Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient design of sparse FIR filters in WLS sense

A novel algorithm is presented in this paper to design sparse FIR filters in the weighted least-squares (WLS) sense. The original design problem is cast as a constrained l0-norm optimization problem. To tackle the nonconvexity, an efficient iterative procedure is developed. In each iterative step, a subproblem in a simpler form is constructed. It can be demonstrated that in each iteration an optimal solution to each subproblem can be efficiently and reliably attained by the successive activation algorithm proposed in this paper, such that the overall design algorithm can converge to a local solution of the original design problem. Since its major part only involves scalar operations, compared with other sparse filter design approaches, the proposed design algorithm is computationally efficient. The effectiveness of the proposed design algorithm is demonstrated by numerical examples.
Recent advances in renal transplantation: antibody-mediated rejection takes center stage

Overlooked for decades, antibodies have taken center stage in renal transplantation and are now widely recognized as the first cause of allograft failure. Diagnosis of antibody-mediated rejection has considerably improved with identification of antibody-mediated lesions in graft biopsies and advances made in the detection of circulating donor-specific antibodies. Unfortunately, this progress has not yet translated into better outcomes for patients. Indeed, in the absence of a drug able to suppress antibody generation by plasma cells, available therapies can only slow down graft destruction. This review provides an overview of the current knowledge of antibody-mediated rejection and discusses future interesting research directions.
The extra-galactic Cepheid distance scale from LMC and Galactic period-luminosity relations

In this paper, we recalibrate the Cepheid distance to some nearby galaxies observed by the HST Key Project and the Sandage-Tammann-Saha group. We use much of the Key Project methodology in our analysis but apply new techniques, based on Fourier methods to estimate the mean of a sparsely sampled Cepheid light curve, to published extra-galactic Cepheid data. We also apply different calibrating PL relations to estimate Cepheid distances, and investigate the sensitivity of the distance moduli to the adopted calibrating PL relation. We re-determine the OGLE LMC PL relations using a more conservative approach and also study the effect of using Galactic PL relations on the distance scale. For the Key Project galaxies after accounting for charge transfer effects, we find good agreement with an average discrepancy of -0.002 and 0.075 mag when using the LMC and Galaxy, respectively, as a calibrating PL relation. For NGC 4258 which has a geometric distance of 29.28 mag, we find a distance modulus of 29.44 ′ 0.06(random) mag, after correcting for metallicity. In addition we have calculated the Cepheid distance to 8 galaxies observed by the Sandage-Tammann-Saha group and find shorter distance moduli by -0.178 mag (mainly due to the use of different LMC PL relations) and -0.108 mag on average again when using the LMC and Galaxy, respectively, as a calibrating PL relation. However care must be taken to extrapolate these changed distances to changes in the resulting values of the Hubble constant because STS also use distances to NGC 3368 and 4414 and because STS calibration of SN Ia is often decoupled from the distance to the host galaxy through their use of differential extinction arguments. We also calculate the distance to all these galaxies using PL relations at maximum light and find very good agreement with mean light PL distances. However, after correcting for metallicity effects, the difference between the distance moduli obtained using the two sets of calibrating PL relations becomes negligible. This suggests that Cepheids in the LMC and Galaxy do follow different PL relations and constrains the sign for the coefficient of the metallicity correction, y, to be negative, at least at the median period log(P) 1.4, of the target galaxies.
The time course of visual field recovery and changes of retinal ganglion cells after optic chiasmal decompression.

PURPOSE To investigate the time course of visual field recovery and changes of retinal ganglion cells (RGCs) after chiasmal decompression using standard automated perimetry (SAP), optical coherence tomography (OCT), and photopic negative response (PhNR). METHODS Nineteen patients undergoing chiasmal decompression surgery were prospectively assessed before and 1 and 3 months after surgery. The same examinations were conducted in nine patients at 6 months after surgery. Mean deviation and temporal visual field sensitivity (1/Lambert) of SAP, retinal nerve fiber layer (RNFL) thickness, and ganglion cell complex (GCC) area measured by OCT and PhNR/b-wave ratio were analyzed. Preoperative measurements were compared with those of 20 eyes of normal controls. Postoperative measurements were compared with preoperative data. The relationships among SAP, OCT, and PhNR measurements throughout the observation periods were evaluated by linear and logarithmic regressions. RESULTS Before surgery, all parameters in patients were significantly worse than those in normal controls. After surgery, the visual field was significantly improved, but RNFL thickness and GCC area were significantly reduced for 3 months. The PhNR/b-wave ratio was also reduced, but not significantly. Six months after surgery, average RNFL thickness, GCC area, and PhNR/b-wave ratio showed significant improvements by 2.82%, 2.66%, and 8.72%, respectively, than those at 3 months. Visual fields were significantly correlated with RNFL thickness, GCC area, and PhNR/b-wave ratio. CONCLUSIONS Visual field recovery and changes of RGCs after chiasmal decompression have similar aspects and are significantly correlated. However, prolonged retrograde degeneration progressed for some period, even after surgical decompression and visual field recovery preceded demonstrable retinal regeneration.
Rapid design space exploration for multi parametric optimization of VLSI designs

