Miracle-dz/UniFAR-FTUs huggingface dataset:
Recovery of urinary nanovesicles from ultracentrifugation supernatants.

BACKGROUND Urinary vesicles represent a newly established source of biological material, widely considered to faithfully represent pathological events in the kidneys and the urogenital epithelium. The majority of currently applied isolation protocols involve cumbersome centrifugation steps to enrich vesicles from urine. To date, the efficiency of these approaches has not been investigated with respect to performing quantitative and qualitative analyses of vesicle populations in the pellet and supernatant (SN) fractions. METHODS After the series of differential centrifugations, the final SN was reduced to one-twentieth of the original volume by ammonium sulphate precipitation, with the precipitate pellet subjected to another round of differential centrifugations. Electron microscopy, dynamic light scattering and western blot analysis were used to characterize the vesicles present in individual fractions of interest. RESULTS Pellets obtained after the second set of centrifugations at 200 000 g revealed the presence of vesicles which share a common marker profile, but with distinct differences from those seen in the initial 200 000 g pellet used as the reference. This suggests an enrichment of previously uncharacterized urinary vesicles still in solution after the initial centrifugation steps. Analysis of protein yields recovered post-ultracentrifugation revealed an additional 40% of vesicles retained from the SN. Moreover, these structures showed a formidable resistance to harsh treatments (e.g. 95% ammonium sulphate saturation, hypotonic dialysis, 0.3 M sodium hydroxide). CONCLUSIONS Methods which employ differential centrifugations of native urine are remarkably ineffective and may lose a substantial population of biologically important vesicle species.
The joint cascade of energy and helicity in three-dimensional turbulence

Three-dimensional (3D) turbulence has both energy and helicity as inviscid constants of motion. In contrast to two-dimensional (2D) turbulence, where a second inviscid invariant—the enstrophy—blocks the energy cascade to small scales, in 3D there is a joint cascade of both energy and helicity simultaneously to small scales. It has long been recognized that the crucial difference between 2D and 3D is that enstrophy is a nonnegative quantity whereas the helicity can have either sign. The basic cancellation mechanism which permits a joint cascade of energy and helicity is illuminated by means of the helical decomposition of the velocity into positively and negatively polarized waves. This decomposition is employed in the present study both theoretically and also in a numerical simulation of homogeneous and isotropic 3D turbulence. It is shown that the transfer of energy to small scales produces a tremendous growth of helicity separately in the + and − helical modes at high wave numbers, diverging in the limi.
Indexing musical pieces using their major repetition

With the growing presence of large collections of musical content, methods for facilitating efficient browsing and fast comparisons of audio pieces become more and more useful. Notably, methods that isolate relevant parts in audio pieces give an insight of the musical content and can be used to improve similarity evaluation systems. In this context, we propose an indexing method that allows retrieving in audio signals particular parts, namely a major repetition. We use harmonic representations together with string matching techniques to strictly define and isolate such segments. Experiments on state-of-the-art structural datasets show a strong correlation between the retrieved parts and the perceived structure of pieces.
A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks

Reinforcement learning has been applied to many interesting problems such as the famous TD-gammon and the inverted helicopter flight. However, little effort has been put into developing methods to learn policies for complex persistent tasks and tasks that are time-sensitive. In this paper, we take a step towards solving this problem by using signal temporal logic (STL) as task specification, and taking advantage of the temporal abstraction feature that the options framework provide. We show via simulation that a relatively easy to implement algorithm that combines STL and options can learn a satisfactory policy with a small number of training cases
Capacity of the mobile MIMO channel for a small wireless handset and user influence

