Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Analytical Treatment of Continuum Electrostatics

An analytical treatment of the continuum model for electrostatic solvation is presented and shown to be in good agreement with numerical finite-difference continuum calculations for the same system. Because the analytic method, called ACE, is relatively fast and yields a pairwise potential from which gradients and second derivatives can be obtained, its use with molecular mechanics/molecular dynamics programs will be possible. For calculating the self-energies of molecules in aqueous solution, a new analytic method is introduced. It is based on a continuum electrostatics model in which charges are defined as Gaussian charge distributions, rather than point charges, and in which the solute interior is described by a continuous density function. The self-energy potential is a pairwise energy function which only depends on charges, interatomic distances, and the effective, solvent-inaccessible volumes of the atoms. The potential is derived by integrating the energy density of the electric field and termed th.
In vitro preliminary study of free radical scavenging activity of extracts from Khaya senegalensis A. Juss. (Meliaceae)

Aqueous and ethanolic crude extracts and fractions of Khaya senegalensis stem barks were investigated for their antioxidant activities. Their ability to act as radical scavengers have not been previously reported and was investigated using 1,1 diphenyl-2-picryl-hydrazyl (DPPH), a stable free radical in comparison with Rutin and Quecetin as antioxidant standards. The study shows that all extracts and fractions are able to scavenge significantly free radicals of DPPH. The IC50 are, respectively 9.82 μg mL-1 for defatted extract; 7.42 μg mL-1 for Rutin (Standard); 7.33 μg mL-1 for lyophilised aqueous extract; 6.01 μg mL-1 for the fraction F1 of lyophilised aqueous extract; 4.70 μg mL-1 for ethanolic extract; 4.37 μg mL-1 for the fraction F2 from methanolic crude extract of stem barks and 2.58 μg mL-1 for Quercetin (Standard). Lyophilised aqueous extract activity is closely related to that of Rutin but less than Quercetin. Fractions can play a pivotal role in the antioxidant activity of crude lyophilised aqueous and alcoholic extract of stem barks of Khaya senegalensis. © 2007 Asian Network for Scientific Information.
Pharmacometabolomics identifies candidate predictor metabolites of an L‐carnitine treatment mortality benefit in septic shock

Sepsis‐induced metabolic dysfunction contributes to organ failure and death. L‐carnitine has shown promise for septic shock, but a recent phase II study of patients with vasopressor‐dependent septic shock demonstrated a non‐significant reduction in mortality. We undertook a pharmacometabolomics study of these patients (n = 250) to identify metabolic profiles predictive of a 90‐day mortality benefit from L‐carnitine. The independent predictive value of each pretreatment metabolite concentration, adjusted for L‐carnitine dose, on 90‐day mortality was determined by logistic regression. A grid‐search analysis maximizing the Z‐statistic from a binomial proportion test identified specific metabolite threshold levels that discriminated L‐carnitine responsive patients. Threshold concentrations were further assessed by hazard ratio and Kaplan‐Meier estimate. Accounting for L‐carnitine treatment and dose, 11 1H‐NMR metabolites and 12 acylcarnitines were independent predictors of 90‐day mortality. Based on the grid‐search analysis numerous acylcarnitines and valine were identified as candidate metabolites of drug response. Acetylcarnitine emerged as highly viable for the prediction of an L‐carnitine mortality benefit due to its abundance and biological relevance. Using its most statistically significant threshold concentration, patients with pretreatment acetylcarnitine greater than or equal to 35 µM were less likely to die at 90 days if treated with L‐carnitine (18 g) versus placebo (p = 0.01 by log rank test). Metabolomics also identified independent predictors of 90‐day sepsis mortality. Our proof‐of‐concept approach shows how pharmacometabolomics could be useful for tackling the heterogeneity of sepsis and informing clinical trial design. In addition, metabolomics can help understand mechanisms of sepsis heterogeneity and variable drug response, because sepsis induces alterations in numerous metabolite concentrations.
Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation

Placing robotsoutside controlled conditions requires versatile movement representations that allow robots to learn new tasks and adapt them to environmental changes. The introduction of obstacles or the placement of additional robots in the workspace and the modification of the joint range due to faults or range-of-motion constraints are typical cases, where the adaptation capabilities play a key role for safely performing the robot’s task. Probabilistic movement primitives (ProMPs) have been proposed for representing adaptable movement skills, which are modeled as Gaussian distributions over trajectories. These are analytically tractable and can be learned from a small number of demonstrations. However, both the original ProMP formulation and the subsequent approaches only provide solutions to specific movement adaptation problems, e.g., obstacle avoidance, and a generic, unifying, probabilistic approach to adaptation is missing. In this article, we develop a generic probabilistic framework for adapting ProMPs. We unify previous adaptation techniques, for example, various types of obstacle avoidance, via-points, and mutual avoidance, in one single framework and combine them to solve complex robotic problems. Additionally, we derive novel adaptation techniques such as temporally unbound via-points and mutual avoidance. We formulate adaptation as a constrained optimization problem, where we minimize the Kullback–Leibler divergence between the adapted distribution and the distribution of the original primitive,while we constrain the probability mass associated with undesired trajectories to be low. We demonstrate our approach on several adaptation problems on simulated planar robot arms and seven-degree-of-freedom Franka Emika robots in a dual-robot-arm setting.
The Carnegie Chicago Hubble Program X: Tip of the Red Giant Branch Distances to NGC 5643 and NGC 1404

The primary goal of the Carnegie Chicago Hubble Program (CCHP) is to calibrate the zero-point of the Type Ia supernova (SN Ia) Hubble Diagram through the use of Population II standard candles. So far, the CCHP has measured direct distances to 11 SNe Ia, and here we increase that number to 15 with two new Tip of the Red Giant Branch (TRGB) distances measured to NGC 5643 and NGC 1404. We present resolved, point-source photometry from new Hubble Space Telescope imaging of these two galaxies in the F814W and F606W bandpasses. From each galaxy’s stellar halo, we construct an F814W-band luminosity function in which we detect an unambiguous edge feature identified as the TRGB. For NGC 5643, we find μ 0 = 30.48 ± 0.03(stat) ± 0.07(sys) mag, and for NGC 1404 we find μ 0 = 31.36 ± 0.04(stat) ± 0.05(sys) mag. From a preliminary consideration of the SNe Ia in these galaxies, we find increased confidence in the results presented in Paper VIII. The high precision of our TRGB distances enables a significant measurement of the 3D displacement between the Fornax cluster galaxies NGC 1404 and NGC 1316 (Fornax A) equal to 1.50−0.39+0.25 Mpc, which we show is in agreement with independent constraints found in the literature.
Fairness for Whom? Understanding the Reader’s Perception of Fairness in Text Summarization

With the surge in user-generated textual information, there has been a recent increase in the use of summarization algorithms for providing an overview of the extensive content. Traditional metrics for evaluation of these algorithms (e.g. ROUGE scores) rely on matching algorithmic summaries to human-generated ones. However, it has been shown that when the textual contents are heterogeneous, e.g., when they come from different socially salient groups, most existing summarization algorithms represent the social groups very differently compared to their distribution in the original data. To mitigate such adverse impacts, some fairness-preserving summarization algorithms have also been proposed. All of these studies have considered normative notions of fairness from the perspective of writers of the contents, neglecting the readers’ perceptions of the underlying fairness notions. To bridge this gap, in this work, we study the interplay between the fairness notions and how readers perceive them in textual summaries. Through our experiments, we show that reader’s perception of fairness is often context-sensitive. Moreover, standard ROUGE evaluation metrics are unable to quantify the perceived (un)fairness of the summaries. To this end, we propose a human-in-the-loop metric and an automated graph-based methodology to quantify the perceived bias in textual summaries. We demonstrate their utility by quantifying the (un)fairness of several summaries of heterogeneous socio-political microblog datasets.
Power Beacon Placement for Maximizing Guaranteed Coverage in Bistatic Backscatter Networks

