Miracle-dz/UniFAR-FTUs huggingface dataset:
Postmating transcriptional changes in reproductive tracts of con- and heterospecifically mated Drosophila mojavensis females

In internally fertilizing organisms, mating involves a series of highly coordinated molecular interactions between the sexes that occur within the female reproductive tract. In species where females mate multiply, traits involved in postcopulatory interactions are expected to evolve rapidly, potentially leading to postmating-prezygotic (PMPZ) reproductive isolation between diverging populations. Here, we investigate the postmating transcriptional response of the lower reproductive tract of Drosophila mojavensis females following copulation with either conspecific or heterospecific (Drosophila arizonae) males at three time points postmating. Relatively few genes (15 total) were differentially regulated in the female lower reproductive tract in response to conspecific mating. Heterospecifically mated females exhibited significant perturbations in the expression of the majority of these genes, and also down-regulated transcription of a number of others, including several involved in mitochondrial function. These striking regulatory differences indicate failed postcopulatory molecular interactions between the sexes consistent with the strong PMPZ isolation observed for this cross. We also report the transfer of male accessory-gland protein (Acp) transcripts from males to females during copulation, a finding with potentially broad implications for understanding postcopulatory molecular interactions between the sexes.
A Comparative Study of Diverse Knowledge Sources and Smoothing Techniques via Maximum Entropy for Polyphone Disambiguation in Mandarin TTS Systems

This paper comparatively evaluated various knowledge sources and smoothing algorithms for pronunciation disambiguation in Mandarin TTS (text-to-speech) systems under maximum entropy (maxent) framework. In particular, five kinds of knowledge sources, namely characters and their pronunciations, words, their pronunciations and part-of-speech. together with two smoothing algorithms, i.e. Gaussian prior and inequality were compared. In our experiments conducted on 107 key Chinese polyphones. we found that all the knowledge sources almost perform equally well given the same smoothing measure, but the character-based features compare favorably because they are language independent and can be obtained with the lowest computation cost. Compared with the widely-used Gaussian smoothing, the equality smoothing greatly reduces the number of active features and yields a slightly improved accuracy on each knowledge source. Our best result (96.36%) is achieved by using character-based features together with the inequality smoothing, significantly superior to 81.22% by selecting the most frequent pronunciations and 88.72% by dictionary look-up with the part-of-speech. We also compared the maxent classifier with the transform-based error-driven learning algorithm (E. Brill, 1995) using the same knowledge sources, the results show that the maxent classifier achieve better performance to solve the polyphone disambiguation.
Hash in a Flash: Hash Tables for Solid State Devices

In recent years, information retrieval algorithms have taken center stage for extracting important data in ever larger datasets. Advances in hardware technology have lead to the increasingly wide spread use of flash storage devices. Such devices have clear benefits over traditional hard drives in terms of latency of access, bandwidth and random access capabilities particularly when reading data. There are however some interesting trade-offs to consider when leveraging the advanced features of such devices. On a relative scale writing to such devices can be expensive. This is because typical flash devices (NAND technology) are updated in blocks. A minor update to a given block requires the entire block to be erased, followed by a re-writing of the block. On the other hand, sequential writes can be two orders of magnitude faster than random writes. In addition, random writes are degrading to the life of the flash drive, since each block can support only a limited number of erasures. TF-IDF can be implemented using a counting hash table. In general, hash tables are a particularly challenging case for the flash drive because this data structure is inherently dependent upon the randomness of the hash function, as opposed to the spatial locality of the data. This makes it difficult to avoid the random writes incurred during the construction of the counting hash table for TF-IDF. In this paper, we will study the design landscape for the development of a hash table for flash storage devices. We demonstrate how to effectively design a hash table with two related hash functions, one of which exhibits a data placement property with respect to the other. Specifically, we focus on three designs based on this general philosophy and evaluate the trade-offs among them along the axes of query performance, insert and update times and I/O time through an implementation of the TF-IDF algorithm.
Efficient and Robust Compressed Sensing Using Optimized Expander Graphs

