Reviewerly/exHarmony huggingface dataset:
Accurate point correspondences using a modified coherent point drift algorithm

A crucial challenge of statistical shape modeling is to find corresponding points. The task is much more difficult especially in cases of objects with greater shape diversities (such as liver). In this paper, we propose a new fast algorithm to find corresponding points based on modified Coherent Point Drift technique which is comparable to the state-of-the-art “Minimum Description Length” (MDL) algorithm. Combining fuzzy correspondence and deterministic annealing method together with non-rigid registration of shapes helps to find corresponding points of different liver shapes. We optimized the algorithm parameters to prevent it from producing unacceptable results. Evaluation of the results was performed using Compactness, Generality, Specificity, and run-time measures. We compared our method using both conventional and state-of-the-art techniques including “Iterative Closest Point” (ICP), “Thin Plate Spline-Robust Point Matching” (TPS-RPM) and MDL. The performance of our method is superior to ICP and TPS-RPM algorithms and our results are close to the MDL results. The Generality of ICP, TPS-RPM, MDL and our method are 7.1 ± 1.80, 3.78 ± 0.96, 2.64 ± 0.65 and 3.64 ± 0.85 respectively. The Specificity of MDL and TPS-RPM is the same as ours (0.21 ± 0.06) and the ICP stands in the fourth rank (0.30 ± 0.18). The Compactness of ICP, TPS-RPM, MDL and our method are 23, 19, 16 and 18 respectively. The run-time of ICP, TPS-RPM, MDL and our method are 30, 390, 3600 and 68 s. Contrary to the MDL, our algorithm does not trap into degenerate cases.
When Negative Turns Positive and Vice Versa: The Case of Repeated Measurements

In increasing numbers of studies, participants are measured on the same variables of interest – such as task performance and effort associated with task performance – several times. Three statistical approaches that are encountered in health professions education research in this context are: aggregation, multiplication, and repeated measures analysis. In the aggregation approach, repeated measurements are reduced to average scores (i.e., aggregates) for each measure and these are used in subsequent analysis. In the multiplication approach, repeated measurements are treated as if they came from different instead of from the same respondents. In the repeated measures approach, the repeated measurements are treated as is. While the aggregation and multiplication approach are frequently encountered, the repeated measures approach is not. Through a simulated data example that incorporates features from studies on this kind of data encountered in the literature, this article compares the three aforementioned approaches in terms of information and statistical validity. The comparison illustrates that, contrary to repeated measures analysis, the aggregation and multiplication approach fail to capture essential information from repeated measurements data and can result in erroneous implications for future research and practice (i.e., ecological fallacy). The findings from this comparison have implications for all statistical techniques that are based on correlation and regression; failing to account for repeated measurements structures in data can distort correlations and all statistics based on them.
Gaussian-Gamma collaborative filtering: A hierarchical Bayesian model for recommender systems

The traditional collaborative filtering (CF) suffers from two key challenges, namely, the normal assumption that it is not robust, and it is difficult to set in advance the penalty terms of the latent features. We therefore propose a hierarchical Bayesian model-based CF and the related inference algorithm. Specifically, we impose a Gaussian-Gamma prior on the ratings, and the latent features. We show the model is more robust, and the penalty terms can be adapted automatically in the inference. We use Gibbs sampler for the inference and provide a statistical explanation. We verify the performance using both synthetic and real datasets.
Efficient dynamic dictionary matching with DAWGs and AC-automata

