Miracle-dz/UniFAR-FTUs huggingface dataset:
Disturbance-observer-based motion control of redundant manipulators using inertially decoupled dynamics

In this paper, a robust motion control method for kinematically redundant manipulators is addressed to control the motion of the end-effector, as well as the null-space motion. To reduce the disturbance effects on the system performance, a new disturbance observer is proposed to improve the performance of the conventional structure. A minimal parametrization of the null space is performed to visualize the null-space motion explicitly using the weighted decomposition of joint space. Augmenting this null motion with conventional velocity relations, an extended task space formulation is obtained. The proposed disturbance observer is adopted in the extended task space formulation and a motion control method is devised based on the passivity concept. The performance of the proposed controller is verified through experiments with a three-link planar direct-drive manipulator.
Bayesian analysis of Ecological Momentary Assessment (EMA) data collected in adults before and after hearing rehabilitation

This paper presents a new Bayesian method for analyzing Ecological Momentary Assessment (EMA) data and applies this method in a re-analysis of data from a previous EMA study. The analysis method has been implemented as a freely available Python package EmaCalc, RRID:SCR 022943. The analysis model can use EMA input data including nominal categories in one or more situation dimensions, and ordinal ratings of several perceptual attributes. The analysis uses a variant of ordinal regression to estimate the statistical relation between these variables. The Bayesian method has no requirements related to the number of participants or the number of assessments by each participant. Instead, the method automatically includes measures of the statistical credibility of all analysis results, for the given amount of data. For the previously collected EMA data, the analysis results demonstrate how the new tool can handle heavily skewed, scarce, and clustered data that were collected on ordinal scales, and present results on interval scales. The new method revealed results for the population mean that were similar to those obtained in the previous analysis by an advanced regression model. The Bayesian approach automatically estimated the inter-individual variability in the population, based on the study sample, and could show some statistically credible intervention results also for an unseen random individual in the population. Such results may be interesting, for example, if the EMA methodology is used by a hearing-aid manufacturer in a study to predict the success of a new signal-processing method among future potential customers.
Microstructure and magnetic properties of CoPtCr-SiO/sub 2/ perpendicular recording media

SiO/sub 2/ added CoPtCr magnetic layers are employed for perpendicular recording media. The microstructure, magnetic properties, and recording performance of these media are discussed. Very fine grains (size /spl sim/7 nm), surrounded by amorphous-like grain boundaries, are realized together with good c-axis orientation normal to the film plane. It is considered that the addition of SiO/sub 2/ to CoPtCr is very effective in realizing a well-isolated fine-grain structure without disturbing the epitaxial growth of CoPtCr grains on the Ru underlayer. The medium with a 12-nm-thick recording layer shows a large perpendicular anisotropy K/sub u/ of /spl sim/4 /spl times/ 10/sup 6/ erg/cm/sup 3/, and high coercivity H/sub c/ of /spl sim/4 kOe. Moreover, the medium shows excellent SNR performance together with high thermal stability, indicating great potential for high-density perpendicular recording media.
On the Evolution of the Stellar Mass Spectrum: Possible Evidence for Stellar Evaporation

The authors continue the analysis of the results of their preceding paper where a pure coagulation model was applied to star clusters in general. By combining in a certain way the free parameters of the model, it is possible to evaluate the ratio of the total mass of the cluster as it is today to what it was at its formation, according to the model. A correlation is found between this ratio and the age of the cluster. The analysis of this correlation also gives order of magnitude estimates of dierent processes such as accretion rate, evaporation rate, and timescale of disruption by evaporation (or mean life expectancy). These estimates are found to be in agreement with other published results. It is also found that the mass-loss rate of the cluster is proportional to the amount of coagulation that occured. All in all, it is shown that the coagulation model, although simplistic, merits more attention. Accepted by Ap. J.
Determination of the gain and saturation characteristics of a semiconductor laser amplifier using a ringlaser configuration

Determination of the gain of a semiconductor laser amplifier using a tunable laser source or a set of fixed wavelength lasers is a rather time consuming exercise. The reason is that the position of the gain peak and the positions of the local maxima and minima belonging to the residual Fabry-Perot ripple are not known in advance and moreover shift with increasing drive current. Therefore determination of the peak gain at various currents levels, which is desirable to select promising devices worthy of further study or to provide feedback in a fabrication process, involves measuring the gain at various different wavelengths to ensure that the measured value corresponds to a (local) maximum. We present an alternative method of gain determination using the laser amplifier in a ringlaser configuration (fig. 1). This method is very fast and does not require a tunable signal source.
Kinetics of Cloud Drop Formation and Its Parameterization for Cloud and Climate Models

