Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing DCCP: congestion control without reliability

Fast-growing Internet applications like streaming media and telephony prefer timeliness to reliability, making TCP a poor fit. Unfortunately, UDP, the natural alternative, lacks congestion control. High-bandwidth UDP applications must implement congestion control themselves-a difficult task-or risk rendering congested networks unusable. We set out to ease the safe deployment of these applications by designing a congestion-controlled unreliable transport protocol. The outcome, the Datagram Congestion Control Protocol or DCCP, adds to a UDP-like foundation the minimum mechanisms necessary to support congestion control. We thought those mechanisms would resemble TCP's, but without reliability and, especially, cumulative acknowledgements, we had to reconsider almost every aspect of TCP's design. The resulting protocol sheds light on how congestion control interacts with unreliable transport, how modern network constraints impact protocol design, and how TCP's reliable bytestream semantics intertwine with its other mechanisms, including congestion control.
Analog compressed sensing for RF propagation channel sounding

Massively broadband® RF channel sounding is severely constrained by the sampling rates required for analog to digital conversion. Analog compressed sensing (CS) techniques based on Xampling have demonstrated the ability to lower sampling rates far below the Nyquist rate. Here, we show attributes of the multipath channel sounding problem appear to be well suited to CS approaches for reducing measurement acquisition time while simultaneously estimating time delays, multipath amplitudes, and angles of arrival. This paper presents results of the fusion of CS with modern channel sounding. We show measured propagation data from 60 GHz field trials and note the channel sparsity in time and space. We then propose an architecture for the first massively broadband CS channel sounder based on the Xampling framework (which we call the Channel Sounding Xampler) to exploit the sparsity, and we use field measurements to explore tradeoffs between analog and digital signal processing to perform channel impulse response (CIR) parameter estimation in real time. We also offer conceptual approaches for the Channel Sounding Xampler designed to trade off analog and digital components with the goal of improving CIR acquisition at sub-THz frequencies.
On-line Language Model Biasing for Statistical Machine Translation

The language model (LM) is a critical component in most statistical machine translation (SMT) systems, serving to establish a probability distribution over the hypothesis space. Most SMT systems use a static LM, independent of the source language input. While previous work has shown that adapting LMs based on the input improves SMT performance, none of the techniques has thus far been shown to be feasible for on-line systems. In this paper, we develop a novel measure of cross-lingual similarity for biasing the LM based on the test input. We also illustrate an efficient on-line implementation that supports integration with on-line SMT systems by transferring much of the computational load off-line. Our approach yields significant reductions in target perplexity compared to the static LM, as well as consistent improvements in SMT performance across language pairs (English-Dari and English-Pashto).
L1‐regularization path algorithm for generalized linear models

Summary. We introduce a path following algorithm for L1‐regularized generalized linear models. The L1‐regularization procedure is useful especially because it, in effect, selects variables according to the amount of penalization on the L1‐norm of the coefficients, in a manner that is less greedy than forward selection–backward deletion. The generalized linear model path algorithm efficiently computes solutions along the entire regularization path by using the predictor–corrector method of convex optimization. Selecting the step length of the regularization parameter is critical in controlling the overall accuracy of the paths; we suggest intuitive and flexible strategies for choosing appropriate values. We demonstrate the implementation with several simulated and real data sets.
Nuclear Segmentation in Microscope Cell Images: a Hand-segmented Dataset and Comparison of Algorithms

Image segmentation is an essential step in many image analysis pipelines and many algorithms have been proposed to solve this problem. However, they are often evaluated subjectively or based on a small number of examples. To fill this gap, we hand-segmented a set of 97 fluorescence microscopy images (a total of 4009 cells) and objectively evaluated some previously proposed segmentation algorithms. We focus on algorithms appropriate for high-throughput settings, where only minimal user intervention is feasible. The hand-labeled dataset (and all software used to compare methods) is publicly available to enable others to use it as a benchmark for newly proposed algorithms.
miR-127 Regulates Cell Proliferation and Senescence by Targeting BCL6