The dictionary matching is a task to find all occurrences of pattern strings in a set D (called a dictionary) on a text string T. The Aho–Corasick-automaton (AC-automaton) which is built on D is a fundamental data structure which enables us to solve the dictionary matching problem in O(dlog⁡σ) preprocessing time and O(nlog⁡σ+occ) matching time, where d is the total length of the patterns in the dictionary D, n is the length of the text, σ is the alphabet size, and occ is the total number of occurrences of all the patterns in the text. The dynamic dictionary matching is a variant where patterns may dynamically be inserted into and deleted from the dictionary D. This problem is called semi-dynamic dictionary matching if only insertions are allowed. In this paper, we propose two efficient algorithms that can solve both problems with some modifications. For a pattern of length m, our first algorithm supports insertions in O(mlog⁡σ+log⁡d/log⁡log⁡d) time and pattern matching in O(nlog⁡σ+occ) for the semi-dynamic setting. This algorithm also supports both insertions and deletions in O(σm+log⁡d/log⁡log⁡d) time and pattern matching in O(n(log⁡d/log⁡log⁡d+log⁡σ)+occ(log⁡d/log⁡log⁡d)) time for the dynamic dictionary matching problem by some modifications. This algorithm is based on the directed acyclic word graph (DAWG) of Blumer et al. (JACM 1987). Our second algorithm, which is based on the AC-automaton, supports insertions in O(mlog⁡σ+uf+uo) time for the semi-dynamic setting and supports both insertions and deletions in O(σm+uf+uo) time for the dynamic setting, where uf and uo respectively denote the numbers of states in which the failure function and the output function need to be updated. This algorithm performs pattern matching in O(nlog⁡σ+occ) time for both settings. Our algorithm achieves optimal update time for AC-automaton based methods over constant-size alphabets, since any algorithm which explicitly maintains the AC-automaton requires Ω(m+uf+uo) update time.
Pairwise concurrence in cyclically symmetric quantum states

We provide an initial characterization of pairwise concurrence in quantum states which are invariant under cyclic permutations of party labeling. We prove that maximal entanglement can be entirely described by adjacent pairs, then give explicit descriptions of those states in specific subsets of 4 and 5 qubit states - X states. We also construct a monogamy bound on shared concurrences in the same subsets in 4 and 5 qubits, finding that above non-maximal entanglement thresholds, no other entanglements are possible.
AXIOMATIZATION OF PROVABLE n-PROVABILITY

Abstract A formula φ is called n-provable in a formal arithmetical theory S if φ is provable in S together with all true arithmetical ${{\rm{\Pi }}_n}$ -sentences taken as additional axioms. While in general the set of all n -provable formulas, for a fixed $n &gt; 0$ , is not recursively enumerable, the set of formulas φ whose n -provability is provable in a given r.e. metatheory T is r.e. This set is deductively closed and will be, in general, an extension of S . We prove that these theories can be naturally axiomatized in terms of progressions of iterated local reflection principles. In particular, the set of provably 1-provable sentences of Peano arithmetic $PA$ can be axiomatized by ${\varepsilon _0}$ times iterated local reflection schema over $PA$ . Our characterizations yield additional information on the proof-theoretic strength of these theories (w.r.t. various measures of it) and on their axiomatizability. We also study the question of speed-up of proofs and show that in some cases a proof of n -provability of a sentence can be much shorter than its proof from iterated reflection principles.
Gaps and interleaving of point processes in sampling from a residual allocation model

This article presents a limit theorem for the gaps $\widehat{G}_{i:n}:= X_{n-i+1:n} - X_{n-i:n}$ between order statistics $X_{1:n} \le \cdots \le X_{n:n}$ of a sample of size $n$ from a random discrete distribution on the positive integers $(P_1, P_2, \ldots)$ governed by a residual allocation model (also called a Bernoulli sieve) $P_j:= H_j \prod_{i=1}^{j-1}(1-H_i)$ for a sequence of independent random hazard variables $H_i$ which are identically distributed according to some distribution of $H \in (0,1)$ such that $- \log(1 - H)$ has a non-lattice distribution with finite mean $\mu_{\mbox{log}}$. As $n\to \infty$ the finite dimensional distributions of the gaps $\widehat{G}_{i:n}$ converge to those of limiting gaps $G_i$ which are the numbers of points in a stationary renewal process with i.i.d. spacings $- \log(1 - H_j)$ between times $T_{i-1}$ and $T_i$ of births in a Yule process, that is $T_i := \sum_{k=1}^i \varepsilon_{k}/k$ for a sequence of i.i.d. exponential variables $\varepsilon_k$ with mean 1. A consequence is that the mean of $\widehat{G}_{i:n}$ converges to the mean of $G_i$, which is $1/(i \mu_{\mbox{log}} )$. This limit theorem simplifies and extends a result of Gnedin, Iksanov and Roesler for the Bernoulli sieve.
Parametrization of quantum states based on the quantum state discrimination problem

