Reviewerly/exHarmony huggingface dataset:
Speeding up Generalized PSR Parsers by Memoization Techniques

Predictive shift-reduce (PSR) parsing for hyperedge replacement (HR) grammars is very efficient, but restricted to a subclass of unambiguous HR grammars.To overcome this restriction, we have recently extended PSR parsing to generalized PSR (GPSR) parsing along the lines of Tomita-style generalized LR parsing.Unfortunately, GPSR parsers turned out to be too inefficient without manual tuning.This paper proposes to use memoization techniques to speed up GPSR parsers without any need of manual tuning, and which has been realized within the graph parser distiller GRAPPA.We present running time measurements for some example languages; they show a significant speed up by some orders of magnitude when parsing valid graphs.But memoization techniques do not help when parsing invalid graphs or if all parses of an ambiguous input graph shall be determined.
Quantifying quantum non-Markovianity based on quantum coherence via skew information

Based on the nonincreasing property of quantum coherence via skew information under incoherent completely positive and trace-preserving maps, we propose a non-Markovianity measure for open quantum processes. As applications, by applying the proposed measure to some typical noisy channels, we find that it is equivalent to the three previous measures of non-Markovianity for phase damping and amplitude damping channels, i.e., the measures based on the quantum trace distance, dynamical divisibility, and quantum mutual information. For the random unitary channel, it is equivalent to the non-Markovianity measure based on $l_1$ norm of coherence for a class of output states and it is incompletely equivalent to the measure based on dynamical divisibility. We also use the modified Tsallis relative $\alpha$ entropy of coherence to detect the non-Markovianity of dynamics of quantum open systems, the results show that the modified Tsallis relative $\alpha$ entropy of coherence are more comfortable than the original Tsallis relative $\alpha$ entropy of coherence for small $\alpha$.
Epistemic Logic Programs: A Different World View

Epistemic Logic Programs (ELPs), an extension of Answer Set Programming (ASP) with epistemic operators, have received renewed attention from the research community in recent years.Classically, evaluating an ELP yields a set of world views, with each being a set of answer sets.In this paper, we propose an alternative definition of world views that represents them as three-valued assignments, where each atom can be either always true, always false, or neither.Based on several examples, we show that this definition is natural and intuitive.We also investigate relevant computational properties of these new semantics, and explore how other notions, like strong equivalence, are affected.
Robust and Automatic Skyline Detection Algorithm Based on MSSDN

Automatic detection of the skyline plays an important role in several applications, such as visual geo-localization, flight control, port security, and mountain peak recognition. Existing skyline detection methods are mostly used under common weather conditions; however, they do not consider bad weather situations, such as rain, which limits their application in real scenes. In this paper, we propose a multi-stream-stage DenseNet to detect skyline automatically under different weather conditions. This model fully considers the adverse factors influencing the skyline and outputs a probability graph of the skyline. Finally, a dynamic programming algorithm is implemented to detect the skyline in images accurately. A comparison with the existing state-of-the-art methods proves that the proposed model shows a good performance under rainy or common weather conditions and exhibits the best detection precision for the public database.
Theory for frequent measurements of spontaneous emissions in a non-Markovian environment: Beyond the Lorentzian spectrum

The measurement-result-conditioned evolution of a system (e.g., an atom) with spontaneous emissions of photons is described by the quantum trajectory (QT) theory. In this work we generalize the associated QT theory from an infinitely wide bandwidth Markovian environment to the finite bandwidth non-Markovian environment. In particular, we generalize the treatment for an arbitrary spectrum, which is not restricted by the specific Lorentzian case. We rigorously prove the general existence of a perfect scaling behavior jointly defined by the bandwidth of the environment and the time interval between successive photon detections. For a couple of examples, we obtain analytic results to facilitate the QT simulations based on the Monte-Carlo algorithm. For the case where the analytical result is not available, a numerical scheme is proposed for practical simulations.
Generic Entanglement Entropy for Quantum States with Symmetry

When a quantum pure state is drawn uniformly at random from a Hilbert space, the state is typically highly entangled. This property of a random state is known as generic entanglement of quantum states and has been long investigated from many perspectives, ranging from the black hole science to quantum information science. In this paper, we address the question of how symmetry of quantum states changes the properties of generic entanglement. More specifically, we study bipartite entanglement entropy of a quantum state that is drawn uniformly at random from an invariant subspace of a given symmetry. We first extend the well-known concentration formula to the one applicable to any subspace and then show that 1. quantum states in the subspaces associated with an axial symmetry are still highly entangled, though it is less than that of the quantum states without symmetry, 2. quantum states associated with the permutation symmetry are significantly less entangled, and 3. quantum states with translation symmetry are as entangled as the generic one. We also numerically investigate the phase-transition behavior of the distribution of generic entanglement, which indicates that the phase transition seems to still exist even when random states have symmetry.
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning.