Design Space Exploration (DSE) is one of the most important stages in High Level Synthesis designing methodology. This paper presents a novel DSE approach for the current generation of systems with heterogeneous multi parametric optimization objectives. The method introduced in this paper is capable of concurrently resolving multiple conflicting issues encountered during DSE, such as maximization of accuracy needed in the evaluation of design space with minimization in time expended to explore the best architecture. Results of the proposed method for different benchmarks indicated significant acceleration in exploration process compared to another existing approach that is also based on Pareto optimal analysis.
Aspects of the Theory of Comprehension, Memory and Attention

This paper proposed a two-stage model to capture some basic relations between attention, comprehension and memory for sentences. According to the model, the first stage of linguistic processing is carried out in short-term memory (M1) and involves a superficial analysis of semantic and syntactic features of words. The second stage is carried out in long-term memory (M2) and involves application of transformational rules to the analyses of M1 so as to determine the deep or underlying relations among words and phrases. According to the theory, attention is an M2 process: preliminary analyses by M1 are carried out even for unattended inputs, but final analyses by M2 are only carried out for attended inputs. The theory was shown to be consistent with established facts concerning memory, attention and comprehension, and additional support for the theory was obtained in a series of dichotic listening experiments.
Ticagrelor regulates osteoblast and osteoclast function and promotes bone formation in vivo via an adenosine‐dependent mechanism

As many as 10% of bone fractures heal poorly, and large bone defects resulting fromtrauma, tumor, or infection may not heal without surgical intervention. Activation of adenosine A2A receptors (A2ARs) stimulates bone formation. Ticagrelor and dipyridamole inhibit platelet function by inhibiting P2Y12 receptors and platelet phosphodiesterase, respectively, but share the capacity to inhibit cellularuptake of adenosine and thereby increase extracellular adenosine levels. Because dipyridamole promotes bone regeneration by an A2A R‐mediated mechanism we determined whether ticagrelor could regulate the cells involved in bone homeostasis and regeneration in a murine model and whether inhibition of P2Y12 or indirect A2AR activation via adenosine was involved. Ticagrelor, dipyridamole and the active metabolite of clopidogrel (CAM), an alternative P2Y12 antagonist, inhibited osteoclast differentiation and promoted osteoblast differentiation in vitro. A2AR blockade abrogated the effects of ticagrelor and dipyridamole on osteoclast and osteoblast differentiation where as A2BR blockade abrogated the effects of CAM. Ticagrelor and CAM, when applied to a 3‐dimentional printed resorbable calcium‐triphosphate/ hydroxyapatite scaffold implanted in a calvarial bone defect, promoted significantly more bone regeneration than the scaffold alone and as much bone regeneration as BMP‐2, a growth factor currently used to promote bone regeneration. These results suggest novel approaches to targeting adenosine receptors in the promotion of bone regeneration. —Mediero, A. , Wilder, T. , Reddy, V. S. R. , Cheng, Q. , Tovar, N. , Coelho, P. G. , Witek, L. , Whatling, C. , Cronstein, B. N. Ticagrelor regulates osteoblast and osteoclast function and promotes bone formation in vivo via an adenosine‐dependent mechanism. FASEB J. 30, 3887–3900 (2016) www.fasebj. org
Towards a debugging system for sensor networks

Due to their resource constraints and tight physical coupling, sensor networks afford limited visibility into an application's behavior. As a result it is often difficult to debug issues that arise during development and deployment. Existing techniques for fault management focus on fault tolerance or detection; before we can detect anomalous behavior in sensor networks, we need first to identify what simple metrics can be used to infer system health and correct behavior. We propose metrics and events that enable system health inferences, and present a preliminary design of Sympathy, a debugging tool for pre‐ and post‐deployment sensor networks. Sympathy will contain mechanisms for collecting system performance metrics with minimal memory overhead; mechanisms for recognizing application‐defined events based on these metrics; and a system for collecting events in their spatiotemporal context. The Sympathy system will help programmers draw correlations between seemingly unrelated, distributed events, and produce graphs that highlight those correlations. As an example, we describe how we used a preliminary version of Sympathy to help debug a complex application, Tiny Diffusion. Copyright © 2005 John Wiley & Sons, Ltd.
Paraxial Coupling of Electromagnetic Waves in Random Media