A discrimination problem consists of N linearly independent pure quantum states $$\Phi =\{|\phi _i\rangle \}$$ and the corresponding occurrence probabilities $$\eta =\{\eta _i\}$$ . With any such problem, we associate, up to a permutation over the probabilities $$\{\eta _i\}$$ , a unique pair of density matrices $$\varvec{\rho _{_{T}}}$$ and $$\varvec{\eta _{{p}}}$$ defined on the N-dimensional Hilbert space $$\mathcal {H}_N$$ . The first one, $$\varvec{\rho _{_{T}}}$$ , provides a new parametrization of a generic full-rank density matrix in terms of the parameters of the discrimination problem, i.e., the mutual overlaps $$\gamma _{ij}=\langle \phi _i|\phi _j\rangle $$ and the occurrence probabilities $$\{\eta _i\}$$ . The second one, on the other hand, is defined as a diagonal density matrix $$\varvec{\eta _p}$$ with the diagonal entries given by the probabilities $$\{\eta _i\}$$ with the ordering induced by the permutation p of the probabilities. $$\varvec{\rho _{_{T}}}$$ and $$\varvec{\eta _{{p}}}$$ capture information about the quantum and classical versions of the discrimination problem, respectively. In this sense, when the set $$\Phi $$ can be discriminated unambiguously with probability one, i.e., when the states to be discriminated are mutually orthogonal and can be distinguished by a classical observer, then $$\varvec{\rho _{_{T}}} \rightarrow \varvec{\eta _{{p}}}$$ . Moreover, if the set lacks its independency and cannot be discriminated anymore, the distinguishability of the pair, measured by the fidelity $$F(\varvec{\rho _{_{T}}}, \varvec{\eta _{{p}}})$$ , becomes minimum. This enables one to associate with each discrimination problem a measure of discriminability defined by the fidelity $$F(\varvec{\rho _{_{T}}}, \varvec{\eta _{{p}}})$$ . This quantity, though distinct from the maximum probability of success, has the advantage of being easy to calculate, and in this respect, it can find useful applications in estimating the extent to which the set is discriminable.
SURE-Fuse WFF: A Multi-Resolution Windowed Fourier Analysis for Interferometric Phase Denoising

Interferometric phase (InPhase) imaging is an important part of many present-day coherent imaging technologies. Often in such imaging techniques, the acquired images, known as interferograms, suffer from two major degradations: 1) phase wrapping caused by the fact that the sensing mechanism can only measure sinusoidal $2\pi$-periodic functions of the actual phase, and 2) noise introduced by the acquisition process or the system. This work focusses on InPhase denoising which is a fundamental restoration step to many posterior applications of InPhase, namely to phase unwrapping. The presence of sharp fringes that arises from phase wrapping makes InPhase denoising a hard-inverse problem. Motivated by the fact that the InPhase images are often locally sparse in Fourier domain, we propose a multi-resolution windowed Fourier filtering (WFF) analysis that fuses WFF estimates with different resolutions, thus overcoming the WFF fixed resolution limitation. The proposed fusion relies on an unbiased estimate of the mean square error derived using the Stein's lemma adapted to complex-valued signals. This estimate, known as SURE, is minimized using an optimization framework to obtain the fusion weights. Strong experimental evidence, using synthetic and real (InSAR & MRI) data, that the developed algorithm, termed as SURE-fuse WFF, outperforms the best hand-tuned fixed resolution WFF as well as other state-of-the-art InPhase denoising algorithms, is provided.
A Second Order Multi-Stencil Fast Marching Method With a Non-Constant Local Cost Model