The bistatic backscatter architecture, with its extended range, enables flexible deployment opportunities for backscatter devices. In this paper, we study the placement of power beacons (PBs) in bistatic backscatter networks to maximize the guaranteed coverage distance (GCD), defined as the distance from the reader within which backscatter devices are able to satisfy a given quality-of-service constraint. This work departs from conventional energy source placement problems by considering the performance of the additional backscatter link on top of the energy transfer link. We adopt and optimize a symmetric PB placement scheme to maximize the GCD. The optimal PB placement under this scheme is obtained using either analytically tractable expressions or an efficient algorithm. Numerical results provide useful insights into the impacts of various system parameters on the PB placement and the resulting GCD, plus the advantages of the adopted symmetric placement scheme over other benchmark schemes.
Reservoir Computing with Magnetic Thin Films

Advances in artificial intelligence are driven by technologies inspired by the brain, but these technologies are orders of magnitude less powerful and energy efficient than biological systems. Inspired by the nonlinear dynamics of neural networks, new unconventional computing hardware has emerged with the potential to exploit natural phenomena and gain efficiency, in a similar manner to biological systems. Physical reservoir computing demonstrates this with a variety of unconventional systems, from optical-based to memristive systems. Reservoir computers provide a nonlinear projection of the task input into a high-dimensional feature space by exploiting the system's internal dynamics. A trained readout layer then combines features to perform tasks, such as pattern recognition and time-series analysis. Despite progress, achieving state-of-the-art performance without external signal processing to the reservoir remains challenging. Here we perform an initial exploration of three magnetic materials in thin-film geometries via microscale simulation. Our results reveal that basic spin properties of magnetic films generate the required nonlinear dynamics and memory to solve machine learning tasks (although there would be practical challenges in exploiting these particular materials in physical implementations). The method of exploration can be applied to other materials, so this work opens up the possibility of testing different materials, from relatively simple (alloys) to significantly complex (antiferromagnetic reservoirs).
A Causal Convolutional Neural Network for Motion Modeling and Synthesis

synthesis. Experimental results show that our model can effectively handle the variation of skeleton conﬁgurations, and it runs fast to synthesize different types of motions on-line. We also perform user studies to verify that the quality of motions generated by our network is superior to the motions of state-of-the-art human motion synthesis methods.
DNN-Life: An Energy-Efficient Aging Mitigation Framework for Improving the Lifetime of On-Chip Weight Memories in Deep Neural Network Hardware Architectures

Negative Biased Temperature Instability (NBTI)-induced aging is one of the critical reliability threats in nano-scale devices. This paper makes the first attempt to study the NBTI aging in the on-chip weight memories of deep neural network (DNN) hardware accelerators, subjected to complex DNN workloads. We propose DNN-Life, a specialized aging analysis and mitigation framework for DNNs, which jointly exploits hardware- and software-level knowledge to improve the lifetime of a DNN weight memory with reduced energy overhead. At the software-level, we analyze the effects of different DNN quantization methods on the distribution of the bits of weight values. Based on the insights gained from this analysis, we propose a micro-architecture that employs low-cost memory-write (and read) transducers to achieve an optimal duty-cycle at run time in the weight memory cells, thereby balancing their aging. As a result, our DNN-Life framework enables efficient aging mitigation of weight memory of the given DNN hardware at minimal energy overhead during the inference process.
Principles and Techniques of Electroanalytical Stripping Methods for Pharmaceutically Active Compounds in Dosage Forms and Biological Samples

A review of the applications, principles, advantages of stripping voltammetric techniques, namely anodic, cath- odic, abrasive, adsorptive stripping voltammetry and potentiometric stripping analysis is presented. Stripping voltammet- ric methods in pharmaceutical analysis are discussed and the generally applicable procedure is given. The use and advan- tages and disadvantages of these techniques at different electrodes are discussed. The analytical applications of stripping techniques to pharmaceutically active compounds are also discussed. Various selected determination studies on drugs by stripping techniques from pharmaceutical dosage forms and biological samples are reviewed.
A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search