Expander graphs have been recently proposed to construct efficient compressed sensing algorithms. In particular, it has been shown that any <i>n</i>-dimensional vector that is <i>k</i>-sparse can be fully recovered using <i>O</i>(<i>k</i>log<i>n</i>) measurements and only <i>O</i>(<i>k</i>log<i>n</i>) simple recovery iterations. In this paper, we improve upon this result by considering expander graphs with expansion coefficient beyond <sup>3</sup> <i>/</i> <sub>4</sub> and show that, with the same number of measurements, only <i>O</i>(<i>k</i>) recovery iterations are required, which is a significant improvement when <i>n</i> is large. In fact, full recovery can be accomplished by at most <i>2k</i> very simple iterations. The number of iterations can be reduced arbitrarily close to <i>k</i>, and the recovery algorithm can be implemented very efficiently using a simple priority queue with total recovery time <i>O</i>(<i>n</i>log(<sup>n</sup>/<sub>k</sub>))). We also show that by tolerating a small penalty on the number of measurements, and not on the number of recovery iterations, one can use the efficient construction of a family of expander graphs to come up with explicit measurement matrices for this method. We compare our result with other recently developed expander-graph-based methods and argue that it compares favorably both in terms of the number of required measurements and in terms of the time complexity and the simplicity of recovery. Finally, we will show how our analysis extends to give a robust algorithm that finds the position and sign of the <i>k</i> significant elements of an almost <i>k</i>-sparse signal and then, using very simple optimization techniques, finds a <i>k</i>-sparse signal which is close to the best <i>k</i>-term approximation of the original signal.
Evenly Spaced Streamlines for Surfaces: An Image‐Based Approach

We introduce a novel, automatic streamline seeding algorithm for vector fields defined on surfaces in 3D space. The algorithm generates evenly spaced streamlines fast, simply and efficiently for any general surface‐based vector field. It is general because it handles large, complex, unstructured, adaptive resolution grids with holes and discontinuities, does not require a parametrization, and can generate both sparse and dense representations of the flow. It is efficient because streamlines are only integrated for visible portions of the surface. It is simple because the image‐based approach removes the need to perform streamline tracing on a triangular mesh, a process which is complicated at best. And it is fast because it makes effective, balanced use of both the CPU and the GPU. The key to the algorithm's speed, simplicity and efficiency is its image‐based seeding strategy. We demonstrate our algorithm on complex, real‐world simulation data sets from computational fluid dynamics and compare it with object‐space streamline visualizations.
Study on a Real-Time Image Object Tracking System

A programmable real-time system based on DSP and FPGA for the image object tracking is proposed. The high-performance fixed-point DSP TMS320DM643 as kernel processor is suitable for processing video signal. This paper presents in detail the work flow of the solution, the function and principle of components, such as preprocessing and OSD FPGA, the external memory interface, the transport and control of digital video data stream, the module of multi-voltage power supply and monitoring. The experiments and analysis show that peak-value, poid, and correlation tracking algorithms using gray-scale of video sequence could be implemented on the hardware platform in real time.
Artificial Societies

This article introduces a general sociological readership to multiagent systems (MAS), a new computer simulation technology that has increasingly been used to describe and explain sociological phenomena. The author uses the term artificial societies to refer to social simulations using MAS and he describes MAS technology and contrasts it with other social simulation technologies. The author argues that MAS have attained a level of maturity where they can be useful tools for sociologists, and he shows how MAS provide new perspectives on contemporary discussions of the micro-macro link in sociological theory by focusing on three aspects of the micro-macro link: micro-to-macro emergence, macro-to-micro social causation, and the dialectic between emergence and social causation.
Greedy List Intersection

A common technique for processing conjunctive queries is to first match each predicate separately using an index lookup, and then compute the intersection of the resulting row- id lists, via an AND-tree. The performance of this technique depends crucially on the order of lists in this tree: it is important to compute early the intersections that will produce small results. But this optimization is hard to do when the data or predicates have correlation. We present a new algorithm for ordering the lists in an AND- tree by sampling the intermediate intersection sizes. We prove that our algorithm is near-optimal and validate its effectiveness experimentally on datasets with a variety of distributions.
A blind adaptive decorrelating detector for CDMA systems

The decorrelating detector is known to eliminate multiaccess interference given that the signature sequences of the users are linearly independent, at the cost of enhancing the Gaussian receiver noise. In this paper, we present and study the convergence of a blind adaptive decorrelating detector which is based on the observation of the available statistics. The algorithm is iterative and distributed. Only two parameters are needed for the construction of the receiver filter of a user: the user's signature sequence and the variance of the white Gaussian receiver noise. The computational complexity of the proposed algorithm per iteration is linear in the number of users.
Effects of Age, Sex, and Neuropsychological Performance on Financial Decision-Making

The capacity to make sound financial decisions across the lifespan is critical for interpersonal, occupational, and psychological health and success. In the present study, we explored how healthy younger and older adults make a series of increasingly complex financial decisions. One-hundred sixteen healthy older adults, aged 56–90 years, and 102 college undergraduates, completed the Financial Decision-Making Questionnaire, which requires selecting and justifying financial choices across four hypothetical scenarios and answering questions pertaining to financial knowledge. Results indicated that Older participants significantly outperformed Younger participants on a multiple-choice test of acquired financial knowledge. However, after controlling for such pre-existing knowledge, several age effects were observed. For example, Older participants were more likely to make immediate investment decisions, whereas Younger participants exhibited a preference for delaying decision-making pending additional information. Older participants also rated themselves as more concerned with avoiding monetary loss (i.e., a prevention orientation), whereas Younger participants reported greater interest in financial gain (i.e., a promotion orientation). In terms of sex differences, Older Males were more likely to pay credit card bills and utilize savings accounts than were Older Females. Multiple positive correlations were observed between Older participants’ financial decision-making ability and performance on neuropsychological measures of non-verbal intellect and executive functioning. Lastly, the ability to justify one’s financial decisions declined with age, among the Older participants. Several of the aforementioned results parallel findings from the medical decision-making literature, suggesting that older adults make decisions in a manner that conserves diminishing cognitive resources.
Incremental Optimization of Large Robot-Acquired Maps