We obtained results on the capacity of a mobile MIMO channel and on the influence of the user. Measurements were done in a configuration with three distributed transmitters equipped with single antennas and with a small wireless handset with four antennas, creating a 4Rx /spl times/ 3Tx configuration. We express the quality of the radio channel in terms of the effective degree of freedom (EDOF). From our measurement it follows that fitting a small handset with multiple antennas still makes sense in an outdoor-to-indoor mobile MIMO scenario. The channel capacity can be excellent, comparable with fully uncorrelated Rayleigh fading branch signals. However, on average the user should be satisfied with less favourable conditions amounting to a loss in EDOF of 0.16 at 50% complementary cumulative distribution function (CCDF) level. Systems operating without power control will suffer from an extra degradation in EDOF of 0.10 with an increased spread in channel quality both at the high and low end of the EDOF curve.
Area and power efficient pattern prediction architecture for filter cache access prediction in the instruction memory hierarchy

The pattern prediction algorithm has been shown in past research to provide substantial improvements in energy consumption, without sacrificing performance when used to predict access to the filter cache in the instruction memory hierarchy. Since, the pattern predictor and the filter cache are an overhead to the existing cache architecture, it is imperative that they should together not lower the energy efficiency of the existing architecture. Thus, the extra energy consumed by the predictor should be minimal as compared to the overall energy reduction achieved by the introduction of the filter cache in the instruction memory hierarchy. In this paper, we present the hardware architecture of a low-power, single-cycle pattern predictor, which can be used in conjunction with the filter cache. The predictor has been functionally validated against results from simple scalar simulations on the MediaBench benchmark suite. The predictor is capable of performing single cycle predictions for processor clocks of over 0.5 GHz (in 0.35/spl mu/ process technology) and adds an area overhead of a mere 1500 to 2700 gates to the silicon area depending on the configuration chosen. The predictor is area and power efficient, and does not add any significant overheads to the processor's power and area budget. Thus providing an elegant and attractive solution for inclusion in modern embedded processors.
Sociolinguistics and programming

This paper focuses on the use of machine learning techniques for the analysis of computer programs in order to acquire information about an author's gender. There are few existing studies that address the relationship between linguistics and programming; however, in many areas where language is analyzed it is possible to mine important information about the users of that language associated with set of attribute or coding style. In this work we use open source implementations of machine learning algorithms, specifically, nearest neighbor (K*), decision tree (J48), and Bayes classifier (Naïve Bayes). These algorithms were applied to C++ programs which were associated with sociolinguistic information about the program authors. Our goal was to classify the programs according to the gender of the author. As indicated by our initial results we have been able to achieve precision of 72.3%, recall of 72%, and f-measure of 71.9% which demonstrates that we can predict the gender of the authors of C++ programs.
Proceedings the Annual Meeting of the Cognitive Science Society A Implicit Acquisition of Recursive Sequence Structures

A dominant hypothesis in empirical research on the evolution of language is the following: the fundamental difference between animal and human communication systems is captured by the distinction between regular and more complex non-regular grammars. Studies reporting successful artificial grammar learning of nested recursive structures and imaging studies of the same have methodological shortcomings since they typically allow explicit problem solving strategies and this has been shown to account for the learning effect in subsequent behavioral studies. The present study overcomes these shortcomings by using subtle violations of agreement structure in a preference classification task. In contrast to the studies conducted so far, we use an implicit learning paradigm, allowing the time needed for both abstraction processes and consolidation to take place. Our results demonstrate robust implicit learning of recursively embedded structures (context-free grammar) and recursive structures with cross-dependencies (context-sensitive grammar) in an artificial grammar learning task spanning 9 days.
Procedural Analysis of Choice Rules with Applications to Bounded Rationality

