Miracle-dz/UniFAR-FTUs huggingface dataset:
Heralding efficiency and correlated-mode coupling of near-IR fiber-coupled photon pairs

We report on a systematic experimental study of the heralding efficiency and generation rate of telecom-band infrared photon pairs generated by spontaneous parametric down-conversion and coupled to single-mode optical fibers. We define the correlated-mode coupling efficiency, an inherent source efficiency, and explain its relation to heralding efficiency. For our experiment, we developed a reconfigurable computer-controlled pump-beam and collection-mode optical apparatus which we used to measure the generation rate and correlated-mode coupling efficiency. The use of low-noise, high-efficiency superconducting nanowire single-photon detectors in this setup allowed us to explore focus configurations with low overall photon flux. The measured data agree well with theory, and we demonstrated a correlated-mode coupling efficiency of 97% ± 2%, which is the highest efficiency yet achieved for this type of system. These results confirm theoretical treatments and demonstrate that very high overall heralding efficiencies can, in principle, be achieved in quantum optical systems. It is expected that these results and techniques will be widely incorporated into future systems that require, or benefit from, a high heralding efficiency.
Genetic Analysis of SUMOylation in Arabidopsis: Conjugation of SUMO1 and SUMO2 to Nuclear Proteins Is Essential1[OA]

The posttranslational addition of small ubiquitin-like modifiers (SUMOs) to other intracellular proteins has been implicated in a variety of eukaryotic functions, including modifying cytoplasmic signal transduction, nuclear import and subnuclear compartmentalization, DNA repair, and transcription regulation. For plants, in particular, both genetic analyses and the rapid accumulation of SUMO conjugates in response to various adverse environmental conditions suggest that SUMOylation plays a key role in the stress response. Through genetic analyses of various SUMO conjugation mutants, we show here that the SUMO1 and SUMO2 isoforms, in particular, and SUMOylation, in general, are essential for viability in Arabidopsis (Arabidopsis thaliana). Null T-DNA insertion mutants affecting the single genes encoding the SUMO-activating enzyme subunit SAE2 and the SUMO-conjugating enzyme SCE1 are embryonic lethal, with arrest occurring early in embryo development. Whereas the single genes encoding the SUMO1 and SUMO2 isoforms are not essential by themselves, double mutants missing both are also embryonic lethal. Viability can be restored by reintroduction of SUMO1 expression in the homozygous sum1-1 sum2-1 background. Various stresses, like heat shock, dramatically increase the pool of SUMO conjugates in planta. This increase involves SUMO1 and SUMO2 and is mainly driven by the SUMO protein ligase SIZ1, with most of the conjugates accumulating in the nucleus. Taken together, it appears that SIZ1-mediated conjugation of SUMO1 and SUMO2 to other intracellular proteins is essential in Arabidopsis, possibly through stress-induced modification of a potentially diverse pool of nuclear proteins.
Games with a Purpose for the Semantic Web

Weaving the semantic Web requires that humans contribute their labor and judgment for creating, extending, and updating formal knowledge structures. Hiding such tasks behind online multiplayer games presents the tasks as fun and intellectually challenging entertainment. The games we've presented are the first prototypes of the OntoGame series. We're extending and improving the scenarios in several directions.
Construction of power flow feasibility sets

We develop a new approach for construction of convex analytically simple regions where the AC power flow equations are guaranteed to have a feasible solutions. Construction of these regions is based on efficient semidefinite programming techniques accelerated via sparsity exploiting algorithms. Resulting regions have a simple geometric shape in the space of power injections (polytope or ellipsoid) and can be efficiently used for assessment of system security in the presence of uncertainty. Efficiency and tightness of the approach is validated on a number of test networks.
An Adaptive Multiscale Finite Element Method

This work is devoted to an adaptive multiscale finite element method (MsFEM) for solving elliptic problems with rapidly oscillating coefficients. Starting from a general version of the MsFEM with oversampling, we derive an a posteriori estimate for the $H^1$-error between the exact solution of the problem and a corresponding MsFEM approximation. Our estimate holds without any assumptions on scale separation or on the type of the heterogeneity. The estimator splits into different contributions which account for the coarse grid error, the fine grid error, and the oversampling error. Based on the error estimate, we construct an adaptive algorithm that is validated in numerical experiments.
A dyadic analysis of negative emotion personality predisposition effects with psychological distress in neonatal intensive care unit parents.

