Miracle-dz/UniFAR-FTUs huggingface dataset:
SPATL: Honey, I Shrunk the Coherence Directory

One of the key scalability challenges of on-chip coherence in a multicore chip is the coherence directory, which provides information on sharing of cache blocks. Shadow tags that duplicate entire private cache tag arrays are widely used to minimize area overhead, but require an energy-intensive associative search to obtain the sharing information. Recent research proposed a Tagless directory, which uses bloom filters to summarize the tags in a cache set. The Tagless directory associates the sharing vector with the bloom filter buckets to completely eliminate the associative lookup and reduce the directory overhead. However, Tagless still uses a full map sharing vector to represent the sharing information, resulting in remaining area and energy challenges with increasing core counts. In this paper, we first show that due to the regular nature of applications, many bloom filters essentially replicate the same sharing pattern. We next exploit the pattern commonality and propose SPATL (Sharing-pattern based Tagless Directory). SPATL exploits the sharing pattern commonality to decouple the sharing patterns from the bloom filters and eliminates the redundant copies of sharing patterns. SPATL works with both inclusive and noninclusive shared caches and provides 34% storage savings over Tagless, the previous most storage-efficient directory, at 16 cores. We study multiple strategies to periodically eliminate the false sharing that comes from combining sharing pattern compression with Tagless, and demonstrate that SPATL can achieve the same level of false sharers as Tagless with 5% extra bandwidth. Finally, we demonstrate that SPATL scales even better than an idealized directory and can support 1024-core chips with less than 1% of the private cache space for data parallel applications.
Computational methods for large-scale data in medical diagnostics

The bottleneck of the large-scale data processing has made bioinformatic analyses a crucial component in life sciences workflows. The two large fields in biomedical studies, whose rapid development in the recent years has strongly depended on computational methods, are genetics and proteomics. They both are strictly linked to each other, e.g. structural organization of the genome affects the variety of proteins in the organism; on the other hand, proteins are the crucial functional molecules that participate in the process of extracting the information encoded in the genome. In this thesis, we present selected bioinformatic methods and discuss their application in basic research as well as in clinical diagnostics.
Visual comparison for information visualization

Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. Increasingly, information visualization tools support such comparisons explicitly, beyond simply allowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we see that all designs are assembled from the building blocks of juxtaposition, superposition and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a general understanding of comparative visualization and facilitating the development of more comparative visualization tools.
A Framework for Energy-Optimal Cloud Robotic System under MIMO Wireless Channel

The advances of cloud computing and robot enable the cloud robotics become a newly emerging research hotspot of intelligent automobile robot. Lots of technical challenges of cloud robotics remain to be worked out. For example, the computation challenges, the communication challenges, and the security challenges. In this paper we intend to design an optimal execution framework for the cloud robotics under MIMO (multiple-input multiple-output) wireless channel model, which aims to minimize the energy consumption of the robot and extend the battery lifetime. The model of the energy consumption for cloud execution is derived, and the problem of optimization is analyzed during the process. Additionally, a model and optimization of the standalone execution by robot are proposed. Based on the models, we provide a theoretical framework, which decide whether to offloading task. Finally, the numerical results illustrate that the framework is significant for minimizing the energy consumption of robot.
Evaluation of four automated high-sensitivity C-reactive protein methods: implications for clinical and epidemiological applications.

BACKGROUND C-reactive protein (CRP) can provide prognostic information about the risk of developing atherosclerotic complications in apparently healthy patients. This new clinical application requires quantification of CRP concentrations below those traditionally measured in the clinical laboratory. METHODS The Dade Behring BN II, the Abbott IMx, the Diagnostic Products Corporation IMMULITE, and the Beckman Coulter IMMAGE are four automated analyzers with high-sensitivity CRP (hs-CRP) methods. We evaluated these assays for precision, linearity, and comparability with samples from 322 apparently healthy blood donors. RESULTS The imprecision (CV) of the BN II, IMx, IMMULITE, and IMMAGE methods was < or = 7.6%, < or = 12%, < or = 9.8%, and < or = 9.7% at 3.5 mg/L, respectively. The BN II, IMx, IMMULITE, and IMMAGE methods were linear down to < or = 0.30, < or = 0.32, < or = 0.85, and 2.26 mg/L, respectively. CRP concentrations demarcating each quartile in a healthy population were method dependent. The IMx method gave results comparable to the BN II method for values in the reference interval. The IMMULITE method had a positive intercept compared with the BN II method. The IMMAGE method demonstrated more scatter and a positive intercept compared with the BN II method, which may reflect the fact that it is a less sensitive assay. CONCLUSIONS The four hs-CRP methods exhibited differences in results for a healthy population. Additional standardization efforts are required to ensure that hs-CRP results can be related to large-scale epidemiologic studies.
Ckming of cDNA sequences of a progestin-regulated mRNA from MCF7 human breast cancer cells

