Miracle-dz/UniFAR-FTUs huggingface dataset:
Paricalcitol Attenuates Cardiac Fibrosis and Expression of Endothelial Cell Transition Markers in Isoproterenol-Induced Cardiomyopathic Rats

Objectives: Acute cardiomyopathy is a health problem worldwide. Few studies have shown an association between acute cardiomyopathy and low vitamin D status. Paricalcitol, a vitamin D receptor activator, clinically benefits patients with advanced kidney disease. The effect of paricalcitol supplement on cardiac remodeling in cardiomyopathic rats is unknown. This experimental study investigated the effect of paricalcitol in rats with cardiomyopathy induced by isoproterenol. Design: Prospective, randomized, controlled experimental study. Setting: Hospital-affiliated animal research institution. Subjects: Eight-week-old male Wistar-Kyoto rats. Interventions: Male Wistar-Kyoto rats were first injected intraperitoneally with isoproterenol to create a rat model of acute cardiomyopathy. Then paricalcitol was administered intraperitoneally to isoproterenol-injected rats at a dosage of 200 ng three times a week for 3 weeks. Relevant cardiomyopathy-related variables were measured regularly in three groups of rats, controls, isoproterenol, and isoproterenol plus paricalcitol. Rat hearts were obtained for evaluation of cardiac fibrosis using Masson trichrome staining and commercially available software, and evaluation of cell transition using immunofluorescence staining analysis. Measurements and Main Results: Isoproterenol infusions generated significant cardiac fibrosis (p < 0.001). Subsequent paricalcitol treatment attenuated the isoproterenol-induced cardiac fibrosis (p = 0.006). Fluorescence showed colocalization of endothelial and fibroblast cell markers (cluster differentiation 31 and &agr;-smooth muscle actin, respectively) in the isoproterenol-treated hearts. Paricalcitol injections attenuated the isoproterenol-induced fluorescence intensity of two cell markers (p < 0.01). Conclusions: Paricalcitol injections may ameliorate isoproterenol-induced cardiac fibrosis possibly through regulating cell transition.
Performance Isolation and Fairness for Multi-Tenant Cloud Storage

Shared storage services enjoy wide adoption in commercial clouds. But most systems today provide weak performance isolation and fairness between tenants, if at all. Misbehaving or high-demand tenants can overload the shared service and disrupt other well-behaved tenants, leading to unpredictable performance and violating SLAs. This paper presents Pisces, a system for achieving datacenter-wide per-tenant performance isolation and fairness in shared key-value storage. Today's approaches for multi-tenant resource allocation are based either on per-VM allocations or hard rate limits that assume uniform workloads to achieve high utilization. Pisces achieves per-tenant weighted fair shares (or minimal rates) of the aggregate resources of the shared service, even when different tenants' partitions are co-located and when demand for different partitions is skewed, time-varying, or bottlenecked by different server resources. Pisces does so by decomposing the fair sharing problem into a combination of four complementary mechanisms--partition placement, weight allocation, replica selection, and weighted fair queuing--that operate on different time-scales and combine to provide system-wide max-min fairness. An evaluation of our Pisces storage prototype achieves nearly ideal (0.99 Min-Max Ratio) weighted fair sharing, strong performance isolation, and robustness to skew and shifts in tenant demand. These properties are achieved with minimal overhead (<3%), even when running at high utilization (more than 400,000 requests/second/server for 10B requests).
Emergence of function from coordinated cells in a tissue

Significance A basic problem in biology is understanding how information from a single genome gives rise to function in a mature multicellular tissue. Genome dynamics stabilize to give rise to a protein distribution in a given cell type, which in turn gives rise to the identity of a cell. We build a highly idealized mathematical foundation that combines the genome (within cell) and the diffusion (between cell) dynamical forces. The trade-off between these forces gives rise to the emergence of function. We define emergence as the coordinated effect of individual components that establishes an objective not possible for an individual component. Our setting of emergence may further our understanding of normal tissue function and dysfunctional states such as cancer. This work presents a mathematical study of tissue dynamics. We combine within-cell genome dynamics and diffusion between cells, so that the synthesis of the two gives rise to the emergence of function, akin to establishing “tissue homeostasis. ” We introduce two concepts, monotonicity and a weak version of hardwiring. These together are sufficient for global convergence of the tissue dynamics.
Combinatorial auctions with restricted complements