The relation of parents personality predisposition to guilt, shame, and fear of death with symptoms ofposttraumatic stress, anxiety, and depression was examined in 67 couples who were the parents of sicknewborn infants previously hospitalized in a Neonatal Intensive Care Unit. The Actor-Partner Interde-pendence Model method of dyadic analysis was used to determine the relation of parents predispositionto guilt, shame, and fear of death to their own posttraumatic stress, anxiety, and depression (actor effect),and to the posttraumatic stress, anxiety, and depression of their partners (partner effect). The couplemembers were not statistically distinguishable by gender. Guilt had a significant actor effect withposttraumatic stress, anxiety, and depression, and a significant partner effect with anxiety and depression.Guilt explained 1518% of the variance in these measures of psychological distress. Fear of death hada significant actor effect with all measures of psychological distress, but none of the partner effects wasstatistically significant. Shame did not predict posttraumatic stress or anxiety, but there was a significantactor effect with depression. Fear of death and shame explained only small proportions (36%) of thevariance in the psychological distress variables with which they had statistically significant relationships.The guilt effects with psychological distress were intrapersonal (actor) and interpersonal (partner),whereas the fear of death and shame effects were intrapersonal. These emotions, particularly fear of deathand shame, tend to be elusive as well as aversive, but they are important forerunners of psychopathologyand deserve specific attention in psychological counseling.Keywords: parents, neonatal intensive care, posttraumatic stress, anxiety, depression
Time selected multiple algorithms for reliable f0 tracking in difficult recording conditions

Prosodic data mining in large spontaneous speech corpora often requires acoustic analysis of recordings done in poor conditions. The most detrimental pertain to the absence first harmonic in voiced segments and the presence of echo. In such cases it is very difficult to separate inter vocalic consonantal voicing from echo, or recover Fo from remaining harmonics. To address these problems, 10 different pitch tracking algorithms were implemented in the software program WinPitch, in order to allow an expert user, guided by an underlying displayed narrow band spectrogram, to select manually the appropriate process to deliver a satisfactory Fo curve for a selected time segment.
On Effective Decomposition of Training Data Sets for Min-Max Modular Classifier

Our previous work shows that traditional randomization partition method for min-max modular (M) classifier can not ensure stable generalization accuracy when the number of two-class problems increases. To overcome this drawback, we consider how to effectively decompose the training data set for a two-class problem in this paper. We propose four basic clustering and anti-clustering strategies and their combinations for partitioning training data sets. These four basic strategies are hyperplane decomposition, K-means algorithm, anti-K-means algorithm, and scatter procedure. Our experimental results show that all the proposed clustering and anti-clustering strategies are superior to the traditional random partition method.
Realism, Deweyan Pragmatism, and Educational Research

In a recent exchange, Cleo H. Cherryholmes (1992)opposed Rortyan pragmatism to the “scientific realism'” of Ernest R. House (1991 , 1992). In my article, I hope to find a middle ground between Cherryholmes and House that might be more attractive to many in the field of education. My article emphasizes some of the most important aspects of Dewey's philosophy, including his social behaviorist theory of meaning, his naturalistic metaphysics, philosophy of nature, social constructivism, and transactional realism. For some reason, most educational theorists, researchers, and practitioners have almost entirely ignored these crucial Deweyan themes, often with disastrous consequences. I will conclude by trying to show how all of these themes converge into Dewey's defense of democracy as not only the best form of government but also the best way for the community of educators to conduct and consume educational research.
The folding thermodynamics and kinetics of crambin using an all-atom Monte Carlo simulation.

We present a novel Monte Carlo simulation of protein folding, in which all heavy atoms are represented as interacting hard spheres. This model includes all degrees of freedom relevant to folding, all side-chain and backbone torsions, and uses a Go potential. In this study, we focus on the 46 residue alpha/beta protein crambin and two of its structural components, the helix and helix hairpin. For a wide range of temperatures, we recorded multiple folding events of these three structures from random coils to native conformations that differ by less than 1 A C(alpha) dRMS from their crystal structure coordinates. The thermodynamics and kinetic mechanism of the helix-coil transition obtained from our simulation shows excellent agreement with currently available experimental and molecular dynamics data. Based on insights obtained from folding its smaller structural components, a possible folding mechanism for crambin is proposed. We observed that the folding occurs via a cooperative, first order-like process, and that many folding pathways to the native state exist. One particular sequence of events constitutes a "fast-folding" pathway where kinetic traps are avoided. At very low temperatures, a kinetic trap arising from the incorrect packing of side-chains was observed. These results demonstrate that folding to the native state can be observed in a reasonable amount of time on desktop computers even when an all-atom representation is used, provided the energetics sufficiently stabilize the native state.
Explaining Inflation in the Aftermath of the Great Recession