A cDNA clone corresponding to an mRNA regulated by the progestin R5O2O, has been isolated by differential screening of a cDNA library from the MCF7 breast cancer cell line, which contains estrogen and progesterone receptors. This probe hybridized with a single species of poly A + RNA of 8-kb molecular weight as shown by Northern blot analysis and could also be used to total RNA preparation. This recombinant clone hybridized specifically to an mRNA coding for a 250,000 daltons protein when tranlated In vitro. This protein was Identical to the 250 kDa progestin-regulated protein that. we previously described (Biochem. Biophys. Res. Commun. 121, 421-427, 1984) as shown by immunoprecipitation with specific rabbit polyclonal antibodies. Dose-response curve and specificity studies show that the accumulation of the Pg8 mRNA and that of the 250-kDa protein was increased by 5 to 30-fold following progestin treatment and that this effect was mediated by the progesterone receptor. Time course of induction indicated that the accumulation of mRNA was rapid and preceded that of the protein. This is the first report on a cloned cDNA probe of progestin-regulated mRNA in human cell lines.
Responses of medial rectus motoneurons in monkeys with strabismus.

PURPOSE Monkeys reared under conditions of alternating monocular occlusion during their first few months of life show large horizontal strabismus, "A" patterns, and dissociated vertical deviation. "A" patterns manifest as an inappropriate horizontal component in the deviated eye during vertical eye movements (cross-axis movement). The objective of this study was to investigate response properties of medial rectus motoneurons (MRMNs) in relation to strabismus properties. METHODS Burst-tonic activity of 21 MRMNs in the oculomotor nucleus were recorded from two monkeys with exotropia as they performed horizontal and vertical smooth pursuit (0.2 Hz, ±10°) under monocular viewing conditions. Neuronal responses and horizontal component of eye movements were used to identify regression coefficients in a first-order model for each tracking condition. RESULTS Comparison of position, velocity, and constant parameter coefficients, estimated from horizontal tracking data with either eye viewing, showed no significant differences (P > 0.07), indicating that neuronal activity could account for the horizontal misalignment. Comparison of the position, velocity, and constant parameter coefficients estimated from horizontal tracking and the cross-axis condition showed no significant differences (P > 0.07), suggesting that motoneuron activity could account for most of the inappropriate horizontal cross-axis movement observed in the covered eye during vertical smooth pursuit. CONCLUSIONS These data suggest that, in animals with sensory-induced strabismus, central innervation to extraocular muscles is responsible for setting the state of strabismus. Mechanical factors such as muscle length adaptation (for horizontal misalignment) and pulley heterotopy or static torsion (for "A" patterns) likely do not play a major role in determining properties in a sensory-induced strabismus.
DiscoverCT Discover more about clinical trials 2015 Final Project Report UC Berkeley School of Information Team Members

The ClinicalTrials.gov registry is a public resource that contains valuable information about clinical research, yet most of the data lacks structure or easy accessibility. We aim to create an interface that allows researchers, trial sponsors, and patients and their physicians to easily understand the state of clinical research on particular conditions or at a specific institution. Ideally this interface would also provide tools that permit users to actively improve the quality of data in the registry so that it is more accessible to the general public.
Ligand-Dependent Opening of the Multiple AMPA Receptor Conductance States: A Concerted Model

Modulation of the properties of AMPA receptors at the post-synaptic membrane is one of the main suggested mechanisms underlying fast synaptic transmission in the central nervous system of vertebrates. Electrophysiological recordings of single channels stimulated with agonists showed that both recombinant and native AMPA receptors visit multiple conductance states in an agonist concentration dependent manner. We propose an allosteric model of the multiple conductance states based on concerted conformational transitions of the four subunits, as an iris diaphragm. Our model predicts that the thermodynamic behaviour of the conductance states upon full and partial agonist stimulations can be described with increased affinity of receptors as they progress to higher conductance states. The model also predicts the existence of AMPA receptors in non-liganded conductive substates. However, the probability of spontaneous openings decreases with increasing conductances. Finally, we predict that the large conductance states are stabilized within the rise phase of a whole-cell EPSC in glutamatergic hippocampal neurons. Our model provides a mechanistic link between ligand concentration and conductance states that can explain thermodynamic and kinetic features of AMPA receptor gating.
Hierarchical Evolution of Neural

