads. To address the problem, we conduct a detailed data-driven analysis to understand the benefits and limitations of carbon-aware spatiotemporal scheduling for cloud workloads. We utilize carbon intensity data from 123 regions, encompassing most major cloud sites, to analyze two broad classes of workloads -- batch and interactive -- and their various characteristics, e.g., job duration, deadlines, and SLOs. Our findings show that while spatiotemporal workload shifting can reduce workloads' carbon emissions, the practical upper bounds of these carbon reductions are currently limited and far from ideal. We also show that simple scheduling policies often yield most of these reductions, with more sophisticated techniques yielding little additional benefit. Notably, we also find that the benefit of carbon-aware workload scheduling relative to carbon-agnostic scheduling will decrease as the energy supply becomes"greener".
Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning

The proliferation of the Internet has led to the emergence of online advertising, driven by the mechanics of online auctions. In these repeated auctions, software agents participate on behalf of aggregated advertisers to optimize for their long-term utility. To fulfill the diverse demands, bidding strategies are employed to optimize advertising objectives subject to different spending constraints. Existing approaches on constrained bidding typically rely on i.i. d. train and test conditions, which contradicts the adversarial nature of online ad markets where different parties possess potentially conflicting objectives. In this regard, we explore the problem of constrained bidding in adversarial bidding environments, which assumes no knowledge about the adversarial factors. Instead of relying on the i.i. d. assumption, our insight is to align the train distribution of environments with the potential test distribution meanwhile minimizing policy regret. Based on this insight, we propose a practical Minimax Regret Optimization (MiRO) approach that interleaves between a teacher finding adversarial environments for tutoring and a learner meta-learning its policy over the given distribution of environments. In addition, we pioneer to incorporate expert demonstrations for learning bidding strategies. Through a causality-aware policy design, we improve upon MiRO by distilling knowledge from the experts. Extensive experiments on both industrial data and synthetic data show that our method, MiRO with Causality-aware reinforcement Learning (MiROCL), outperforms prior methods by over 30%.