We consider the propagation of temporally pulsed electromagnetic waves in a three-dimensional random medium. The main objective is to derive effective white-noise paraxial equations from Maxwell's equations. We address the scaling regime in which (1) the carrier wavelength is small compared to the incident beam radius, which itself is small compared to the propagation distance; (2) the correlation length of the fluctuations of the random medium is of the same order as the beam radius, and the typical amplitude of the fluctuations is small. In this regime we prove that the wave field is characterized by a white-noise paraxial wave equation that has the form of a Schrodinger-type equation driven by a Brownian field. We identify the covariance function of the Brownian field in terms of the two-point statistics of the fluctuations of the dielectric permittivity and the magnetic permeability of the medium. We also study the case in which a strong interface is embedded in the random medium and study the reflect.
Improving Protein Function Prediction using the Hierarchical Structure of the Gene Ontology

High performance and accurate protein function prediction is an important problem in molecular biology. Many contemporary ontologies, such as Gene Ontology (GO), have a hierarchical structure that can be exploited to improve the prediction accuracy, and lower the computational cost, of protein function prediction. We leverage the hierarchical structure of the ontology in two ways. First, we present a method of creating hierarchy-aware training sets for machine-learned classifiers and we show that, in the case of GO molecular function, it is the most accurate method compared to not considering the hierarchy during training. Second, we use the hierarchy to reduce the computational cost of classification. We also introduce a sound methodology for evaluating hierarchical classifiers using global cross-validation. Biologists often use sequence similarity (e.g. BLAST) to identify a " nearest neighbor" sequence and use the database annotations of this neighbor to predict protein function. In these cases, we use the hierarchy to improve accuracy by a small amount. When no similar sequences can be found (which is true for up to 40% of some common proteomes), our technique can improve accuracy by a more significant amount. Although this paper focuses on a specific important application-protein function prediction for the GO hierarchy-the techniques may be applied to any classification problem over a hierarchical ontology.
Using behavioral exploration objectives to solve deceptive problems in neuro-evolution

Encouraging exploration, typically by preserving the diversity within the population, is one of the most common method to improve the behavior of evolutionary algorithms with deceptive fitness functions. Most of the published approaches to stimulate exploration rely on a distance between genotypes or phenotypes; however, such distances are difficult to compute when evolving neural networks due to (1) the algorithmic complexity of graph similarity measures, (2) the competing conventions problem and (3) the complexity of most neural-network encodings. In this paper, we introduce and compare two conceptually simple, yet efficient methods to improve exploration and avoid premature convergence when evolving both the topology and the parameters of neural networks. The two proposed methods, respectively called behavioral novelty and behavioral diversity, are built on multiobjective evolutionary algorithms and on a user-defined distance between behaviors. They can be employed with any genotype. We benchmarked them on the evolution of a neural network to compute a Boolean function with a deceptive fitness. The results obtained with the two proposed methods are statistically similar to those of NEAT and substantially better than those of the control experiment and of a phenotype-based diversity mechanism.
Problem structure and flowshop scheduling

ID: A234 Abstract Many algorithms leverage structure to solve problems. In owshop scheduling, we study two types of structure. First, for each job, we generate processing times that are similar across all machines (job-correlated problems). Then, for each machine, we generate processing times that are similar for all the jobs (machine-correlated problems). For these types of problems, we study the innuence of structure on the search landscape generated by local search operators. It has been shown that for randomly generated problems the shift operator induces a big-valley structure. We show that for both job-correlated and machine-correlated problems the big-valley structure degrades into a stepped-valley structure composed of plateaus of equivalent tness solutions. A good lower bound is useful when assessing the quality of a owshop scheduling solution. For job-correlated owshop problems, the traditional lower bound computation is extremely poor. We present a new, tighter lower bound computation and prove its correctness.
The Future of Arrhythmias and Electrophysiology

