Miracle-dz/UniFAR-FTUs huggingface dataset:
Rarefied particle motions on hillslopes – Part 4: Philosophy

Abstract. Theoretical and experimental work (Furbish et al., 2020a, 2020b, 2020c) indicates that the travel distances of rarefied particle motions on rough hillslope surfaces are described by a generalized Pareto distribution. The form of this distribution varies with the balance between gravitational heating due to conversion of potential to kinetic energy and frictional cooling by particle-surface collisions. The generalized Pareto distribution in this problem is a maximum entropy distribution constrained by a fixed energetic cost – the total cumulative energy extracted by collisional friction per unit kinetic energy available during particle motions. The analyses leading to these results provide an ideal case study for highlighting three key elements of a statistical mechanics framework for describing sediment particle motions and transport: the merits of probabilistic versus deterministic descriptions of sediment motions; the implications of rarefied versus continuum transport conditions; and the consequences of increasing uncertainty in descriptions of sediment motions and transport that accompany increasing length and time scales. We use the analyses of particle energy extraction, the spatial evolution of particle energy states, and the maximum entropy method applied to the generalized Pareto distribution as examples to illustrate the mechanistic yet probabilistic nature of the approach. These examples highlight the idea that the endeavor is not simply about adopting theory or methods of statistical mechanics off the shelf, but rather involves appealing to the style of thinking of statistical mechanics while tailoring the analysis to the process and scale of interest. Under rarefied conditions, descriptions of the particle flux and its divergence pertain to ensemble conditions involving a distribution of possible outcomes, each realization being compatible with the controlling factors. When these factors change over time, individual outcomes reflect a legacy of earlier conditions that depends on the rate of change in the controlling factors relative to the intermittency of particle motions. The implication is that landform configurations and associated particle fluxes reflect an inherent variability ( weather ) that is just as important as the expected ( climate ) conditions in characterizing system behavior.
Communicating Intention in Decentralized Multi-Agent Multi-Objective Reinforcement Learning Systems

In the past few decades, many studies have proposed different approaches to multi-agent reinforcement learning (MARL) systems and have found application in a variety of domains such as swarm robotics coordination, traffic-light control, and supply chain management. These methods usually fall under the two categories of centralized and decentralized systems, with each approach having certain advantages at a cost of some drawbacks. In the former approach, a single top-level centralized controller is used for making the action decisions of every agent in the world, whereas in the latter method, each individual agent is responsible for choosing its actions and learning its own individual behavior policy using the local rewards received during the process. While the decentralized systems can avoid the scalability problem of the centralized monolithic approach in complex problems, coordinating the agents in order to produce a coherent collective behavior that satisfies the global criteria still remains a challenge. In this thesis, we compared both the centralized and decentralized approaches to MARL systems in coordinating multiple agents in a shared 2-dimensional environment that consists of two separate goal locations. We explored several approaches of communication in the decentralized systems to enhance the coordination among the independent learners and assist them to overcome their limited observation of the environment. Five variations of decentralized MARL systems are proposed that differ in the range of communication they provide between the independent agents. In order to evaluate the effect of the communication mechanisms and compare the performance of the MARL systems under different conditions, four experiment scenarios were designed in which the difficulty of the task was altered using different world configurations and limited vision of independent learners. The results of our experiments show that the communication mechanisms can substantially enhance the performance of the baseline decentralized MARL system in the complex setups and also accelerate the convergence in the average scenarios. Our method of enabling the agents to shape and communicate their intention using multi-objective reinforcement learning managed to demonstrate a faster learning process than the centralized MARL system in the complex scenarios, even with limited observability of the environment. Similarly, policy-sharing outperforms the centralized MARL system in scenarios with a large number of agents and needs significantly shorter training sessions.
Epidemiology and Mortality of Sepsis in Intensive Care Units in Prefecture-Level Cities in Sichuan, China: A Prospective Multicenter Study