I study how limited abilities to process information affect choice behavior. I model the decision making process by an automaton, and measure the complexity of a specific choice rule by the minimal number of categories an automaton implementing the rule uses to process information. I establish that any choice rule that is less complicated than utility maximization displays framing effects. I then prove that the unique choice rule that results from an optimal tradeoff between maximizing utility and minimizing complexity is a history-dependent satisficing procedure that displays primacy and recency effects, default tendency and choice overload. ∗I am indebted to Bob Wilson for his devoted guidance, constant support, and most valuable suggestions. I am grateful to Ariel Rubinstein and Jeremy Bulow for the encouragement, productive discussions, and important comments. I thank Gil Kalai, Ron Siegel, and Andy Skrzypacz for most insightful feedback in various stages of this project. I also thank Matt Jackson, Jon Levin, Michael Ostrovsky, Roy Radner, Ilya Segal, and seminar participants at UC Berkeley, Brown, Caltech, Harvard, Hebrew University, Iowa, LSE, MIT, Northwestern, NYU, Stanford, Tel-Aviv, UCL, Washington University in St. Louis, and Yale for helpful comments. This research is supported in part by the Leonard W. and Shirley R. Ely Fellowship of the Stanford Institute for Economic Policy Research.
Job Insecurity and Children's Emancipation: The Italian Puzzle ∗

Why are Italian grown-up children so reluctant to leave the parental home? In this paper we present a theoretical model predicting that this behavior can be induced by high job insecurity of children and low job insecurity of their parents. Both aggregate data for EU countries and microeconometric results, using data from the Italian Survey of Household Income and Wealth for the 1990s, are broadly supportive of this hypothesis.
Autonomous Navigation and Obstacle Avoidance for a Wheeled Mobile Robots: A Hybrid Approach

this paper, an autonomous navigation and obstacle avoidance strategy is proposed for an omnidirectional mobile robot. The robot plans a path, starting from an initial point going to a target point. A hybrid approach has been developed where a global approach has been applied to the motion along the desired path (DP) using 2 nd order polynomial planning, while a local reactive approach is used to avoid collisions with static and/or dynamic obstacles based on the "sensing vector" and the "gap vector" concepts. The "sensing vector" is a binary vector which provides information about obstacles detection, while the "gap vector" provides information about a possible nearest gap the robot can pass through it. Keywordsmobile robot; autonomous navigation; path planning ; obstacle avoidance; hybrid approach.
Global 3D Modeling and its Evaluation for Large-Scale Highway Tunnel using Laser Range Sensor

Precise and accurate 3D model of a road structure is basic information that can be utilized for various purposes, such as safety measures, driving simulation, and reference data for autonomous driving. Generally, the 3D model of a tunnel has been constructed using gyro sensor, however, error accumulation becomes a considerable problem in the case of long tunnels. The authors tried a method to obtain the most optimal structure of a tunnel by geometric processing: (1) Acquire a set of partial structures by static scanning, and align them by 3D matching using edge feature (2) Fix the absolute position of the data at both ends of the tunnel by global positioning system (GPS) and align the rest data again. The authors modeled the Awagatake (Kanaya) Tunnel in Shin-Tomei Expressway, whose length is 4.6 km by this method. The error in lateral direction was up to 0.1% relative to the tunnel length.
Study on network failure prediction based on alarm logs

To avoid unpredictable losses because of network failure, the reliability of the network needs to be evaluated in some application scenarios. This paper start the network failure prediction research upon 14 months' network alarm logs we collected. The logs are of one Metropolitan area network. The research method is shown as below: firstly, construct features to represent network characteristics by the means of the feature construction method which is based on two levels time windows; secondly, select optimal parameter combination to create the feature files through multiple experiments; thirdly, design and build adaptive failure prediction model according to classification learning methods. Numbers of experiments show that accuracy of predicting whether the network failure takes place in 6 hours is up to 70%, is better than the prediction result of Weibull distribution model obviously; the results of classification prediction for network equipment failure are slightly better than the prediction method on the basis of Weibull distribution. Preliminary research results show that most network failures can be predicted through analyzing previous network running logs and the method proposed in this paper is verified to be with good prediction effect. This method can detect failures in practical application on early stage and reduce unnecessary economic losses.
The Design Space of Implicit Hierarchy Visualization: A Survey