Complements between goods--where one good takes on added value in the presence of another--have been a thorn in the side of algorithmic mechanism designers. On the one hand, complements are common in the standard motivating applications for combinatorial auctions, like spectrum license auctions. On the other, welfare maximization in the presence of complements is notoriously difficult, and this intractability has stymied theoretical progress in the area. For example, there are no known positive results for combinatorial auctions in which bidder valuations are multi-parameter and non-complement-free, other than the relatively weak results known for general valuations. To make inroads on the problem of combinatorial auction design in the presence of complements, we propose a model for valuations with complements that is parameterized by the "size" of the complements. The model permits a succinct representation, a variety of computationally efficient queries, and non-trivial welfare-maximization algorithms and mechanisms. Specifically, a hypergraph-r valuation v for a good set M is represented by a hypergraph H = (M,E), where every (hyper-)edge e -- E contains at most r vertices and has a nonnegative weight we. Each good j -- M also has a nonnegative weight wj. The value v(S) for a subset S ⊆ M of goods is defined as the sum of the weights of the goods and edges entirely contained in S. We design the following polynomial-time approximation algorithms and truthful mechanisms for welfare maximization with bidders with hypergraph valuations. (1) For bidders whose valuations correspond to subgraphs of a known graph that is planar (or more generally, excludes a fixed minor), we give a truthful and (1 + ∈)-approximate mechanism. (2) We give a polynomial-time, r-approximation algorithm for welfare maximization with hypergraph-r valuations. Our algorithm randomly rounds a compact linear programming relaxation of the problem. (3) We design a different approximation algorithm and use it to give a polynomial-time, truthful-inexpectation mechanism that has an approximation factor of O(logr m).
RISC-based moving threads multicore architecture

In this paper, we describe the architectural output of our `Moving threads realization study' (MOTH) project, which is a RISC-based multicore architecture framework. Each fraction of the memory can be accesses only via a certain core, via its cache memory. This approach leads to moving light-weight threads but at the same time provides strong memory coherences as no main memory location is replicated to several caches. We describe the overall multicore architecture, but special emphasis is put on describing the functionality of individual RISC-based core.
The anatomy of a large-scale human computation engine

In this paper we describe Rabj, an engine designed to simplify collecting human input. We have used Rabj to collect over 2.3 million human judgments to augment data mining, data entry, and curation tasks at Freebase over the course of a year. We illustrate several successful applications that have used Rabj to collect human judgment. We describe how the architecture and design decisions of Rabj are affected by the constraints of content agnosticity, data freshness, latency and visibility. We present work aimed at increasing the yield and reliability of human computation efforts. Finally, we discuss empirical observations and lessons learned in the course of a year of operating the service.
Harnessing the hygroscopic and biofluorescent behaviors of genetically tractable microbial cells to design biohybrid wearables

We harnessed the hygroscopic and biofluorescent behaviors of microbial cells to design sweat-responsive biohybrid wearables. Cells’ biomechanical responses to external stimuli have been intensively studied but rarely implemented into devices that interact with the human body. We demonstrate that the hygroscopic and biofluorescent behaviors of living cells can be engineered to design biohybrid wearables, which give multifunctional responsiveness to human sweat. By depositing genetically tractable microbes on a humidity-inert material to form a heterogeneous multilayered structure, we obtained biohybrid films that can reversibly change shape and biofluorescence intensity within a few seconds in response to environmental humidity gradients. Experimental characterization and mechanical modeling of the film were performed to guide the design of a wearable running suit and a fluorescent shoe prototype with bio-flaps that dynamically modulates ventilation in synergy with the body’s need for cooling.
Active disturbance rejection control: a paradigm shift in feedback control system design