Background Studies on the epidemiology of sepsis in intensive care units (ICUs) of prefecture-level hospitals in China are rare. This study aimed to investigate the epidemiological characteristics and mortality risk factors of sepsis in ICUs of tertiary hospitals in Sichuan, China. Material/Methods In this prospective, multicenter, observational study, patients admitted to the ICU of 7 tertiary hospitals in Sichuan (China) between October 10, 2017 and January 9, 2018 were screened for sepsis using the Sepsis-3 criteria. Patients with sepsis were included. Results Of the 1604 patients screened for sepsis, 294 (18.3%) had sepsis, and 140 (47.6%) had septic shock. Of these, 169 (57.5%) died. Multivariable analysis showed that central nervous system dysfunction (odds ratio [OR]=2.59, 95% confidence interval [CI]: 1.15–5.84, P=0.022), lowest blood phosphorus level (OR=2.56, 95% CI: 1.21–5.44, P=0.014), highest lactate level (OR=1.20, 95% CI: 1.10–1.32, P<0.001), 24-h Acute Physiologic Assessment and Chronic Health Evaluation-II (APACHE-II) score (OR=1.08, 95% CI: 1.03–1.13, P=0.002), and lung infection (OR=2.57, 95% CI: 1.30–5.09, P=0.007) were independently associated with mortality in patients with sepsis. Conclusions The prevalence and mortality rates of sepsis are high in tertiary hospital ICUs in Sichuan, China. The APACHE-II score on day 1 after diagnosis, acute central nervous system dysfunction, lowest blood phosphorus, high serum lactate, and lung infection were independent risk factors of mortality in patients with sepsis.
Preparation of Hydrophobic Silica with Isocyanates

Hydrophobic silicas were prepared as follows: 0.5g of silica powder was dried at 200°C in an electric furnace and then the powder was treated in n-hexane solution containing 1.0g of 2, 4-tolylenediisocyanate (2, 4-TDI) at room temperature for 4h. The other isocyanates, hexamethylene diisocyanate (HMDI), isophorone diisocyanate (IPDI), methylene diphenylisocyanate (MDI) and phenyl isocyanate (PI) were used. Surface modification by IPDI and MDI gave hydrophobic silica. The conditions for preparing hydrophobic silica, such as drying temperature and reaction time were investigated. Also the dispersibility of the products to some organic solvents was discussed.
Safe and Efficient Decarboxylation Process: A Practical Synthetic Route to 4-Chlorobenzo[b]thiophene

We established an improved synthetic route to 4-chlorobenzo[b]thiophene, a key intermediate in brexpiprazole synthesis, via a practical decarboxylation process in three steps. Thermal analysis demonstrated that the coexistence of the decarboxylated product with DBU should be avoided and that removal of the product outside the reactor was vital. Our process yields the target compound by distillation under reduced pressure and is safe, highly batch efficient, cost-effective, and high yielding. Furthermore, manufacturing on a pilot scale was also accomplished through our approach.
Antibacterial activity of Thymus phenols by direct bioautography

Planar chromatography is a powerful technique for separating certain classes of compound of biological interest. In higher plants there are many substances which have antimicrobial effect. Bioautography is a method to localize antibacterial activity on a chromatogram. Thymol and carvacrol have antibacterial effect against Erwinia amylovora and Erwinia carotovora. Rosmarinic and caffeic acids were detected in some Thymus taxa but have no antibacterial activity with direct bioautography. Acta Biol Szeged 46(3-4):145-146 (2002)
Effect of plasma isolated Orexin-A on the regulation of metabolites in male rats