Apart from explicit node-link representations, implicit visualizations and especially the Treemap as their frontrunner have acquired a solid position among the available techniques to visualize hierarchies. Their advantage is a highly space-efficient graphical representation that does not require explicit drawing of edges. In this paper, we survey the design space for this class of visualization techniques. We establish the design space along the four axes of dimensionality, edge representation, node representation, and layout by examining existing implicit hierarchy visualization techniques. The survey is completed by casting some light into regions of the design space that have not yet been explored. Our design space is not a mere theoretical construct, but a practically usable tool for rapid visualization development. To that end, we discuss a software implementation of the introduced design space.
Learning-Induced Improvement in Encoding and Decoding of Specific Movement Directions by Neurons in the Primary Motor Cortex

Many recent studies describe learning-related changes in sensory and motor areas, but few have directly probed for improvement in neuronal coding after learning. We used information theory to analyze single-cell activity from the primary motor cortex of monkeys, before and after learning a local rotational visuomotor task. We show that after learning, neurons in the primary motor cortex conveyed more information about the direction of movement and did so with relation to their directional sensitivity. Similar to recent findings in sensory systems, this specific improvement in encoding is correlated with an increase in the slope of the neurons' tuning curve. We further demonstrate that the improved information after learning enables a more accurate reconstruction of movement direction from neuronal populations. Our results suggest that similar mechanisms govern learning in sensory and motor areas and provide further evidence for a tight relationship between the locality of learning and the properties of neurons; namely, cells only show plasticity if their preferred direction is near the training one. The results also suggest that simple learning tasks can enhance the performance of brain–machine interfaces.
Decoder-based Discriminative Training of Phrase Segmentation for Statistical Machine Translation

In this paper, we propose a new method of training phrase segmentation model for phrasebased statistical machine translation(SMT). We define a good segmentation as the segmentation producing a good translation. According to this definition, we propose a method that can discriminate between a good segmentation and a bad segmentation based on the translation quality. The proposed approach constructs the phrase labeled data by using the SMT decoder, so that the phrase segmentations supporting good translations can be acquired. Furthermore, our iterative training algorithm of the segmentation model can gradually improve the performance of the SMT decoder. Experimental results show that the proposed method is effective in improving the translation quality of the phrase-based SMT system. TITLE AND ABSTRACT IN ANOTHER LANGUAGE (KOREAN) 통계 기계번역을 위한 디코더 기반 구 분할 차별 학습 방 법 본 논문은 구 기반 통계 기계번역을 위한 구 분할 모델의 새로운 학습 방법을 제안한 다. 우리는좋은번역(good translation)을생성하는구분할을좋은분할(good segmentation) 이라고 정의한다. 우리는 이 정의에 따라 번역 품질에 기반하여 좋은 분할과 좋지 않은 분할을 차별할 수 있는 방법을 제안한다. 제안하는 접근방법은 통계 기계번역(SMT) 디코더을 이용하여 구 부착 데이터를 구축함으로써, 좋은 번역을 만드는 구 분할을 얻을 수 있다. 또한 SMT 디코더의 성능을 점진적으로 개선시킬 수 있는 반복적인 학습 알고리즘을 제안한다. 실험을 통해, 제안 방법이 구 기반 SMT 시스템의 번역 품질 향상에 효과적이었음을보인다.
A New Data Stream Mining Algorithm for Interestingness-Rich Association Rules

Frequent itemset mining and association rule generation is a challenging task in data stream. Even though, various algorithms have been proposed to solve the issue, it has been found out that only frequency does not decides the significance interestingness of the mined itemset and hence the association rules. This accelerates the algorithms to mine the association rules based on utility i.e. proficiency of the mined rules. However, fewer algorithms exist in the literature to deal with the utility as most of them deals with reducing the complexity in frequent itemset/association rules mining algorithm. Also, those few algorithms consider only the overall utility of the association rules and not the consistency of the rules throughout a defined number of periods. To solve this issue, in this paper, an enhanced association rule mining algorithm is proposed. The algorithm introduces new weightage validation in the conventional association rule mining algorithms to validate the utility and its consistency in the mined association rules. The utility is validated by the integrated calculation of the cost/price efficiency of the itemsets and its frequency. The consistency validation is performed at every defined number of windows using the probability distribution function, assuming that the weights are normally distributed. Hence, validated and the obtained rules are frequent and utility efficient and their interestingness are distributed throughout the entire time period. The algorithm is implemented and the resultant rules are compared against the rules that can be obtained from conventional mining algorithms.
Computations in class groups of imaginary quadratic number fields