The question addressed in this paper is: just what do we need to know about a process in order to control it? With active disturbance rejection, perhaps we don't need to know as much as we were told. In fact, it is shown that the unknown dynamics and disturbance can be actively estimated and compensated in real time and this makes the feedback control more robust and less dependent on the detailed mathematical model of the physical process. In this paper we first examine the basic premises in the existing paradigms, from which it is argued that a paradigm shift is necessary. Using a motion control metaphor, the basis of such a shift, the active disturbance rejection control, is introduced. Stability analysis and applications are presented. Finally, the characteristics and significance of the new paradigm are discussed
ADAPTIVE MULTILEVEL SPLITTING FOR RARE EVENT ANALYSIS

: The estimation of rare event probability is a crucial issue in areas such as reliability, telecommunications, aircraft management. In complex systems, analytical study is out of question and one has to use Monte Carlo methods. When rare is really rare, which means a probability less than 10 − 9 , naive Monte Carlo becomes unreasonable. A widespread technique consists in multilevel splitting, but this method requires enough knowledge about the system to decide where to put the levels at hand. This is unfortunately not always possible. In this paper, we propose an adaptive algorithm to cope with this problem: the estimation is asymptotically consistent, costs just a little bit more than classical multilevel splitting and has the same eﬃciency in terms of asymptotic variance. In the one dimensional case, we prove rigorously the a.s. convergence and the asymptotic normality of our estimator, with the same variance as with other algorithms that use ﬁxed crossing levels. In our proofs we mainly use tools from the theory of empirical processes, which seems to be quite new in the ﬁeld of rare events.
Real-time Pedestrian Detection Using LIDAR and Convolutional Neural Networks

This paper presents a novel real-time pedestrian detection system utilizing a LIDAR-based object detector and convolutional neural network (CNN)-based image classifier. Our method achieves over 10 frames/second processing speed by constraining the search space using the range information from the LIDAR. The image region candidates detected by the LIDAR are confirmed for the presence of pedestrians by a convolutional neural network classifier. Our CNN classifier achieves high accuracy at a low computational cost thanks to its ability to automatically learn a small number of highly discriminating features. The focus of this paper is the evaluation of the effect of region of interest (ROI) detection on system accuracy and processing speed. The evaluation results indicate that the use of the LIDAR-based ROI detector can reduce the number of false positives by a factor of 2 and reduce the processing time by a factor of 4. The single frame detection accuracy of the system is above 90% when there is 1 false positive per second
AUTHENTICITY OF HONEY AND OTHER BEE PRODUCTS

Honey authenticity is defined by the Codex Alimentarius standard, the EU Honey Directive and several national legislations. The Codex and EU standards were recently revised. The authenticity of honey and other bee products have two aspects. Authenticity in respect of production i.e. to prevent adulteration by addition of other food ingredients by correct beekeeping practice and processing. The other aspect concerns authenticity in respect of geographical and botanical origin. As honeys from certain geographical regions or botanical sources attain higher prices than others, mislabelling is of economic interest. Today, the authenticity of the botanical origin of bee products is determined by sensory analysis, pollen analysis and several physico-chemical methods while for testing of the geographical authenticity melissopalynological methods are employed. These analyses, however are time-consuming and require specialized know-how and expertise. Objective evaluation techniques based on analytical chemistry and statistics should be developed for reliable authenticity testing in order to ensure the quality of the bee products as valuable natural products. The objective of this presentation is to discuss the different authenticity issues, the current methods used and to evaluate new methods for successful authenticity testing.
Visualizing 3D velocity fields near contour surfaces

Vector field rendering is difficult in 3D because the vector icons overlap and hide each other. We propose four different techniques for visualizing vector fields only near surfaces. The first uses motion blurred particles in a thickened region around the surface. The second uses a voxel grid to contain integral curves of the vector field. The third uses many antialiased lines through the surface, and the fourth uses hairs sprouting from the surface and then bending in the direction of the vector field. All the methods use the graphics pipeline, allowing real time rotation and interaction, and the first two methods can animate the texture to move in the flow determined by the velocity field. <<ETX>>
TRANSFORM as Adaptive and Dynamic Furniture