Article history: Received April 24, 2020 Accepted July 14, 2020 Available online June 23, 2021 This research includes the isolation and purification of Orexin-A from the plasma of healthy human via various biochemical techniques, it was proposed the therapeutic role of orexin on hyperlipidemia and lipid peroxidation and it has been suggested to study the effect of isolated orexin A on the metabolism of lipids and glucose in normal and hyperlipidemic rats, a high level of orexin-A had been found only in second peak (B) isolated by gel filtration chromatography (using Sephadex G-50) and showed (34.5) fold of purification, also, the effect of isolated orexin-A on some clinical parameters had been studied in normal and hyperlipidemic male rats. The rats were injected intraperitoneally with orexin-A at a dose of 1μmol/kg of body weight/day for one month. The results, obtained before treatment and after two and four weeks of treatment, had been showing a significant decrease in the concentration of total cholesterol, triglycerides, low and very low-density lipoprotein cholesterol, glucose, malondialdehyde and hyperinsulinemia, while there was a significant increase in the concentration of high-density lipoprotein cholesterol in normal and hyperlipidemic rats. It was concluded that orexin-A had an important role in regulating the metabolism of glucose and lipids, treatment of hyperinsulinemia and insulin resistance, and decreasing lipid peroxidation in normal and hyperlipidemic rats.
Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks

Bounding is one of the important gaits in quadrupedal locomotion for negotiating obstacles. However, due to a large number of robot and environmental constraints, conventional planning and control has limited ability to adapt bounding gaits on various terrains in real-time. We proposed an efficient approach to learn robust bounding gaits by first pretraining the deep neural network (DNN) using data from a robot that used conventional model-based controllers. Next, the pretrained DNN weights are optimized further via deep reinforcement learning (DRL). Also, we designed a reward function considering contact points to enforce the gait symmetry and periodicity, and used feature engineering to improve input features of the DRL model and the bounding performance. The DNN-based feedback controller was learned in simulation first and deployed directly on the real Jueying-Mini robot successfully, which was computationally more efficient and performed much better than the previous model-based control in terms of robustness and stability in both indoor and outdoor experiments.
Hop-Constrained Metric Embeddings and their Applications

In network design problems, such as compact routing, the goal is to route packets between nodes using the (approximated) shortest paths. A desirable property of these routes is a small number of hops, which makes them more reliable, and reduces the transmission costs. Following the overwhelming success of stochastic tree embeddings for algorithmic design, Haeupler, Hershkowitz, and Zuzic (STOC'21) studied hop-constrained Ramsey-type metric embeddings into trees. Specifically, embedding $f: G(V, E)\rightarrow T$ has Ramsey hop-distortion ($t, M,\beta, h$), (here $t, \beta, h\geq 1$ and $M\subseteq V)$ if $\forall u\in M, v\in V,\ d_{G}^{(\beta\cdot h)}(u, v)\leq d_{T}(u, v)\leq t\cdot d_{G}^{(h)}(u, v). t$ is called the distortion, $\beta$ is called the hop-stretch, and $d_{G}^{(h)}(u, v)$ denotes the minimum weight of a $u-v$ path with at most $h$ hops. Haeupler et al. constructed embedding where $M$ contains $1-\epsilon$ fraction of the vertices and $\beta=t=O(\frac{\log^{2}n}{\epsilon})$. They used their embedding to obtain multiple bicriteria approximation algorithms for hop-constrained network design problems. In this paper, we first improve the Ramsey-type embedding to obtain parameters $t=\beta=\frac{\tilde{O}(\log n)}{\epsilon}$, and generalize it to arbitrary distortion parameter $t$ (in the cost of reducing the size of $M$). This embedding immediately implies polynomial improvements for all the approximation algorithms from Haeupler et al. Further, we construct hop-constrained clan embeddings (where each vertex has multiple copies), and use them to construct bicriteria approximation algorithms for the group Steiner tree problem, matching the state of the art of the non constrained version. Finally, we use our embedding results to construct hop constrained distance oracles, distance labeling, and most prominently, the first hop constrained compact routing scheme with provable guarantees. All our metric data structures almost match the state of the art parameters of the non-constrained versions.
Multiphase Powerful Outflows Detected in High-z Quasars