This paper considers whether the Phillips curve can explain the recent behavior of inflation in the United States. Standard formulations of the model predict that the ongoing large shortfall in economic activity relative to full employment should have led to deflation over the past several years. I confirm previous findings that the slope of the Phillips curve has varied over time and probably is lower today than it was several decades ago. This implies that estimates using historical data will overstate the responsiveness of inflation to present-day economic conditions. I modify the traditional Phillips curve to explicitly account for time variation in its slope and show how this modified model can explain the recent behavior of inflation without relying on anchored expectations. Specifically, I explore reasons why the slope might vary over time, focusing on implications of the sticky-price and sticky-information approaches to price adjustment. These implications suggest that the inflation environment and uncertainty about regional economic conditions should influence the slope of the Phillips curve. I introduce proxies to account for these effects and find that a Phillips curve modified to allow its slope to vary with uncertainty about regional economic conditions can best explain the recent path of inflation.
A Mutant Cell with a Novel Defect in MHC Class I Quality Control1

COS7 (African Green Monkey kidney) cells stably transfected with the mouse MHC class I allele H-2Kb were mutagenized, selected for low surface expression of endogenous MHC class I products, and subcloned. A mutant cell line, 4S8.12, expressing very low surface MHC class I (∼5% of parental levels) was identified. This cell line synthesized normal levels of the MHC class I H chain and β2-microglobulin, as well as normal levels of TAP, tapasin, GRP78, calnexin, calreticulin, ERp57, and protein disulfide isomerase. Full-length OVA was processed to generate presented H-2Kb-SIINFEKL complexes with equal efficiency in wild-type and mutant cells, demonstrating that proteasomes, as well as TAP and tapasin, functioned normally. Therefore, all the known components of the MHC class I Ag presentation pathway were intact. Nevertheless, primate (human and monkey) MHC class I H chain and β2-microglobulin failed to associate to form the normal peptide-receptive complex. In contrast, mouse H chains associated with β2-microglobulin normally and bound peptide at least as well as in wild-type cells. The 4S8.12 cells provide strong genetic evidence for a novel component in the MHC class I pathway. This as-yet unidentified gene is important in early assembly of primate, but not mouse, MHC class I complexes.
Wadge-like reducibilities on arbitrary quasi-Polish spaces

The structure of the Wadge degrees on zero-dimensional spaces is very simple (almost well ordered), but for many other natural nonzero-dimensional spaces (including the space of reals) this structure is much more complicated. We consider weaker notions of reducibility, including the so-called Δ0 α-reductions, and try to find for various natural topological spaces X the least ordinal α X such that for every α X ⩽ β < ω1 the degree-structure induced on X by the Δ0 β-reductions is simple (i.e. similar to the Wadge hierarchy on the Baire space). We show that α X ⩽ ω for every quasi-Polish space X, that α X ⩽ 3 for quasi-Polish spaces of dimension ≠ ∞, and that this last bound is in fact optimal for many (quasi-)Polish spaces, including the real line and its powers.
Syntactic deficits and the construction of local phrases in spoken language comprehension

An aphasic patient who has problems in using syntactic structural information appropriately will be unable to construct a normal semantic representation of an utterance. In this paper we report data from one patient, DE, who has a syntactic processing deficit but is able to interpret an utterance semantically. We explore the hypothesis that he structures the speech input into local phrases by conducting two word monitoring studies. In these studies, we manipulate the availability of various types of local and global structural information to determine how word monitoring latencies are affected by the presence or absence of different kinds of processing information. We conclude that DE does construct local structures as he processes an utterance, and that the kinds of information upon which these local structures are built depend upon the type of material to which he is listening.
The importance of districts

Effectiveness studies have largely concentrated on the school as the unit of analysis, although an increasing number have directed their attention to the teacher as the main unit of interest. But policy is often directed through organisations at the district level or what is sometimes known as the Education Authority (EA). Few studies have explored their importance, but the seminal study by Willms (1987) stands out. This paper reports an analysis of 7 large national databases from England using 3-level multilevel models with pupils nested within primary schools that are in turn nested within districts. All tell the same story: The EA attended by pupils has almost no relevance to their progress.
StreamIt: A Compiler for Streaming Applications ⁄

Streamingprogramsrepresentanincreasinglyimportantand widespread class of applications that holds unprecedented opportunities for high-impact compiler technology. Unlike sequentialprogramswithobscureddependenceinformation and complex communication patterns, a stream program is naturally written as a set of concurrent fllters with regular steady-state communication. The StreamIt language aims to provide a natural, high-level syntax that improves programmer productivity in the streaming domain. At the same time, the language imposes a hierarchical structure onthestreamgraphthatenablesnovelrepresentationsand optimizations within the StreamIt compiler. We deflne the \stream dependence function", a fundamental relationship betweentheinputchannelsoftwoflltersinastreamgraph. We also describe a suite of stream optimizations, a denotational semantics for validating these optimizations, and a novel phased scheduling algorithm for stream graphs. In addition,wehaveimplementedaprototypeoftheStreamIt optimizing compiler thatis showingpromising results.
Fast mining of complex time-stamped events