The most crucial question when using a cryptographic scheme is how to select the cryptographic parameters such that the scheme is "secure" and reasonably efficient at the same time. In this article that question will be considered for cryptographic schemes based on class groups of imaginary quadratic number fields. Specifically, an algorithm is investigated that is very similar to the (p - 1) factoring algorithm. This algorithm is already known to be asymptotically inferior to the IQ-MPQS algorithm. However, based on rough running time estimates of both algorithms, there has been the suspicion that for parameters within range of cryptographic relevance the algorithm was competing with the IQ-MPQS. In this article it is clearly shown that this is not even remotely the case
On caching and prefetching of virtual objects in distributed virtual environments

ld~~ces in networkg tetiology and the mtabkhtnent of the Morrnation Superhighway have rendered the virtnd Xbrary a concrete possibfi~. iTeze currently investigating user &\Terience in _ through a large virtual environment in the contti% of bternet. This provid~ users with the abiity to view t+ous virtual objects from tirent & t anca and angles, using common web browsers. To dtiver a good petiorrnance for such applications, we need to addr~s Several issues in Merent resemch discipbes. F&t. , we must be able to modd virtual objects tiectivdy. The recently devdoped techniques for mdti-~olution object modting in computer graphics are of great Aue here, since they are capable of sintp~g the object mod& and therefore reducing the time to render them. Secon& tith the Eted bandwidth constraint of the btemet, we need to reduce the response time by reducing the amount of data requwted over the network One dtemative is to cache object mod& of high -w. Prefetching object mod~ by predicting those which ~e &dy to be used in the near fnture and dotioading them in advance wiU lead to a Mar improvement. Third, the bt.ernet often tiers from disconnection. A caching mechanism hat dews objects to be cached n? th at least the-mminimum resolution m be we~ to provide at least a coarse x<ew of the objects to a disconnected >tiewerfor improved ~hd perception. k this paper, m propose a multi-T~*oZutionaching m~hrmtim wd invG tigate its tiectiveness in supporthg virtud w~u~ apFEcations in the ktemet. environment. The caching mechanism is further complemented with severs object prefetching m echanLms for predicting future acc~ed objects. The performance of our proposed me~ and their feasibfities are qnsmtXed via s“mtdat.ed ~Terirnents.
Power and Performance of Native and Java Benchmarks on 130nm to 32nm Process Technologies

Over the past decade, chip fabrication technology shrank from 130nm to 32nm. This reduction was generally considered to provide performance improvements together with chip power reductions. This paper examines how well process technology and microarchitecture delivered on this assumption. This paper evaluates power and performance of native and Java workloads across a selection of IA32 processors from five technology generations (130nm, 90nm, 65nm, 45nm, and 32nm). We use a Hall effect sensor to accurately measure chip power. This paper reports a range findings in three areas. 1) Methodology: TDP is unsurprisingly a poor predictor of application power consumption for a particular processor, but worse, TDP is a poor predictor of relative power consumption between processors. 2) Power-performance trends: Processors appear to have already hit the power wall at 45nm. 3) Native versus Java workloads and their relationship to processor technology: Single threaded Java workloads exploit multiple cores. These results indicate that Java workloads offer different opportunities and challenges compared to native workloads. Our findings challenge prevalent methodologies and offer new insight into how microarchitectures have traded power and performance as process technology shrank.