We present results from a comprehensive study of ultrafast outflows (UFOs) detected in a sample of 14 quasars, 12 of which are gravitationally lensed, in a redshift range of 1.41–3.91, near the peak of the active galactic nucleus (AGN) and star formation activity. New XMM-Newton observations are presented for six of them, which were selected to be lensed and contain a narrow absorption line (NAL) in their UV spectra. Another lensed quasar was added to the sample, albeit already studied because it was not searched for UFOs. The remaining seven quasars of our sample are known to contain UFOs. The main goals of our study are to infer the outflow properties of high-z quasars, constrain their outflow induced feedback, study the relationship between the outflow properties and the properties of the ionizing source, and compare these results to those of nearby AGN. Our study adds six new detections (> 99% confidence) of UFOs at z > 1.4, almost doubling the current number of cases. Based on our survey of six quasars selected to contain a NAL and observed with XMM-Newton, the coexistence of intrinsic UV NALs and UFOs is found to be significant in >83% of these quasars suggesting a link between multiphase AGN feedback properties of the meso- and microscale. The kinematic luminosities of the UFOs of our high-z sample are large compared to their bolometric luminosities (median of L K/L Bol ≳ 50%). This suggests they provide efficient feedback to influence the evolution of their host galaxies and that magnetic driving may be a significant contributor to their acceleration.
Detecting Changed-Hands Online Review Accounts

A reputable social media or review account can be a good cover for spamming activities. It has become prevalent that spammers buy/sell such accounts openly on the Web. We call these sold/bought accounts the changed-hands (CH) accounts. They are hard to detect by existing spam detection algorithms as their spamming activities are under the disguise of clean histories. In this paper, we first propose the problem of detecting CH accounts, and then design an effective detection algorithm which exploits changes in content and writing styles of individual accounts, and a proposed novel feature selection method that works at a fine-grained level within each individual account. The proposed method not only determines if an account has changed hands, but also pinpoints the change point. Experimental results with online review accounts demonstrate the high effectiveness of our approach.
NOEMA Observations of CO Emission in Arp 142 and Arp 238

Previous studies have shown significant differences in the enhancement of the star formation rate (SFR) and star formation efficiency (SFE = SFR/M mol) between spiral–spiral and spiral–elliptical mergers. In order to shed light on the physical mechanism of these differences, we present NOEMA observations of the molecular gas distribution and kinematics (linear resolutions of ∼2 kpc) in two representative close major-merger star-forming pairs: the spiral–elliptical pair Arp 142 and the spiral–spiral pair Arp 238. The CO in Arp 142 is widely distributed over a highly distorted disk without any nuclear concentration, and an off-center ringlike structure is discovered in channel maps. The SFE varies significantly within Arp 142, with a starburst region (region 1) near the eastern tip of the distorted disk showing an SFE ∼ 0.3 dex above the mean of the control sample of isolated galaxies and the SFE of the main disk (region 4) 0.43 dex lower than the mean of the control sample. In contrast, the CO emission in Arp 238 is detected only in two compact sources at the galactic centers. Compared to the control sample, Arp 238-E shows an SFE enhancement of more than 1 dex, whereas Arp 238-W has an enhancement of ∼0.7 dex. We suggest that the extended CO distribution and large SFE variation in Arp 142 are due to an expanding large-scale ring triggered by a recent high-speed head-on collision between the spiral galaxy and the elliptical galaxy, and the compact CO sources with high SFEs in Arp 238 are associated with nuclear starbursts induced by gravitational tidal torques in a low-speed coplanar interaction.
Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey

This is a tutorial and survey paper on unification of spectral dimensionality reduction methods, kernel learning by Semidefinite Programming (SDP), Maximum Variance Unfolding (MVU) or Semidefinite Embedding (SDE), and its variants. We first explain how the spectral dimensionality reduction methods can be unified as kernel Principal Component Analysis (PCA) with different kernels. This unification can be interpreted as eigenfunction learning or representation of kernel in terms of distance matrix. Then, since the spectral methods are unified as kernel PCA, we say let us learn the best kernel for unfolding the manifold of data to its maximum variance. We first briefly introduce kernel learning by SDP for the transduction task. Then, we explain MVU in detail. Various versions of supervised MVU using nearest neighbors graph, by class-wise unfolding, by Fisher criterion, and by colored MVU are explained. We also explain out-of-sample extension of MVU using eigenfunctions and kernel mapping. Finally, we introduce other variants of MVU including action respecting embedding, relaxed MVU, and landmark MVU for big data.
Untidy Data: The Unreasonable Effectiveness of Tables