Cellular senescence occurs as a response to extracellular and intracellular stresses and contributes to aging and age-related pathologies. Emerging evidence suggests that cellular senescence also acts as a potent tumor suppression mechanism that prevents the oncogenic transformation of primary human cells. Recent reports have indicated that miRNAsact as key modulators of cellular senescence by targeting critical regulators of the senescence pathways. We previously reported that miR-127 is up-regulated in senescent fibroblasts. In this report, we identified miR-127 as a novel regulator of cellular senescence that directly targets BCL6. We further showed that miR-127 is down-regulated in breast cancer tissuesand that this down-regulation is associated with up-regulation of BCL6. Over-expression of miR-127 or depletion of BCL6 inhibits breast cancer cell proliferation. Our data suggest that miR-127 may function as a tumor suppressor that modulates the oncogene BCL6.
Functional Polymers from Graft Copolymerization of Binary Monomer Mixtures onto Lignocellulosic Biomass: Synthesis, Characterization and Properties Evaluation

Graft Copolymerization of binary mixture of methyl methacrylate (MMA) and acrylonitrile (AN) has been carried out onto Agave americana fibers in the presence of Ce (IV) ions at a temperature of 45.0 ± 0.1 0 C. The addition of acrylonitrile as comonomer has shown a significant increase in graft copolymerization of methyl methacrylate onto the lignocellulosic fibers. A synergistic effect of acrylonitrile on methyl methacrylate has been observed when graft copolymers were prepared using different feed compositions ranging from 0.2 to 0.8 at constant feed molarity of 5.0 x 10 -1 mol dm -3 . The rate of grafting has shown a linear dependence on the concentration of comonomers within the range of feed molarity from 3.5 x 10 -1 to 5.0 x 10 -1 mol dm -3 . The variation of grafting parameters such as graft yield and graft efficiency has been studied as a function of feed molarity and feed composition. The graft copolymers were characterized by various techniques such as FT-IR, TGA/DTA, X-RD and SEM. Further graft copolymers were evaluated for the physico- chemical properties such as swelling behavior in different solvents, moisture absorption behavior and resistance to chemicals as a function of percent grafting. The mechanism for grafting has been proposed based upon experimental observations.
Raptor packets: A packet-centric approach to distributed raptor code design

In this paper, we address the problem of distributed Raptor code design over information packets located across the network nodes. We propose a novel approach to this problem that consists of generating, encoding and dispersing Raptor packets across the network. Unlike recent node-centric proposals, where network nodes are responsible for collecting information packets and performing Raptor encoding, in the proposed packet-centric approach this task is assigned to Raptor packets. In a two-step encoding procedure that corresponds to precoding and LT-coding step of standard Raptor encoding, Raptor packets randomly traverse the network, collect and encode sufficient number of information packets following exactly a given degree distribution, and finish their paths in a random network node. The efficiency of the distributed Raptor coding scheme is confirmed by simulation results, where their performance is demonstrated to approach closely the performance of standard (centralized) Raptor codes.
Width of Gene Expression Profile Drives Alternative Splicing

Alternative splicing generates an enormous amount of functional and proteomic diversity in metazoan organisms. This process is probably central to the macromolecular and cellular complexity of higher eukaryotes. While most studies have focused on the molecular mechanism triggering and controlling alternative splicing, as well as on its incidence in different species, its maintenance and evolution within populations has been little investigated. Here, we propose to address these questions by comparing the structural characteristics as well as the functional and transcriptional profiles of genes with monomorphic or polymorphic splicing, referred to as MS and PS genes, respectively. We find that MS and PS genes differ particularly in the number of tissues and cell types where they are expressed.We find a striking deficit of PS genes on the sex chromosomes, particularly on the Y chromosome where it is shown not to be due to the observed lower breadth of expression of genes on that chromosome. The development of a simple model of evolution of cis-regulated alternative splicing leads to predictions in agreement with these observations. It further predicts the conditions for the emergence and the maintenance of cis-regulated alternative splicing, which are both favored by the tissue specific expression of splicing variants. We finally propose that the width of the gene expression profile is an essential factor for the acquisition of new transcript isoforms that could later be maintained by a new form of balancing selection.
Observer-based adaptive control of a variable reluctance motor: experimental results