In most applications of neuro-evolution, each individual in the population represents a complete neural network. Recent work on the SANE system, however, has demonstrated that evolving individual neurons often produces a more eecient genetic search. This paper explores the merits of neuro-evolution both at the neuron level and at the network level. While SANE can solve easy tasks in just a few generations, in tasks that require high precision, its progress often stalls and is exceeded by a standard, network-level evolution. In this paper, a new approach called Hierarchical SANE is presented that combines the advantages of both approaches by integrating two levels of evolution in a single framework. Hierarchical SANE couples the early explorative quality of SANE's neuron-level search with the late exploitative quality of a more standard network-level evolution. In a sophisticated robot arm manipulation task, Hierarchical SANE signiicantly outperformed both SANE and a standard, network-level neuro-evolution approach, suggesting that it can more eeciently solve a broad range of tasks.
Axonal PPARγ promotes neuronal regeneration after injury

PPARγ is a ligand‐activated nuclear receptor best known for its involvement in adipogenesis and glucose homeostasis. PPARγ activity has also been associated with neuroprotection in different neurological disorders, but the mechanisms involved in PPARγ effects in the nervous system are still unknown. Here we describe a new functional role for PPARγ in neuronal responses to injury. We found both PPAR transcripts and protein within sensory axons and observed an increase in PPARγ protein levels after sciatic nerve crush. This was correlated with increased retrograde transport of PPARγ after injury, increased association of PPARγ with the molecular motor dynein, and increased nuclear accumulation of PPARγ in cell bodies of sensory neurons. Furthermore, PPARγ antagonists attenuated the response of sensory neurons to sciatic nerve injury, and inhibited axonal growth of both sensory and cortical neurons in culture. Thus, axonal PPARγ is involved in neuronal injury responses required for axonal regeneration. Since PPARγ is a major molecular target of the thiazolidinedione (TZD) class of drugs used in the treatment of type II diabetes, several pharmaceutical agents with acceptable safety profiles in humans are available. Our findings provide motivation and rationale for the evaluation of such agents for efficacy in central and peripheral nerve injuries. © 2015 Wiley Periodicals, Inc. Develop Neurobiol 76: 688–701, 2016
Atom Decomposition with Adaptive Basis Selection Strategy for Matrix Completion

Estimating missing entries in matrices has attracted much attention due to its wide range of applications like image inpainting and video denoising, which are usually considered as low-rank matrix completion problems theoretically. It is common to consider nuclear norm as a surrogate of the rank operator since it is the tightest convex lower bound of the rank operator under certain conditions. However, most approaches based on nuclear norm minimization involve a number of singular value decomposition (SVD) operations. Given a matrix X ∈ Rm×n, the time complexity of the SVD operation is O(mn2), which brings prohibitive computational burden on large-scale matrices, limiting the further usage of these methods in real applications. Motivated by this observation, a series of atom-decomposition-based matrix completion methods have been studied. The key to these methods is to reconstruct the target matrix by pursuit methods in a greedy way, which only involves the computation of the top SVD and has great advantages in efficiency compared with the SVD-based matrix completion methods. However, due to gradually serious accumulation errors, atom-decomposition-based methods usually result in unsatisfactory reconstruction accuracy. In this article, we propose a new efficient and scalable atom decomposition algorithm for matrix completion called Adaptive Basis Selection Strategy (ABSS). Different from traditional greedy atom decomposition methods, a two-phase strategy is conducted to generate the basis separately via different strategies according to their different nature. At first, we globally prune the basis space to eliminate the unimportant basis as much as possible and locate the probable subspace containing the most informative basis. Then, another group of basis spaces are learned to improve the recovery accuracy based on local information. In this way, our proposed algorithm breaks through the accuracy bottleneck of traditional atom-decomposition-based matrix completion methods; meanwhile, it reserves the innate efficiency advantages over SVD-based matrix completion methods. We empirically evaluate the proposed algorithm ABSS on real visual image data and large-scale recommendation datasets. Results have shown that ABSS has much better reconstruction accuracy with comparable cost to atom-decomposition-based methods. At the same time, it outperforms the state-of-the-art SVD-based matrix completion algorithms by similar or better reconstruction accuracy with enormous advantages on efficiency.
Coliform mastitis in sows: a review.