Many robotics applications require the robot to build a map of its environment. We consider the problem of Simultaneous Localization and Mapping (SLAM), i.e., building a map of an environment while exploring it for the first time. SLAM algorithms approach this by identifying features in the environment (e.g., the corner of a desk) and determining the relative positions of features. A robot’s sensors are imperfect, so the relative position of one feature to another is almost always considered probabilistically– typically with a Gaussian distribution.
Convolutional Neural Network using a threshold predictor for multi-label speech act classification

Regarding the spoken language understanding (SLU) pilot task of the Dialog State Tracking Challenge 5 (DSTC5), it is required to classify label sets of speech acts on human-to-human dialogues. In this paper, we propose a multi-label classification model with the assistance of algorithm adaptation method. To be specific, a Convolutional Neural Network (CNN) model on top of pre-trained word vectors is adapted for the multi-label classification task by utilizing a threshold learning mechanism. In order to evaluate the performance of our proposed model, comparative experiments on the DSTC5 dialogue datasets are conducted. Experimental results show that the proposed model outperforms most of the submitted model in the DSTC5 in terms of F1-score. Without any manually designed features, our model has advantage of handling the multi-label SLU task, using only publicly available pre-trained word vectors.
Agents for wireless sensor network power management

The primary function of wireless sensor networks is data acquisition or monitoring of some medium, such as temperature. In many instances these networks are deployed throughout inaccessible or hazardous regions meaning frequent maintenance such as battery replacement is undesirable and in some cases impossible. Intelligent power management for these devices is critical in maximizing the networks life span and ultimately will dictate the success of such deployments. This longevity must, however, be achieved while maintaining the integrity of the sensory data harvested by the network. Due to the inherent distributed nature of wireless sensor networks, intelligent software agents lend themselves to performing this power management in such a distributed domain. In this paper we examine some of the potential decisions an agent may face regarding intelligent power management and we look at how the stronger notion of agency could be employed to allow a richer deliberation regarding potential decisions. Allowing more adaptive control of WSNs in light of their computationally challenged nature.
Low power current mode 8 1.5-bit stages pipelined a/d converter

This paper presents a novel prototype low power current mode 8 1.5-bit stages pipelined a/d converter. The a/d converter structure is composed of current mode building blocks and final comparator block which converts the analog current signal into digital voltage signal. All building blocks have been designed in CMOS AMS 0. 35µm technology, then simulated to verify proposed concept. The performances of the converter are compared to performances of known voltage mode switched capacitance converter structures. Low power consumption and small chip area are advantages of the proposed converter.
PETAL : Preset Encoding Table Information Leakage

SPDY is an application-layer protocol which multiplexes multiple HTTP requests and compresses HTTP headers over a single TCP connection protected by SSL/TLS encryption. Web applications are ubiquitous, and HTTP headers carry HTTP cookies which often contain sensitive information which can result in loss of privacy if leaked. We perform a security analysis on the proposed compression scheme for the next revision of the SPDY protocol, particularly with respect to the previously disclosed CRIME attack which uses compression-based information leaks. We have identified a new information leakage in the compression scheme of the proposed and previous versions of the SPDY protocol, which we call PETAL1, which exploits the use of a fixed Huffman encoding table and the lack of byte-alignment of encoded characters, and we have identified a way to recover cookies using this information leakage by exploiting the way that multiple HTTP cookies with the same name but different Path attributes are handled by current web browsers. We perform a detailed analysis of the impact of this information leakage, and find that after considering practical issues such as the byte-padded nature of network communications, our hypothesized attack only leaks less than 2-bits of information for 30-character uppercase alphanumeric strings, and does not allow a network attacker to recover meaningful amounts of information despite our discovered information leakage. Acknowledgements: We would like to thank Collin Jackson and Lin-Shung Huang for helpful discussions and suggestions for this paper. We would also like to thank Roberto Peon and Adam Langley for feedback and helpful discussions on this paper.
Speaker recognition using residual signal of linear and nonlinear prediction models