Approximate nearest neighbor search (ANNS) constitutes an important operation in a multitude of applications, including recommendation systems, information retrieval, and pattern recognition. In the past decade, graph-based ANNS algorithms have been the leading paradigm in this domain, with dozens of graph-based ANNS algorithms proposed. Such algorithms aim to provide effective, efficient solutions for retrieving the nearest neighbors for a given query. Nevertheless, these efforts focus on developing and optimizing algorithms with different approaches, so there is a real need for a comprehensive survey about the approaches' relative performance, strengths, and pitfalls. Thus here we provide a thorough comparative analysis and experimental evaluation of 13 representative graph-based ANNS algorithms via a new taxonomy and fine-grained pipeline. We compared each algorithm in a uniform test environment on eight real-world datasets and 12 synthetic datasets with varying sizes and characteristics. Our study yields novel discoveries, offerings several useful principles to improve algorithms, thus designing an optimized method that outperforms the state-of-the-art algorithms. This effort also helped us pinpoint algorithms' working portions, along with rule-of-thumb recommendations about promising research directions and suitable algorithms for practitioners in different fields.
A Comprehensive Survey on 6G Networks: Applications, Core Services, Enabling Technologies, and Future Challenges

Cellular Internet of Things (IoT) is considered as de facto paradigm to improve the communication and computation systems. Cellular IoT connects massive number of physical and virtual objects to the Internet using cellular networks. The latest generation of cellular networks, e.g. fifth-generation (5G), use evolutionary and revolutionary technologies to notably improve the performance of wireless networks. However, given the envisioned new use-cases, e.g., holographic communication, and the ever-increasing deployment of massive smart-physical end-devices in IoT, the volume of network traffic has considerably raised, and therefore, the current generation of mobile networks cannot wholly meet the ever-increasing demands. Hence, it is envisioned that the next generation, sixth generation (6G) networks, need to play a critical role to alleviate such challenges in IoT by providing new communication services, network capacity, and ultra-low latency communications (uRLLC). In this paper, first, the need for 6G networks is discussed. Then, the potential 6G requirements and trends, as well as the latest research activities related to 6G are introduced e.g., Tactile Internet and Terahertz (THz). Furthermore, the key performance indicators, applications, new services, and the potential key enabling technologies for 6G networks are presented. Finally, several potential unresolved challenges for future 6G networks are presented.
Transition based Graph Decoder for Neural Machine Translation

While a number of works showed gains from incorporating source-side symbolic syntactic and semantic structure into neural machine translation (NMT), much fewer works addressed the decoding of such structure. We propose a general Transformer-based approach for tree and graph decoding based on generating a sequence of transitions, inspired by a similar approach that uses RNNs by Dyer (2016). Experiments with using the proposed decoder with Universal Dependencies syntax on English-German, German-English and English-Russian show improved performance over the standard Transformer decoder, as well as over ablated versions of the model. \tacltxt{\footnote{All code implementing the presented models will be released upon acceptance.
Mobilization of arsenic in sedimentary aquifer vis-à-vis subsurface iron reduction processes

Groundwater of the arsenic affected areas demonstrates that the water is Ca-bicarbonate type and typical anoxic in nature. High arsenic groundwater is distributed in patches and hot spots are identified in palaeo-meander belt. Iron rich arsenic traps undergo reduction and release redox sensitive species (As and Fe) under local reducing conditions. Releases of redox species are often depends on land-use pattern and the abundance of bioavailable forms of iron oxides/minerals.
Optimal strategies for reject option classifiers

In classification with a reject option, the classifier is allowed in uncertain cases to abstain from prediction. The classical cost-based model of a reject option classifier requires the cost of rejection to be defined explicitly. An alternative bounded-improvement model, avoiding the notion of the reject cost, seeks for a classifier with a guaranteed selective risk and maximal cover. We coin a symmetric definition, the bounded-coverage model, which seeks for a classifier with minimal selective risk and guaranteed coverage. We prove that despite their different formulations the three rejection models lead to the same prediction strategy: a Bayes classifier endowed with a randomized Bayes selection function. We define a notion of a proper uncertainty score as a scalar summary of prediction uncertainty sufficient to construct the randomized Bayes selection function. We propose two algorithms to learn the proper uncertainty score from examples for an arbitrary black-box classifier. We prove that both algorithms provide Fisher consistent estimates of the proper uncertainty score and we demonstrate their efficiency on different prediction problems including classification, ordinal regression and structured output classification.
GLEAM: Galaxy Line Emission & Absorption Modeling