TRANSFORM is an exploration of how shape display technology can be integrated into our everyday lives as interactive, shape changing furniture. These interfaces not only serve as traditional computing devices, but also support a variety of physical activities. By creating shapes on demand or by moving objects around, TRANSFORM changes the ergonomics, functionality and aesthetic dimensions of furniture. The video depicts a story with various scenarios of how TRANSFORM shape shifts to support a variety of use cases in the home and in the work environment: It holds and moves objects like fruits, game tokens, office supplies and tablets; creates dividers on demand; and generates interactive sculptures to convey messages and audio.
Feedback-controlled WLAN: Achieving Channel-Availability in Mesh Networks

Wireless mesh networks are a promising backbone-solution for applications in industrial automation. However, high availability of the communication channel is not inherently guaranteed because of environment-dynamics and unauthorized users congesting the medium. In this paper we propose a method to increase channel-availability. We use an automated coverage planning method with feedback from the environment to cope with environment-dynamics. Unauthorized users can be automatically localized: this eases their finding and elimination. Our experimental evaluation shows that the method estimates the actual network coverage with an accuracy of 10 dB; users are located with 3.7 m accuracy on average. The main improvement of the coverage planning and localization methods is the achieved autonomy in dynamic environments. Therefore channel-availability also increases.
Mechanical and thermal characterization of carbon nanofiber reinforced low-density polyethylene composites

The mechanical, thermal, and fracture morphological characterization of low-density polyethylene (LDPE) composites reinforced with vapor-grown carbon nanofibers (VGCNFs) is presented in this article. VGCNF/LDPE composites with different VGCNF weight percentages were prepared by single-screw extrusion followed by injection molding. Scanning electron microscopy showed that VGCNFs were predominantly dispersed uniformly within the LDPE matrix. Differential scanning calorimetry has shown that the VGCNF/LDPE composites crystallized at higher temperatures than pure LDPE polymer. Tensile testing has shown that strength and Young’s modulus of VGCNF/LDPE composites were improved by 15% and 44%, respectively, when the VGCNF loading reached 3 wt%.
Phase I and pharmacokinetic study of NSC 655649, a rebeccamycin analog with topoisomerase inhibitory properties.

PURPOSE To assess the feasibility of administering NSC 655649, a water-soluble, rebeccamycin analog with topoisomerase inhibitory properties, as a brief intravenous (IV) infusion once every 3 weeks and to determine the maximum-tolerated dose (MTD) of NSC 655649, characterize its pharmacokinetic behavior, and seek preliminary evidence of antitumor activity. PATIENTS AND METHODS Patients with advanced solid malignancies were treated with escalating doses of NSC 655649 administered over 30 to 60 minutes IV once every 3 weeks. An accelerated dose-escalation method was used to guide dose escalation. After three patients were treated at the first dose level, doses were escalated in increments that ranged up to 150% using single patient cohorts until moderate toxicity was observed, when a more conservative dose-escalation scheme was invoked. MTD was defined as the highest dose level at which the incidence of dose-limiting toxicity did not exceed 20%. MTD was determined for both minimally pretreated (MP) and heavily pretreated (HP) patients. Plasma and urine were sampled to characterize the pharmacokinetic and excretory behavior of NSC 655649. RESULTS Forty-five patients were treated with 130 courses of NSC 655649 at doses ranging from 20 mg/m(2) to 744 mg/m(2). Myelosuppression was the principal toxicity. Severe neutropenia, which was often associated with thrombocytopenia, was unacceptably high in HP and MP patients treated at 572 mg/m(2) and 744 mg/m(2), respectively. Nausea, vomiting, and diarrhea were common but rarely severe. The pharmacokinetics of NSC 655649 were dose dependent and fit a three-compartment model. The clearance and terminal elimination half-lives for NSC 655649 averaged 7.57 (SD = 4.2) L/h/m(2) and 48.85 (SD = 23.65) hours, respectively. Despite a heterogeneous population of MP and HP patients, the magnitude of drug exposure correlated well with the severity of myelosuppression. Antitumor activity was observed in two HP ovarian cancer patients and one patient with a soft tissue sarcoma refractory to etoposide and doxorubicin. CONCLUSION Recommended phase II doses are 500 mg/m(2) and 572 mg/m(2) IV once every 3 weeks for HP and MP patients, respectively. The absence of severe nonhematologic toxicities, the encouraging antitumor activity in HP patients, and the unique mechanism of antineoplastic activity of NSC 655649 warrant further clinical development.
Self-control of chaotic dynamics using LTI filters