Abstract To study the kinetics of drop nucleation in clouds, the integro–differential equation for integral water supersaturation in cloud is derived and analyzed. Solving the supersaturation equation with an algebraic form of the cloud condensation nuclei (CCN) activity spectrum, analytical expressions are obtained for the time tm of CCN activation process, the maximum supersaturation sm, and droplet concentration Ndr(sm), limited by the total aerosol concentration at high supersaturations. All three quantities are expressed as functions of vertical velocity and characteristics of the CCN size spectra: mean geometric radius, dispersion, and parameters of solubility. A generalized power law for the drop activation, Ndr(sm) = C(sm)sk(sm)m, is formulated that is similar in form to the Twomey power law, but both the coefficient C(sm) and index k(sm) are functions of supersaturation expressed analytically in terms of vertical velocities and CCN microphysical parameters. A simple and economical numerical solut.
On threshold moving‐average models

In this paper the class of discrete self‐exciting threshold moving‐average (SETMA) models is studied in some detail. In particular, we consider various problems associated with the identification, estimation and testing of these models. A simple method for distinguishing between low order moving average (MA) and low order SETMA models is presented. Some simulation results illustrate the performance of the proposed method. We also derive a Lagrange multiplier (LM) test statistic for testing a linear MA model against a SETMA model. The small sample performance of the LM test is evaluated in a Monte Carlo study. A real example is used to illustrate the results.
THE H, CONTROL PROBLEM USING STATIC OUTPUT FEEDBACK

SUMMARY In this paper we shall consider the H, control problem using static output feedback. The approach uses some recent results from linear algebra. The main result shows that the H, control problem is solvable by a static output feedback controller if and only if there exists a positive definite matrix satisfying two certain quadratic matrix inequalities. A parametrization of all static output feedback H, controllers is given. Many linear controller design analysis problems can be reduced to certain matrix linear algebra problems of the type studied in the covariance control literature ([4-61 and references therein.) For further motivation see the linear matrix inequalities discussed in Reference 3. In fact, the parametrization of all stabilizing controllers (of order equal to or less than the order of the plant) for a large variety of situations (continuous and discrete systems, with or without measurement noise) all reduce to the solution of just two linear algebra problems. In this paper we shall apply these linear algebra results to solve the standard H, control problem for static output feedback control [7-101. Using the linear algebra results we obtain necessary and sufficient existence conditions and a parametrization of all stabilizing static H, controllers. P+ will in the following denote the Moore-Penrose inverse of a matrix P. Everywhere, as usual, Q > 0 shall be taken to mean that Q is a positive definite, symmetric matrix. SVD stands for the singular value decomposition. 2. PRELIMINARIES
Dialogue between VE-Cadherin and Sphingosine 1 Phosphate Receptor1 (S1PR1) for Protecting Endothelial Functions

The endothelial cells (EC) of established blood vessels in adults remain extraordinarily quiescent in the sense that they are not actively proliferating, but they fulfill the necessary role to control the permeability of their monolayer that lines the interior of blood vessels. The cell–cell junctions between ECs in the endothelium comprise tight junctions and adherens homotypic junctions, which are ubiquitous along the vascular tree. Adherens junctions are adhesive intercellular contacts that are crucial for the organization of the EC monolayer and its maintenance and regulation of normal microvascular function. The molecular components and underlying signaling pathways that control the association of adherens junctions have been described in the last few years. In contrast, the role that dysfunction of these adherens junctions has in contributing to human vascular disease remains an important open issue. Sphingosine-1-phosphate (S1P) is a bioactive sphingolipid mediator found at high concentrations in blood which has important roles in the control of the vascular permeability, cell recruitment, and clotting that follow inflammatory processes. This role of S1P is achieved through a signaling pathway mediated through a family of G protein-coupled receptors designated as S1PR1. This review highlights novel evidence for a direct linkage between S1PR1 signaling and the mediation of EC cohesive properties that are controlled by VE-cadherin.
PROJECTOR MONTE CARLO METHOD

A simulation method for the calculation of properties of lattice field theories is studied. This method involves the numerical evaluation of projection operators onto low-lying quantum states. It is an alternative to the standard Monte Carlo evaluation of path integrals or the trace of the partition function and has many advantages. As an illustration, it is applied to a one-dimensional many-fermion system.
Iodine Deficiency and Iodine Prophylaxis: An Overview and Update