The Fast Marching method is widely employed in several fields of image processing.Some years ago a Multi-Stencil version (MSFM) was introduced to improve its accuracy by solving the equation for a set of stencils and choosing the best solution at each considered node.The following work proposes a modified numerical scheme for MSFM to take into account the variation of the local cost, which has proven to be second order.The influence of the stencil set choice on the algorithm outcome with respect to stencil orthogonality and axis swapping is also explored, where stencils are taken from neighborhoods of varying radius.The experimental results show that the proposed schemes improve the accuracy of their original counterparts, and that the use of permutation-invariant stencil sets provides robustness against shifted vector coordinates in the stencil set.
Musical roads: design, construction and potential economic and safety benefits

There are more than 30 stretches of road in the world (mostly in Japan) that use rumble strips to create musical notes as cars drive along them. This paper discusses the three main structural forms and construction methods used to create the musical effect. Beyond their economic value as a tourist attraction, some ‘musical roads’ have been used in dangerous stretches to discourage speeding, with the melody providing a warning or only being recognisable at a safe speed.
Populating Narratives Using Wikidata Events: An Initial Experiment

The study presented in this paper is part of our research aimed at improving the search functionalities of current Digital Libraries using formal narratives. Narratives are intended as sequences of events. We present the results of an initial experiment to detect and extract implicit events from the Wikidata knowledge base in order to construct a narrative in a semi-automatic way. Wikidata contains many historical entities, but comparably few events. The reason is that most events in Wikidata are represented in an implicit way, e.g. by listing a date of birth instead of having an event of type “birth”. For this reason, we decided to generate what we call the Wikidata Event Graph (WEG), i.e. the graph of implicit events found in Wikidata. We performed an initial experiment taking as case study the narrative of the life of Italian poet Dante Alighieri. Only one event of the life of Dante is explicitly represented in Wikidata as instance of the class Q1190554 Occurrence. Using the WEG, we were able to automatically detect 31 more events of Dante’s life that were present in Wikidata in an implicit way.
Incast Mitigation in a Data Center Storage Cluster Through a Dynamic Fair-Share Buffer Policy

Incast is a phenomenon when multiple devices interact with only one device at a given time. Multiple storage senders overflow either the switch buffer or the single-receiver memory. This pattern causes all concurrent-senders to stop and wait for buffer/memory availability, and leads to a packet loss and retransmission-resulting in a huge latency. We present a software-defined technique tackling the many-to-one communication pattern-Incast-in a data center storage cluster. Our proposed method decouples the default TCP windowing mechanism from all storage servers, and delegates it to the software-defined storage controller. The proposed method removes the TCP saw-tooth behavior, provides a global flow awareness, and implements the dynamic fair-share buffer policy for end-to-end I/O path. It considers all I/O stages (applications, device drivers, NICs, switches/routers, file systems, I/O schedulers, main memory, and physical disks) while achieving the maximum I/O throughput. The policy, which is part of the proposed method, allocates fair-share bandwidth utilization for all storage servers. Priority queues are incorporated to handle the most important data flows. In addition, the proposed method provides better manageability and maintainability compared with traditional storage networks, where data plane and control plane reside in the same device.
Estimation of parameters in circuit QED by continuous quantum measurement

Designing high-precision and efficient protocols is of crucial importance for quantum parameter estimation in practice. Estimation based on continuous quantum measurement is one possible type of this, which also appears to be the most natural choice for continuous dynamical processes. In this work we consider the state-of-the-art superconducting circuit quantum-electrodynamics (QED) systems, where high-quality continuous measurements have been extensively performed in the past decade. Within the framework of Bayesian estimation and particularly using the quantum Bayesian rule in circuit QED, we numerically simulate the likelihood function as an estimator for the Rabi frequency of qubit oscillations. We find that, by proper design of the interaction strength of measurement, the estimate precision can scale with the measurement time beyond the standard quantum limit, which is usually assumed for this type of continuous measurement. This unexpected result is supported by the simulated Fisher information and can be understood as a consequence of the quantum correlation between the output signals by simulating the effect of quantum efficiency of measurement.
Uncertainty of Measurement and Reliability of the Decision Making on Compliance