Presents experimental results for two adaptive nonlinear controllers applied to a variable reluctance motor (VRM). The first controller requires both rotor position and velocity (full state) feedback, while the latter is a backstepping-type output feedback controller, requiring only rotor position measurement. Velocity for the full-state controller is estimated using a high-gain "differentiating filter". The experimental comparison shows that the observer-based controller provides a better transient response and a smaller steady-state tracking error when gains for both controllers are comparable.
The Transcriptional Activator Cat8p Provides a Major Contribution to the Reprogramming of Carbon Metabolism during the Diauxic Shift inSaccharomyces cerevisiae *

In yeast, the transition between the fermentative and the oxidative metabolism, called the diauxic shift, is associated with major changes in gene expression and protein synthesis. The zinc cluster protein Cat8p is required for the derepression of nine genes under nonfermentative growth conditions (ACS1,FBP1, ICL1, IDP2, JEN1,MLS1, PCK1, SFC1, andSIP4). To investigate whether the transcriptional control mediated by Cat8p can be extended to other genes and whether this control is the main control for the changes in the synthesis of the respective proteins during the adaptation to growth on ethanol, we analyzed the transcriptome and the proteome of a cat8Δ strain during the diauxic shift. In this report, we demonstrate that, in addition to the nine genes known as Cat8p-dependent, there are 25 other genes or open reading frames whose expression at the diauxic shift is altered in the absence of Cat8p. For all of the genes characterized here, the Cat8p-dependent control results in a parallel alteration in mRNA and protein synthesis. It appears that the biochemical functions of the proteins encoded by Cat8p-dependent genes are essentially related to the first steps of ethanol utilization, the glyoxylate cycle, and gluconeogenesis. Interestingly, no function involved in the tricarboxylic cycle and the oxidative phosphorylation seems to be controlled by Cat8p.
Knowledge Management System's Characteristics that facilitate Knowledge Sharing to Support Decision Making Processes in Multinational Corporations

Knowledge Management Systems (KMS) help decision makers and users leverage and share their knowledge. In the current fluid environment, the challenge for Multinational Corporations (MNC) is to accumulate knowledge that stems from various sources to support Decision Making (DM) processes. Based on semi-structured interviews with 42 subjects, this paper identifies factors that affect knowledge sharing through the use of KMS to support DM processes in MNC. The study extends the existing literature by proposing a new theoretical framework.
Cross-Disciplinary Detection and Analysis of Large Network Motifs

The detection of motifs has recently become an important part of network analysis across all disciplines. In order to detect these network motifs, software such as FANMOD and MAVisto has been created. Although relatively quick in detecting small motifs, this software is comparatively slower when identifying large motifs, such as those of size six or seven. As a result of this time constraint, the discovery and analysis of large motifs in networks of all disciplines is virtually nonexistent. Using FANMOD, motifs were detected in biological, social, and other networks, ranging from motifs with three nodes to those with eight nodes. Topological analysis revealed that similar networks have similar small motifs, but as size increases differences arise. Three-node motifs are the same for almost all undirected networks, and are commonly found in the larger motifs of these networks. Significance profiles of common motifs showed similar low-level structure in multiple undirected networks as well, while the analysis of directed networks revealed both similarities and dissimilarities between networks of different disciplines.
Organization of Multinational Activities and Ownership Structure

We develop a model of a multinational firm serving a foreign market that needs to decide about the location of production and the optimal ownership structure. We study how the location decision and the ownership choice interact, how these decisions are affected by (cultural) distance and how they depend on industry characteristics. Our analysis shows that (i) distance leads to less integration in low tech, but tends to lead to more integration in high tech industries, (ii) distance may have a non-monotonous effect on the likelihood of horizontal investments as opposed to exports, and (iii) marketing intensive industries are relatively more likely to produce close to their customers.
Social Happiness: Theory into Policy and Practice