The thyroid gland requires iodine to synthesize thyroid hormones, and iodine deficiency results in the inadequate production of thyroxine and related thyroid, metabolic, developmental, and reproductive disorders. Iodine requirements are higher in infants, children, and during pregnancy and lactation than in adult men and non-pregnant women. Iodine is available in a wide range of foods and water and is susceptible to almost complete gastric and duodenal absorption as an iodide ion. A healthy diet usually provides a daily iodine consumption not exceeding 50% of the recommended intake. Iodine supplementation is usually necessary to prevent iodine deficiency disorders (IDDs), especially in endemic areas. The community-based strategy of iodine fortification in salt has eradicated IDDs, such as endemic goiter and cretinism, in countries providing adequate measures of iodine prophylaxis over several decades in the 20th century. Iodized salt is the cornerstone of iodine prophylaxis in endemic areas, and the continuous monitoring of community iodine intake and its related clinical outcomes is essential. Despite the relevant improvement in clinical outcomes, subclinical iodine deficiency persists even in Western Europe, especially among girls and women, being an issue in certain physiological conditions, such as pregnancy and lactation, and in people consuming unbalanced vegetable-based or salt-restricted diets. Detailed strategies to implement iodine intake (supplementation) could be considered for specific population groups when iodized salt alone is insufficient to provide adequate requirements.
Three‐dimensional adaptive finite element computations and applications to non‐Newtonian fluids

SUMMARY An adaptive strategy for the finite element solution of three-dimensional viscous flow problems is defined and implemented. The solution strategy is based on an advancing front mesh generator making use of binary data structures for fast geometrical data handling. The error is estimateld a posteriori with a residual-type bound. The error estimate is shown to exhibit proper convergence for tetrahedral elements. Its combination with the mesh generator and an interpolation scheme for unstructured meshes is shown to generate adaptive meshes and to reduce the solution cost for a given error level, as illustrated by the isothermal flow of a shear-thinning fluid. Computational fluid dynamics is gaining acceptance as a valuable tool for engineers in a wide range of fields. In aerodynamics, the field which has successfully pioneered such numerical methods, the complexity of mathematical models has grown with the increase in computer speed and memory size’ and now allows the 3D modelling of the flow past an entire aircraft. As the discipline matures, additional topics besides the eternal quest to minimize computational time have come to the forefront of development:* the estimation of the solution accuracy and the reduction of the engineer’s involvement in the drudgery of the meshing process. The development in the field of material forming-to which the results contained in this paper are applied-has been similar, engineers relying nowadays on computer simulation during design. The emphasis is now put on the areas identified above: reduction of computational cost, knowledge of solution accuracy and better use of the engineer’s time by integration of the finite element code with a solid modeller. In order to answer these requirements globally, adaptive techniques were developed for 2D elasticity problems. In these methods a solution is computed on a user-defined mesh; the error is then estimated from that solution3 and the mesh refined accordingly in areas of high error. This leads to a minimum number of degrees of freedom for a chosen accuracy and reduces the engineer’s work load if the refinement is done automatically. These methods are implemented in available codes using structured meshes (PLTMG? FEARS’), but no 3D extension has been published.
A new class of peptides from wasp venom: a pathway to antiepileptic/neuroprotective drugs

Abstract The ability of venom-derived peptides to disrupt physiological processes in mammals provides an exciting source for pharmacological development. Our research group has identified a new class of neuroactive peptides from the venom of a Brazilian social wasp, Polybia occidentalis, with the potential pharmacological profile to treat epilepsies. The study was divided into five phases: Phase 1 concerned the extraction, isolation and purification of Occidentalin-1202(n) from the crude venom, followed by the synthesis of an identical analogue peptide, named Occidentalin-1202(s). In Phase 2, we described the effects of both peptides in two acute models of epilepsy—kainic acid and pentylenetetrazole-induced model of seizures—and measured estimated ED50 and therapeutic index values, electroencephalographic studies and C-fos evaluation. Phase 3 was a compilation of advanced tests performed with Occidentalin-1202(s) only, reporting histopathological features and its performance in the pilocarpine-induced status epilepticus. After the determination of the antiepileptic activity of Occidentalin-1202(s), Phase 4 consisted of evaluating its potential adverse effects, after chronic administration, on motor coordination (Rotarod) and cognitive impairment (Morris water maze) tests. Finally, in Phase 5, we proposed a mechanism of action using computational models with kainate receptors. The new peptide was able to cross the blood–brain barrier and showed potent antiseizure effects in acute (kainic acid and pentylenetetrazole) and chronic (temporal lobe epilepsy model induced by pilocarpine) models. Motor and cognitive behaviour were not adversely affected, and a potential neuroprotective effect was observed. Occidentalin-1202 can be a potent blocker of the kainate receptor, as assessed by computational analysis, preventing glutamate and kainic acid from binding to the receptor’s active site. Occidentalin-1202 is a peptide with promising applicability to treat epilepsy and can be considered an interesting drug model for the development of new medicines.
Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions

Pretrained language models have shown superior performance on many natural language processing tasks, yet they still struggle at multi-step formal reasoning tasks like grade school math problems. One key challenge of finetuning them to solve such math reasoning problems is that many existing datasets only contain one reference solution for each problem, despite the fact that there are often alternative solutions resembling different reasoning paths to the final answer. This way, the finetuned models are biased towards the limited reference solutions, which limits their generalization to unseen examples. To mitigate this issue, we propose to let the model perform sampling during training and learn from both self-sampled fully-correct solutions, which yield the correct answer upon execution, and partially-correct solutions, whose intermediate state matches an intermediate state of a known correct solution. We show that our use of self-sampled correct and partially-correct solutions can benefit learning and help guide the sampling process, leading to more efficient exploration of the solution space. Additionally, we explore various training objectives to support learning from multiple solutions per example and find they greatly affect the performance. Experiments on two math reasoning datasets show the effectiveness of our method compared to learning from a single reference solution with MLE, where we improve PASS@100 from 35.5% to 44.5% for GSM8K, and 27.6% to 36.2% PASS@80 for MathQA. Such improvements are also consistent across different model sizes. Our code is available at https://github.com/microsoft/TraceCodegen.
Fast and exact fixed-radius neighbor search based on sorting

Fixed-radius near neighbor search is a fundamental data operation that retrieves all data points within a user-specified distance to a query point. There are efficient algorithms that can provide fast approximate query responses, but they often have a very compute-intensive indexing phase and require careful parameter tuning. Therefore, exact brute force and tree-based search methods are still widely used. Here we propose a new fixed-radius near neighbor search method, called SNN, that significantly improves over brute force and tree-based methods in terms of index and query time, provably returns exact results, and requires no parameter tuning. SNN exploits a sorting of the data points by their first principal component to prune the query search space. Further speedup is gained from an efficient implementation using high-level basic linear algebra subprograms (BLAS). We provide theoretical analysis of our method and demonstrate its practical performance when used stand-alone and when applied within the DBSCAN clustering algorithm.
Practical Low Latency Proof of Work Consensus

Bitcoin is the first fully-decentralized permissionless blockchain protocol to achieve a high level of security, but at the expense of poor throughput and latency. Scaling the performance of Bitcoin has a been a major recent direction of research. One successful direction of work has involved replacing proof of work (PoW) by proof of stake (PoS). Proposals to scale the performance in the PoW setting itself have focused mostly on parallelizing the mining process, scaling throughput; the few proposals to improve latency have either sacrificed throughput or the latency guarantees involve large constants rendering it practically useless. Our first contribution is to design a new PoW blockchain Prism++ that has provably low latency and high throughput; the design retains the parallel-chain approach espoused in Prism but invents a new confirmation rule to infer the permanency of a block by combining information across the parallel chains. We show security at the level of Bitcoin with very small confirmation latency (a small constant factor of block interarrival time). A key aspect to scaling the performance is to use a large number of parallel chains, which puts significant strain on the system. Our second contribution is the design and evaluation of a practical system to efficiently manage the memory, computation, and I/O imperatives of a large number of parallel chains. Our implementation of Prism++ achieves a throughput of over 80,000 transactions per second and confirmation latency of tens of seconds on networks of up to 900 EC2 Virtual Machines.
On the watermarking game of the random coding error exponent with large deviations distortion constraints

Watermarking (WM) systems are analyzed in the context of a game between an information hider, a decoder and an attacker. The value of the game is the maximin exponent of the probability of erroneously decoding the hidden message. A single-letter expression of the maximin error exponent and asymptotically optimal strategies are found under some mild assumptions.
Creating generalizable downstream graph models with random projections

We investigate graph representation learning approaches that enable models to generalize across graphs: given a model trained using the representations from one graph, our goal is to apply inference using those same model parameters when given representations computed over a new graph, unseen during model training, with minimal degradation in inference accuracy. This is in contrast to the more common task of doing inference on the unseen nodes of the same graph. We show that using random projections to estimate multiple powers of the transition matrix allows us to build a set of isomorphism-invariant features that can be used by a variety of tasks. The resulting features can be used to recover enough information about the local neighborhood of a node to enable inference with relevance competitive to other approaches while maintaining computational efficiency.
Quantum Computing Toolkit From Nuts and Bolts to Sack of Tools

Quantum computing has the potential to provide exponential performance benefits in processing over classical computing. It utilizes quantum mechanics phenomena (such as superposition, entanglement, and interference) to solve a computational problem. It can explore atypical patterns over data that classical computers can't perform efficiently. Quantum computers are in the nascent stage of development and are noisy due to decoherence, i.e., quantum bits deteriorate with environmental interactions. It will take a long time for quantum computers to achieve fault tolerance although quantum algorithms can be developed in advance. Heavy investment in developing quantum hardware, software development kits, and simulators has led to multiplicity of quantum development tools. Selection of a suitable development platform requires a proper understanding of the capabilities and limitations of these tools. Although a comprehensive comparison of the different quantum development tools would be of great value, to the best of our knowledge, no such extensive study is currently available.