Working with data in table form is usually considered a preparatory and tedious step in the sensemaking pipeline; a way of getting the data ready for more sophisticated visualization and analytical tools. But for many people, spreadsheets – the quintessential table tool – remain a critical part of their information ecosystem, allowing them to interact with their data in ways that are hidden or abstracted in more complex tools. This is particularly true for data workers [61], people who work with data as part of their job but do not identify as professional analysts or data scientists. We report on a qualitative study of how these workers interact with and reason about their data. Our findings show that data tables serve a broader purpose beyond data cleanup at the initial stage of a linear analytic flow: users want to see and “get their hands on” the underlying data throughout the analytics process, reshaping and augmenting it to support sensemaking. They reorganize, mark up, layer on levels of detail, and spawn alternatives within the context of the base data. These direct interactions and human-readable table representations form a rich and cognitively important part of building understanding of what the data mean and what they can do with it. We argue that interactive tables are an important visualization idiom in their own right; that the direct data interaction they afford offers a fertile design space for visual analytics; and that sense making can be enriched by more flexible human-data interaction than is currently supported in visual analytics tools.
Improved Padding in CNNs for Quantitative Susceptibility Mapping

Recently, deep learning approaches have been proposed for QSM processing - background field removal, field-to-source inversion, and single-step QSM reconstruction. In these tasks, the networks usually take local fields or total fields as inputs, which have valid voxels within volume of interests (VOIs) and invalid voxels outside of VOIs. CNNs fail to consider this spatial information when using spatial invariant filters and conventional padding mechanism, which could introduce spatial artifacts in the QSM results. Here, we propose an improved padding technique utilizing neighboring valid voxels of invalid voxels to estimate the invalid voxels in feature maps of CNNs.
K-ZSL: Resources for Knowledge-driven Zero-shot Learning

External knowledge (a.k.a side information) plays a critical role in zero-shot learning (ZSL) which aims to predict with unseen classes that have never appeared in training data. Several kinds of external knowledge such as text and attribute have been widely investigated, but they alone are limited with incomplete semantics. Therefore, some very recent studies propose to use Knowledge Graph (KG) due to its high expressivity and compatibility for representing kinds of knowledge. However, the ZSL community is still in short of standard benchmarks for studying and comparing different KG-based ZSL methods. In this paper, we proposed 5 resources for KG-based research in zero-shot image classification (ZS-IMGC) and zero-shot KG completion (ZS-KGC). For each resource, we contributed a benchmark and its KG with semantics ranging from text to attributes, from relational knowledge to logical expressions. We have clearly presented how the resources are constructed, their statistics and formats, and how they can be utilized with cases in evaluating ZSL methods’ performance and explanations. Our resources are available at https://github.com/China-UK-ZSL/Resources_for_KZSL.
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning

Recent work on graph generative models has made remarkable progress towards generating increasingly realistic graphs, as measured by global graph features such as degree distribution, density, and clustering coefficients. Deep generative models have also made significant advances through better modelling of the local correlations in the graph topology, which have been very useful for predicting unobserved graph components, such as the existence of a link or the class of a node, from nearby observed graph components. A complete scientific understanding of graph data should address both global and local structure. In this paper, we propose a joint model for both as complementary objectives in a graph VAE framework. Global structure is captured by incorporating graph kernels in a probabilistic model whose loss function is closely related to the maximum mean discrepancy(MMD) between the global structures of the reconstructed and the input graphs. The ELBO objective derived from the model regularizes a standard local link reconstruction term with an MMD term. Our experiments demonstrate a significant improvement in the realism of the generated graph structures, typically by 1-2 orders of magnitude of graph structure metrics, compared to leading graph VAEand GAN models. Local link reconstruction improves as well in many cases.