This Paper discusses the usefullness of the residual signal for speaker recognition. It is shown that the combination of both a measure defined over LPCC coefficients and a measure deffined over the energy of the residual signal gives rise to an improvement over the classical method which considers only the LPCC coefficients. If the residual signal is obtained from a linear prediction analisys, the improvement is 2.63% (error rate drops from 6.31% to 3.68%) and if it is computed through a nonlinear predictive neural nets based model, the improvement is 3.68%.
Mask-based non-maximal suppression with iterative pruning for low-complexity corner detection

Adaptive and low-complexity corner detection is essential for the realization of modern real-time video processing applications on low-resource embedded platforms. In our earlier work, we have proposed an efficient iterative pruning strategy for widely used Shi-Tomasi and Harris corner detectors. In this paper we propose a mask-based strategy for efficient nonmaximal suppression. In comparison to the conventional nonmaximal suppression, applying the proposed strategy results in higher efficiency in computations for the iterative pruning based Shi-Tomasi/Harris corner detectors. On the embedded NiOS-2 platform, we achieve a speedup in execution time of 14-42% for Shi-Tomasi and 11-47% for Harris corner detectors.
Receive combining vs. multistream multiplexing in multiuser MIMO systems

In single-user transmission, the receive antennas should preferably be used to enable multiplexing. The situation is different under multiuser transmission, where only the number of transmit antennas limits the multiplexing gain. The system therefore has the choice between sending one stream per scheduled user (i.e., combining receive antennas for diversity) or selecting a smaller number of users and multiplex multiple streams to each of them. This tradeoff is investigated herein, based on zero-forcing (with receive antenna combining) and block-diagonalization pre-coding which represents the two extremes. Based on asymptotic analysis and numerical examples, the unexpected conclusion is that each user only should receive one stream and use its antennas to achieve a receive combining gain. This is explained by zero-forcing having a stronger resilience towards spatial correlation and larger benefit from multiuser diversity. This fundamental result has positive implications for the design of multiuser systems as it reduces the hardware constraints at the user devices.
Nash equilibria with partial monitoring; Computation and Lemke-Howson algorithm

In two player bi-matrix games with partial monitoring, actions played are not observed, only some messages are received. Those games satisfy a crucial property of usual bi-matrix games: there are only a finite number of required (mixed) best replies. This is very helpful while investigating sets of Nash equilibria: for instance, in some cases, it allows to relate it to the set of equilibria of some auxiliary game with full monitoring. In the general case, the Lemke-Howson algorithm is extended and, under some genericity assumption, its output are Nash equilibria of the original game. As a by product, we obtain an oddness property on their number.
Hoc-based Routing Algorithm for Wireless Body Sensor Network

A well-performing medical wireless network can greatly improve the efficiency of medical units and save manpower and resources. And the performance of a wireless network depends on routing algorithm. An optimal routing algorithm means less transmission delay, high packet arrival rate and proper network congestion control. As the soul in Ad hoc network, it can greatly restrict network size and node data transmission. This study focused on research of several typical routing algorithms in wireless ad hoc networks. Then it proposed an ad hoc-based routing algorithm for wireless body sensor networks according to the characteristics of medical networks HAODV (Hospital Ad-hoc on-demand Distance Vector) on its basis. Furthermore, NS2 was used for simulation, and simulation data of different network scenes was analyzed for the comparison of two routing algorithms. Based on the simulation results, the feasibility of the algorithm was verified to guide the construction of medical Ad hoc networks. Copyright © 2014 IFSA Publishing, S. L.
Using Aspects with Object-Oriented Frameworks

Compositions containing A. FLUOROPOLYMER, B. COLLOIDAL SILICA, C. THE SALT OF A POLYAMIDE ACID, D. A COALESCING AGENT, E. MICA PARTICLES, MICA PARTICLES COATED WITH PIGMENT, OR METAL FLAKE, AND F. A LIQUID CARRIER ARE USEFUL FOR PRIMING METAL, ESPECIALLY COOKWARE, BEFORE TOPCOATING IT WITH A FLUOROPOLYMER ENAMEL.
A Matlab Implementation of Differential GPS for Low-cost GPS Receivers

A number of public codes exist for GPS (global positioning system) positioning and baseline determination in off-line mode. However, no software code exists for DGPS (differential GPS) exploiting correction factors at base stations, without relying on double difference information. In order to accomplish it, a methodology is introduced in MATLAB environment for DGPS using C/A (course acquisition) pseudoranges on single frequency L1 only to make it feasible for low-cost GPS receivers. The base station is at accurately surveyed reference point. Pseudoranges and geometric ranges are compared at base station to compute the correction factors. These correction factors are then handed over to rover for all valid satellites observed during an epoch. The rover takes it into account for its own true position determination for corresponding epoch. In order to validate the proposed algorithm, the rover is also placed at a pre-determined location. The proposed code is an appropriate and simple to use tool for post-processing of GPS raw data for accurate position determination of a rover e.g. Unmanned Aerial Vehicle during post-mission analysis.