In this brief, an algorithm for controlling chaotic systems using small, continuous-time perturbations is presented. Stabilization is achieved by self controlling feedback using low order LTI filters. The algorithm alleviates the need of complex calculations or costly delay elements and can be implemented in a wide variety of systems using simple circuit elements only.
Analyzing gene perturbation screens with nested effects models in R and bioconductor

UNLABELLED Nested effects models (NEMs) are a class of probabilistic models introduced to analyze the effects of gene perturbation screens visible in high-dimensional phenotypes like microarrays or cell morphology. NEMs reverse engineer upstream/downstream relations of cellular signaling cascades. NEMs take as input a set of candidate pathway genes and phenotypic profiles of perturbing these genes. NEMs return a pathway structure explaining the observed perturbation effects. Here, we describe the package nem, an open-source software to efficiently infer NEMs from data. Our software implements several search algorithms for model fitting and is applicable to a wide range of different data types and representations. The methods we present summarize the current state-of-the-art in NEMs. AVAILABILITY Our software is written in the R language and freely avail-able via the Bioconductor project at http://www.bioconductor. org.
Recognizing connected digits in a natural spoken dialog

This paper addresses the general problem of connected digit recognition in the telecommunication environment. In particular, we focus on a task of recognizing digits when embedded in a natural spoken dialog. Two different design strategies are investigated: keyword detection of word spotting, and large-vocabulary continuous speech recognition. We characterize the potential benefits and describe the main components of each design method, including acoustic and language modeling, training and utterance verification. Experimental results on a subset of a database that includes customers responses to the open-ended prompt "How may I help you? " are presented.
Abstractions considered helpful: A tools architecture for quantum annealers

Today's usable quantum computers, variously known as adiabatic quantum computers or quantum annealers and exemplified by the D-Wave 2X™ system, have an instruction set architecture foreign to mainstream classical computers and thus require a new class of programming tools to enable their widespread use. We submit that well-chosen abstractions, each balancing the ability of high- and low-level tools to use it, will play an essential role in fostering a vibrant ecosystem of such new tools. We propose the virtual quadratic unconstrained binary optimization (vQUBO) problem as one such abstraction and describe our experience in implementing and using it. As one step toward an effective quantum computing ecosystem, we invite other tool developers to create complementary tools that map from user problems to the vQUBO form for end-to-end usability and performance.
A framework for optimization of turbulent wind-farm boundary layers and application to optimal control of wind-farm energy extraction

A PDE-based optimization framework is presented that allows optimization of turbulent wind-farm boundary layers. It consists of a state-of-the-art large-eddy simulation code that allows the time-resolved simulation of the three-dimensional turbulent flow in the atmospheric boundary layer, together with the adjoint (backward) sensitivity equations to this nonlinear system of PDEs (i.e. the incompressible Navier-Stokes equations). Both the forward and the backward system are efficiently parallelized for supercomputing, and are combined with state-of-the-art gradient-based optimization methods. We use this tool to investigate the use of optimal coordinated control of wind-farm boundary-layer interaction with the aim of increasing the total energy extraction in wind farms. The individual wind turbines are considered as flow actuators and their energy extraction is dynamically regulated in time so as to optimally influence the flow field. Earlier work on wind-farm optimal control in the fully developed regime (Goit & Meyers 2015, J. Fluid Mech. 768, 550) is discussed, and extended towards wind farms in which inflow effects are important.