We present Galaxy Line Emission & Absorption Modeling (gleam), a Python tool for fitting Gaussian models to emission and absorption lines in large samples of 1D extragalactic spectra. gleam is tailored to work well in batch mode without much human interaction. With gleam, users can uniformly process a variety of spectra, including galaxies and active galactic nuclei, in a wide range of instrument setups and signal-to-noise regimes. gleam also takes advantage of multiprocessing capabilities to process spectra in parallel. With the goal of enabling reproducible workflows for its users, gleam employs a small number of input files, including a central, user-friendly configuration in which fitting constraints can be defined for groups of spectra and overrides can be specified for edge cases. For each spectrum, gleam produces a table containing measurements and error bars for the detected spectral lines and continuum and upper limits for nondetections. For visual inspection and publishing, gleam can also produce plots of the data with fitted lines overlaid. In the present paper, we describe gleam’s main features, the necessary inputs, expected outputs, and some example applications, including thorough tests on a large sample of optical/infrared multi-object spectroscopic observations and integral field spectroscopic data. gleam is developed as an open-source project hosted at https://github.com/multiwavelength/gleam and welcomes community contributions.
Five-percent measurements of the growth rate from simulation-based modelling of redshift-space clustering in BOSS LOWZ

We use a simulation-based modelling approach to analyse the anisotropic clustering of the BOSS LOWZ sample over the radial range $0.4 \, h^{-1} \, \mathrm{Mpc}$ to $63 \, h^{-1} \, \mathrm{Mpc}$, significantly extending what is possible with a purely analytic modelling framework. Our full-scale analysis yields constraints on the growth of structure that are a factor of two more stringent than any other study on large scales at similar redshifts. We infer $f \sigma_8 = 0.471 \pm 0.024$ at $z \approx 0.25$, and $f \sigma_8 = 0.431 \pm 0.025$ at $z \approx 0.40$; the corresponding $\Lambda$CDM predictions of the Planck CMB analysis are $0.470 \pm 0.006$ and $0.476 \pm 0.005$, respectively. Our results are thus consistent with Planck, but also follow the trend seen in previous low-redshift measurements of $f \sigma_8$ falling slightly below the $\Lambda$CDM+CMB prediction. We find that small and large radial scales yield mutually consistent values of $f \sigma_8$, but there are $1-2.5 \sigma$ hints of small scales ($< 10 \, h^{-1} \, \mathrm{Mpc}$) preferring lower values for $f \sigma_8$ relative to larger scales. We analyse the constraining power of the full range of radial scales, finding that most of the multipole information about $f\sigma_8$ is contained in the scales $2 \, h^{-1} \, \mathrm{Mpc} \lesssim s \lesssim 20 \, h^{-1} \, \mathrm{Mpc}$. Evidently, once the cosmological information of the quasi-to-nonlinear regime has been harvested, large-scale modes contain only modest additional information about structure growth. Finally, we compare predictions for the galaxy-galaxy lensing amplitude of the two samples against measurements from SDSS and assess the lensing-is-low effect in light of our findings.
Fixpoints and relative precompleteness

We study relative precompleteness in the context of the theory of numberings, and relate this to a notion of lowness. We introduce a notion of divisibility for numberings, and use it to show that for the class of divisible numberings, lowness and relative precompleteness coincide with being computable. We also study the complexity of Skolem functions arising from Arslanov’s completeness criterion with parameters. We show that for suitably divisible numberings, these Skolem functions have the maximal possible Turing degree. In particular this holds for the standard numberings of the partial computable functions and the c.e. sets.