It is shown that the probability of making the right decision on the compliance of the object with the norms depends on the measurement uncertainty associated with the result of the measurement control of the object parameters and the length of the tolerance interval. The decisive rule on the suitability (unfitness) of the object of control should take into account both the possible uncertainty of the result and the requirements for the cost and complexity of the control procedure, as well as to the specified reliability. To improve the reliability of this procedure, a control method based on a sequential adaptive decision-making procedure is proposed, which takes into account the relationship of the parameters of the distribution law of the possible values of the controlled quantity, random effects in the measurement and the length of the tolerance interval. The decision-making procedure assumes consistently, depending on the result at the current stage, to introduce additional tolerance intervals, the length of which is determined by the parameters of the distribution function of random variables accompanying the measurements of the controlled parameters. The result of measurement of the controlled parameter is compared with these intervals, and decisions on continuation or completion of control with introduction of additional limits are made. This provides a reduction in the probability of erroneous decisions at each additional stage of control, since the time of the initial hit of the controlled value in a consistently calculated controlled interval is determined step by step. The use of a sequential adaptive procedure provides a given reliability of control and does not allow to carry out the control procedure in full for all possible values of the controlled value.
Hierarchical Word Mover Distance for Collaboration Recommender System

Natural Language Processing (NLP) techniques have enabled automated analysis over a large collection of documents, which makes it possible to quantitatively compare researcher profiles based on their publications. This paper proposes a novel researcher similarity measuring system which combines a variety of techniques, including topic modelling, Word2vec and word mover distance calculations on publication abstracts. The proposed method, implemented in python, matches researchers based upon a document’s texts by evaluating the semantic meanings of words and topics. The distances between researchers are calculated over various text features in an hierarchical structure. Results show that the system is successful in identifying existing co-authorships from sample data despite co-authorship properties having been removed, as well as suggesting valid potential academic collaboration links from related research areas irrespective of previous collaboration activity.
Lexicon-based Detection of Violence on Social Media

This study adopts a lexicon-based approach to address violence on social media. It uses FrameNet 1.7 ( fn ) and WordNet 3.1 ( wn ) to build a hierarchical domain-specific language resource of violence. The proposed lexicon tethers fn ’s innovative integration of linguistic and paralinguistic knowledge to wn ’s hierarchically-organized database. This tether alleviates the need to gather all paralinguistic violence-associated scenes and organize their linguistic realizations hierarchically. The proposed methodology can be internationally applied, given the multilingual availability of fn and wn , to cognitively and quantitatively explore a concept or a phenomenon. The lexicon is applied, then, to a corpus representing posts and comments retrieved from Donald Trump’s Facebook public page. Results reveal that the proposed lexicon recalls 92.68 of the total violence-related words in the corpus with a 76.31 precision (F-score= 83.7). More important, relating wn to fn inspires the creation of new frames, suggests slight modifications to existing ones and advocates promising mapping between some frames and synsets.
Considerations for Face-based Data Estimates: Affect Reactions to Videos

Video streaming is becoming the new standard for watching videos, providing an opportunity for affective video recommendation that leverages noninvasive sensing data from viewers to suggest content. Face-based data has the distinct advantage that it can be collected noninvasively with minimal equipment such as a simple webcam. Face recordings can be used for estimating individuals’ emotional states based on their facial movements and also for estimating pulse as a signal for emotional reactions. We provide a focused case-based contribution by reporting on methodological challenges experienced in a research study with face-based data estimates which are then used in predicting affective reactions. We build on lessons learned to formulate a set of recommendations that can be useful for continued work towards affective video recommendation.
Grapheme Approach to Recognizing Letters based on Medial Representation

In this paper we propose a new concept of mathematical model of characters’ grapheme which nowadays is not strictly formalized and a method of constructing graphemes based on the continuous medial representation of letters in digital images. We also suggest the recognition method of the printed text image on the basis of mathematical model of the grapheme used at generation of features and for classifier construction. The results of experiments confirming the efficiency of the grapheme approach, high quality of text recognition in different font variants and in different qualities of the text image are presented.