Cardiac arrhythmias are a major cause of morbidity and mortality in heart disease and are the likely cause of more than a quarter of a million deaths annually in the United States alone. Although our current approach to reducing suffering and death from arrhythmias relies largely on diagnosis and therapy aimed at early recognition and treatment, the benefits of prevention could be even more substantial. As we consider how the approach to arrhythmias may evolve in the coming decades, it is useful to consider the great breadth of presentations and scenarios that are encountered. Human arrhythmias occur in 2 scenarios. First, particularly early in life, they arise as a consequence of inborn (congenital) constellations of anatomy and electrophysiology that change over time. There may be no structural cardiac abnormality (eg, long-QT syndrome), a minimal structural abnormality (eg, an accessory pathway causing Wolff-Parkinson-White syndrome), or a severe structural abnormality (eg, endocardial cushion defect with heart block). Second, arrhythmias emerge later in life as a consequence of acquired disease (eg, ventricular tachycardia [VT] late after myocardial infarction) or aging (atrial fibrillation [AF]). Genetic susceptibilities influence all aspects of the pathophysiology. As approaches to prevention and therapy evolve, genetic influences will receive increasing attention. ### Advances in Monitoring and Detection of Arrhythmias: Moving Care From the Office to the Home Mobile applications and monitors are revolutionizing the way we interact with patients who have or are at risk for arrhythmias. Over the past decade, major advances have occurred in both external and implantable monitoring devices. External ambulatory monitors have decreased in size and complexity while the storage capacity has increased. Self-contained wireless patch monitors that self-adhere to the patient’s chest are capable of recording and storing single-lead ECG recordings for up to 30 days. Monitoring can be extended further, up to 3 years, with subcutaneously implanted loop recorders. These implanted devices have already been miniaturized to the size of …
Emerging Biological Treatments for Uterine Cervical Carcinoma

Cervical cancer is the third most common cancer worldwide, and the development of new diagnosis, prognostic, and treatment strategies is a major interest for public health. Cisplatin, in combination with external beam irradiation for locally advanced disease, or as monotherapy for recurrent/metastatic disease, has been the cornerstone of treatment for more than two decades. Other investigated cytotoxic therapies include paclitaxel, ifosfamide and topotecan, as single agents or in combination, revealing unsatisfactory results. In recent years, much effort has been made towards evaluating new drugs and developing innovative therapies to treat cervical cancer. Among the most investigated molecular targets are epidermal growth factor receptor and vascular endothelial growth factor (VEGF) signaling pathways, both playing a critical role in cervical cancer development. Studies with bevacizumab or VEGF receptor tyrosine kinase have given encouraging results in terms of clinical efficacy, without adding significant toxicity. A great number of other molecular agents targeting critical pathways in cervical malignant transformation are being evaluated in preclinical and clinical trials, reporting preliminary promising data. In the current review, we discuss novel therapeutic strategies which are being investigated for the treatment of advanced cervical cancer.
Design and evaluation of speculative multithreading with selective multi-path execution

Thread Level Parallelism (TLP) is the most promising way to the futur e high-p erformance microprocessors. When a sequential program code is speculatively execute din a multithreaded manner, it is natural that each speculative thread follows the control ow of the program. Based on this backgr ound, various studies have b en performed on speculative multithreading, following control ow graph. This pap erprop osesa new execution model that aims at the speedup of the execution of usual sequential program codes by sp eculatively executing the multiple contr ol ows in parallel. Further, this paper shows the thread contr olmechanism and the inter-thread communication facility requir ed for the realization of the model. F or the evaluation of our model, the cycle-b asedinstruction-level simulator has been developed. We evaluate our model with simple benchmark programs and show the e ectiveness of our model especially, for the case where the speculation by single c ontr ol ow is di cult to sp eedup the programs.
Numerical analysis of time-dependent inhibition kinetics: comparison between rat liver microsomes and rat hepatocyte data for mechanistic model fitting

Abstract Time-dependent inhibition (TDI) may confound drug interaction predictions. Recently, models were generated for an array of TDI kinetic schemes using numerical analysis of microsomal assays. Additionally, a distinct terminal inactivation step was identified for certain mechanism based inhibitors (MBI) following reversible metabolite intermediate complex (MIC) formation. Longer hepatocyte incubations potentially allow analysis of slow TDI and terminal inactivation. In the experiments presented here, we compared the quality of TDI parameterization by numerical analysis between hepatocyte and microsomal data. Rat liver microsomes (RLM), suspended rat hepatocytes (SRH) and sandwich-cultured rat hepatocytes (SCRH) were incubated with the prototypical CYP3A MBI troleandomycin and the substrate midazolam. Data from RLM provided a better model fit as compared to SRH. Increased CYP3A expression after dexamethasone (DEX) induction improved the fit for RLM and SRH. A novel sequential kinetic scheme, defining inhibitor metabolite production prior to MIC formation, improved the fit compared to direct MIC formation. Furthermore, terminal inactivation rate constants were parameterized for RLM and SRH samples with DEX-induced CYP3A. The low expression of CYP3A and experimental error in SCRH resulted in poor data for model fitting. Overall, RLM generated data better suited for elucidation of TDI mechanisms by numerical analysis.