Given a collection of complex, time-stamped events, how do we find patterns and anomalies? Events could be meetings with one or more persons and one or more agenda items at zero or more locations (e.g., teleconferences), or they could be publications with authors, keywords, publishers, etc. In such settings, we want to find time stamps that look similar to each other and group them; we also want to find anomalies. In addition, we want our approach to provide interpretations of the clusters and anomalies by annotating them. Furthermore, we want our approach to automatically find the right time-granularity in which to do analysis. Lastly, we want fast, scalable algorithms for all these problems. We address the above challenges through two main ideas. The first (T3) is to turn the problem into a graph analysis problem, by carefully treating each time stamp as a node in a graph. This viewpoint brings to bear the vast machinery of graph analysis methods (PageRank, graph partitioning, proximity analysis, and CenterPiece Subgraphs, to name a few). Thus, T3 can automatically group the time stamps into meaningful clusters and spot anomalies. Moreover, it can select representative events/persons/locations for each cluster and each anomaly, as their interpretations. The second idea (MT3) is to use temporal multi-resolution analysis (e.g., minutes, hours, days). We show that MT3 can quickly derive results from finer-to-coarser resolutions, achieving up to 2 orders of magnitude speedups. We verify the effectiveness as well as efficiency of T3 and MT3 on several real datasets.
Closeness Centrality Extended to Unconnected Graphs: the Harmonic Centrality Index

AbstractSocial network analysis is a rapid expanding interdisciplinary ﬁeld,growingfromworkofsociologists,physicists,historians,mathematicians,political scientists, etc. Some methods have been commonly accepted inspite of defects, perhaps because of the rareness of synthetic work like(Freeman, 1978; Faust & Wasserman, 1992). In this article, we proposeanalternativeindexofclosenesscentralitydeﬁnedonundirectednetworks. We show that results from its computation on real cases are identical tothoseoftheclosenesscentralityindex,withsamecomputationalcomplex-ityandwegivesomeinterpretations. Animportantpropertyisitsuseinthecaseofunconnectednetworks. 1 Introduction The study of centrality is one of the most popular subject in the analysisof social networks. Determining the role of an individual within a society, itsinﬂuence or the ﬂows of information on which he can intervene are examplesofapplicationsofcentralityindices. Theyaredeﬁnedatanactor-levelandareexpectedtocompareandbetterunderstandrolesofeachindividualinthenet-work. Inaddition,agraph-levelindexcalledcentralization(i.e. howmuchtheindex value of the most central node is bigger than the others) is deﬁned foreachexistingindex.Each index provides a way to highlight properties of individuals, depen-dentlyonitsdeﬁnition. Forexample,degreecentralityattributeshighmeasureto an individual having great inﬂuence on its neighbors. Closeness centralityhighlights the players who will be able to contact easily all other members ofthenetwork. Betweennesscentralitygiveshighestvaluestoindividualsthroughwhominformationismorelikelytopass.1
X-CNN: Cross-modal convolutional neural networks for sparse datasets

In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network—thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks). The constituent networks are individually designed to learn the output function on their own subset of the input data, after which cross-connections between them are introduced after each pooling operation to periodically allow for information exchange between them. This injection of knowledge into a model (by prior partition of the input data through domain knowledge or unsupervised methods) is expected to yield greatest returns in sparse data environments, which are typically less suitable for training CNNs. For evaluation purposes, we have compared a standard four-layer CNN as well as a sophisticated FitNet4 architecture against their cross-modal variants on the CIFAR-10 and CIFAR-100 datasets with differing percentages of the training data being removed, and find that at lower levels of data availability, the X-CNNs significantly outperform their baselines (typically providing a 2–6% benefit, depending on the dataset size and whether data augmentation is used), while still maintaining an edge on all of the full dataset tests.
Multi-robot collision avoidance with localization uncertainty

This paper describes a multi-robot collision avoidance system based on the velocity obstacle paradigm. In contrast to previous approaches, we alleviate the strong requirement for perfect sensing (i.e. global positioning) using Adaptive Monte-Carlo Localization on a per-agent level. While such methods as Optimal Reciprocal Collision Avoidance guarantee local collision-free motion for a large number of robots, given perfect knowledge of positions and speeds, a realistic implementation requires further extensions to deal with inaccurate localization and message passing delays. The presented algorithm bounds the error introduced by localization and combines the computation for collision-free motion with localization uncertainty. We provide an open source implementation using the Robot Operating System (ROS). The system is tested and evaluated with up to eight robots in simulation and on four differential drive robots in a real-world situation.