Coliform mastitis (CM) represents an economically very important disease complex in sows that also affects the health, welfare, and performance of the piglets. Most research has concentrated on the husbandry-influenced occurrence of CM. The pathogenesis of CM suggests a prominent role for Escherichia coli and its endotoxins, although other Enterobacteriaceae species have been isolated from affected animals. Most studies on CM were conducted between 1970 and 1990. It is time for a closer look at this disease, particularly with respect to the economic damage it causes and the lack of recent literature. Treatment and use of body temperature as a single indicator for diagnosis of CM must be regarded critically. A combination of appropriate criteria should be applied to achieve a proper diagnosis and to minimize use of antibiotics. Additional approaches, for instance, incorporating knowledge concerning virulence factors of E coli, are promising tools for future prevention.
Lung cancer detection by using artificial neural network and fuzzy clustering methods

The early detection of the lung cancer is a challenging problem, due to the structure of the cancer cells. This paper presents two segmentation methods, Hopfield Neural Network (HNN) and a Fuzzy C-Mean (FCM) clustering algorithm, for segmenting sputum color images to detect the lung cancer in its early stages. The manual analysis of the sputum samples is time consuming, inaccurate and requires intensive trained person to avoid diagnostic errors. The segmentation results will be used as a base for a Computer Aided Diagnosis (CAD) system for early detection of lung cancer which will improves the chances of survival for the patient. The two methods are designed to classify the image of N pixels among M classes. In this study, we used 1000 sputum color images to test both methods, and HNN has shown a better classification result than FCM, the HNN succeeded in extracting the nuclei and cytoplasm regions.
Automatic feature definition and selection in fault diagnosis of oil rig motor pumps

We present a collection of pattern recognition techniques applied to fault detection and diagnosis of motor pumps. Vibrational patterns are the basis for describing the condition of the process. We rely on the data-driven approach to the learning of the fault classes, i.e. supervised learning in pattern recognition. Our work is motivated by the diversity of the studied defects, the availability of real data from operational oil rigs, and the use of statistical pattern recognition techniques usually not explored sufficiently in similar works. We show the results of automatic methods to define, select and combine features that describe the process and to classify the faults on the provided examples. The support vector machine is chosen as the classification architecture.
Higher-order incremental delta-sigma analog-to-digital converters

János Márkus „Higher-order Incremental Delta-Sigma Analog-to-Digital Converters” PhD thesis Analog-to-digital conversion, which takes continuous-time, continuous amplitude signals (voltage, temperature, sound, etc.) and converts them into a series of numbers to be used for digital signal processing, is becoming the key element of the scholarly and industrial applications of measurement and data acquisition, and A/D converters are surrounding (though invisible in most cases) our everyday life. In instrumentation and measurement, there is a growing demand for A/D converters with low or medium bandwidth, but with high absolute accuracy (e.g., sensors, dcmeasurement applications). High linearity and small offset are also among the requirements, as well as small power-consumption and low sensitivity to environmental noise (such as the periodic noise coupled from the mains or digital switching noise). One solution to the problem is the incremental (or charge-balancing) ΔΣ converter, which is basically a first-order ΔΣ A/D converter, operated in transient mode. The converter represents a hybrid between the classical dual-slope converter and the ΔΣ one. This dissertation extends the operation of the incremental converter to higher-order ΔΣ loops. It discusses the basic operation of such a converter, the theoretically achievable resolution, filter design methods for the digital filter following the ΔΣ modulator, and the structure’s sensitivity to analog circuit elements imperfections. The introduced general architecture is flexible, thus it is capable to optimize the trade-off between circuit complexity and conversion accuracy. Design examples and optimization techniques are proposed to help designers selecting the best configuration for a given application. The thesis also compares the results with those found in the literature. The theoretical results are verified by simulations and also by measurements made on an integrated circuit.
Emd-based filtering using the Hausdorff distance

This paper introduces a new signal-filtering method, which combines the empirical mode decomposition (EMD) and the Hausdorff distance (ℌD). A noisy signal is adaptively broken down into oscillatory components called intrinsic mode functions (IMFs) by EMD followed by an estimation of the probability density function (pdf) of each extracted mode. The filtered signal is obtained by performing a partial reconstruction using a selected set of empirical modes (termed relevant modes). The article focuses on an intuitive geometrical approach to identify the relevant modes based on the ℌD between the pdf of the noisy signal and that of each mode. Obtained results of the new filtering are compared to those of the EMD-CMSE (Consecutive Minimum Square Error) [1], the White Noise characteristics approach EMD-WN [2] and the Selection Criterion approach EMD-SC [3]. Dn different signals with varying signal-to-noise ratio values are used to validate the obtained results. The study is limited to signals corrupted by additive white Gaussian noise.