Preface: On happiness, rationality, and empathy in scholarship and policy Part one: Happiness in policy discourse and research Introduction: prosperity debates and the happiness lens What really matters: concepts, evaluations and objections Effects of happiness (and unhappiness) Thinking ourselves happy: on the policy relevance of both subjectivity and objectivity Who makes happiness happen? On positive deviance, emotion work, and psychosocial contagion Governance and responsibility: towards the eudaimonic state? Assessing happiness: measurement and beyond Correlations and causal theories Part Two: Social happiness in policy and practice Love: fighting philophobia around the world The shape of good hope: Cultivating reasonable aspirations Positive parenting and cheerful childlessness Schooling for joy New gender agendas: feel-good feminism for fun and fulfilment Working for happiness, happily working, and work-life harmony Shopping for happiness: corporate happwash and consumption ethics Geronto-eudaimonics: late-life thriving for all Conclusions: review and prospects.
Automatic Term-Level Abstraction

Recent advances in decision procedures for Boolean satisfiability (SAT) and Satisfiability Modulo Theories (SMT) have increased the performance and capacity of formal verification techniques. Even with these advances, formal methods often do not scale to industrial-size designs, due to the gap between the level of abstraction at which designs are described and the level at which SMT solvers can be applied. In order to fully exploit the power of state-of-the-art SMT solvers, abstraction is necessary. However, applying abstraction to industrial-size designs is currently a daunting task, typically requiring major manual efforts. This thesis aims to bridge the gap between the level at which designs are described and the level at which SMT solvers can reason efficiently, referred to as the term level. This thesis presents automatic term-level abstraction techniques in the context of formal verification applied to hardware designs. The techniques aim to perform abstraction as automatically as possible, while requiring little to no user guidance. Data abstraction and function abstraction are the foci of this work. The abstraction techniques presented herein rely on combining static analysis, random simulation, machine learning, and abstraction-refinement in novel ways, resulting in more intelligent and scalable formal verification methodologies. The data abstraction procedure presented in this work uses static analysis to identify portions of a hardware design that can be re-encoded in a theory other than the theory of bit vectors, with the goal of creating an easier to reason about verification model. In addition, the data abstraction procedure can provide feedback that can help the designer create hardware designs that are easier to verify. The function abstraction procedures described in this work rely on static analysis, random simulation, machine learning, and counterexample-guided abstraction-refinement to identify and abstract functional blocks that are hard for formal tools to reason about. Random simulation is used to identify functional blocks that will likely yield substantial performance increases if they were to be abstracted. A static analysis-based technique, ATLAS, and a separate technique, CAL, based on a combination of machine learning and counterexample-guided abstraction-refinement, are then used to compute conditions under which it is precise to abstract. That is, functional blocks are abstracted in a manner that avoids producing spurious counterexamples. Experimental evidence is presented that proves the efficacy and efficiency of the data and function abstraction procedures. The experimental benchmarks are drawn from a wide range of hardware designs including network-on-chip routers, low-power circuits, and microprocessor designs.
A web-based three-dimensional protein retrieval system by matching visual similarity

SUMMARY A web-based three-dimensional (3D) protein retrieval system is available for protein structure data including all PDB and FSSP dataset. In this system, we use a visual-based matching method to compare the protein structure from multiple viewpoints. It takes less than three seconds for each query with 90% accuracy on an average.
Diffusion approximation of frequency sensitive competitive learning

The focus of this paper is a convergence study of the frequency sensitive competitive learning (FSCL) algorithm. We approximate the final phase of FSCL learning by a diffusion process described by the Fokker-Plank equation. Sufficient and necessary conditions are presented for the convergence of the diffusion process to a local equilibrium. The analysis parallels that by Ritter-Schulten (1988) for Kohonen's self-organizing map. We show that the convergence conditions involve only the learning rate and that they are the same as the conditions for weak convergence described previously. Our analysis thus broadens the class of algorithms that have been shown to have these types of convergence characteristics.
Effect of the rotation matrix factorization on the quality of images rotation

Most efficient algorithms for rotating 2D images are based on the succession of three translations, following the lines, then the columns and the lines of the image again (LCL). These translations result from the decomposition of the rotation matrix into a product of an upper triangular matrix, a lower triangular matrix and an upper triangular matrix (ULU). We have shown in this paper that the decomposition of the rotation matrix is not unique and have separated it into a product of a lower triangular matrix, an upper triangular matrix and a lower triangular matrix (LUL). This new decomposition led to a new algorithm based on a succession of three translations following the columns, then the lines and the columns of the images again (CLC). Statistical analysis of experimental results showed that the computational complexity of images rotation does not depend significantly (p>0.05) on the factorization ULU or LUL, whereas the precision depends on it significantly (p<0.05). Each of the two algorithms of rotation is more precise for certain images and for certain angles. The method can be generalized in a case of 3D image rotation using the Euler angles.
Linear and nonlinear PD-type control of robotic manipulators for trajectory tracking

This paper presents trajectory tracking of robot manipulators using linear and nonlinear proportional-derivative (PD) type controllers. It is shown that the controller gains which are nonlinear functions of system states can improve tracking performance and prevent actuator saturation. Furthermore, it is shown that global asymptotic stability of the closed-loop system can be achieved with the proposed PD-type controllers. Simulation results on a single-link robot and a typical two-degrees-of-freedom robot demonstrate excellent performance using the proposed control schemes.
The social context of nonverbal behavior

1. Introducing nonverbal behavior within a social context Pierre Philippot, Robert S. Feldman and Erik J. Coats 2. Cultural influences on nonverbal expressions of emotion Cenita Kupperbusch, David Matsumoto, Kristie Kooken, Sherry Loewinger, Hideko Uchida, Carinda Wilson-Cohn and Nathan Yrizarry 3. Option or obligation to smile: the effects of power and gender on facial expression Marianne LaFrance and Marvin A. Hecht 4. Emotional displays and dissemblance in childhood: implications for self-presentation Carolyn Saarni, Hannelore Weber 5. Family expressiveness: a retrospective and new directions for research Amy G. Halberstadt, Valerie W. Crisp and Kimberly L. Eaton 6. The influence of television on children's nonverbal behavior Erik J. Coats, Robert S. Feldman and Pierre Philippot 7. Group membership and the decoding of nonverbal behavior Gilles Kirouac and Ursula Hess 8. Mimicry: facts and fiction Ursula Hess, Pierre Philippot and Sylvie Blairy 9. Facial expression and emotion: a situationist view Jose-Miguel Fernandez-Dols 10. Facial behavior alone and in the presence of others Hugh Wagner and Victoria Lee 11. The social and emotional functions of facial displays Antony S. R. Manstead, Agneta H. Fischer and Esther Jakobs 12. The evolution of parallel process model of nonverbal behavior Miles L. Patterson 13. Conflict issues and conflict strategies as contexts for nonverbal behavior in close relationships Judith A. Feeney, Patricia Noller, Grania Sheehan and Candida Peterson 14. Love's best habit: deception in the context of relationships D. Eric Anderson, Matthew E. Ansfield, Bella M. DePaulo.
Noncircular Complex ICA by Generalized Householder Reflections

We develop an efficient algorithm for noncircular complex independent component analysis (ICA) by optimizing the rows of separation matrix independently using a generalized Householder reflector and Newton method. We apply the new algorithm to the separation of complex generalized Gaussian distributed (GGD) sources, and propose a convenient way to learn the noncircularity of the sources. Simulation results are reported to study the convergence behavior, and confirm the superior performance of the new algorithm.