We present a multi-agent actor-critic method that aims to implicitly address the credit assignment problem under fully cooperative settings. Our key motivation is that credit assignment among agents may not require an explicit formulation as long as (1) the policy gradients derived from a centralized critic carry sufficient information for the decentralized agents to maximize their joint action value through optimal cooperation and (2) a sustained level of exploration is enforced throughout training. Under the centralized training with decentralized execution (CTDE) paradigm, we achieve the former by formulating the centralized critic as a hypernetwork such that a latent state representation is integrated into the policy gradients through its multiplicative association with the stochastic policies; to achieve the latter, we derive a simple technique called adaptive entropy regularization where magnitudes of the entropy gradients are dynamically rescaled based on the current policy stochasticity to encourage consistent levels of exploration. Our algorithm, referred to as LICA, is evaluated on several benchmarks including the multi-agent particle environments and a set of challenging StarCraft II micromanagement tasks, and we show that LICA significantly outperforms previous methods.
Variational Quantum Fidelity Estimation

Computing quantum state fidelity will be important to verify and characterize states prepared on a quantum computer. In this work, we propose novel lower and upper bounds for the fidelity F(ρ,σ) based on the “truncated fidelity'” F(ρ_m,σ) which is evaluated for a state ρ_m obtained by projecting ρ onto its mm-largest eigenvalues. Our bounds can be refined, i.e., they tighten monotonically with mm. To compute our bounds, we introduce a hybrid quantum-classical algorithm, called Variational Quantum Fidelity Estimation, that involves three steps: (1) variationally diagonalize ρ, (2) compute matrix elements of σ in the eigenbasis of ρ, and (3) combine these matrix elements to compute our bounds. Our algorithm is aimed at the case where σ is arbitrary and ρ is low rank, which we call low-rank fidelity estimation, and we prove that no classical algorithm can efficiently solve this problem under reasonable assumptions. Finally, we demonstrate that our bounds can detect quantum phase transitions and are often tighter than previously known computable bounds for realistic situations.
Local cyclicity of isogeny classes of abelian varieties defined over finite fields

For a prime number ℓ, an isogeny class A of abelian varieties is called ℓ-cyclic if every variety in A have a cyclic ℓ-part of its group of rational points. More generally, for a finite set of prime numbers S, A is said to be S-cyclic if it is ℓ-cyclic for every ℓ∈S. We give lower and upper bounds on the fraction of S-cyclic g-dimensional isogeny classes of abelian varieties defined over the finite field Fq, when q tends to infinity.
Cancer Registry Information Extraction via Transfer Learning

A cancer registry is a critical and massive database for which various types of domain knowledge are needed and whose maintenance requires labor-intensive data curation.In order to facilitate the curation process for building a high-quality and integrated cancer registry database, we compiled a cross-hospital corpus and applied neural network methods to develop a natural language processing system for extracting cancer registry variables buried in unstructured pathology reports.The performance of the developed networks was compared with various baselines using standard micro-precision, recall and Fmeasure.Furthermore, we conducted experiments to study the feasibility of applying transfer learning to rapidly * Corresponding authors develop a well-performing system for processing reports from different sources that might be presented in different writing styles and formats.The results demonstrate that the transfer learning method enables us to develop a satisfactory system for a new hospital with only a few annotations and suggest more opportunities to reduce the burden of cancer registry curation.
Quantum error correction and one-way LOCC state distinguishability

We explore the intersection of studies in quantum error correction and quantum local operations and classical communication (LOCC). We consider one-way LOCC measurement protocols as quantum channels and investigate their error correction properties, emphasizing an operator theory approach to the subject, and we obtain new applications to one-way LOCC state distinguishability as well as new derivations of some established results. We also derive conditions on when states that arise through the stabilizer formalism for quantum error correction are distinguishable under one-way LOCC.
Absolute phase retrieval for colored objects based on three phase-shifting amount codes

We propose an absolute phase retrieval method based on three phase-shifting amount codes (3-PSA-codes) to measure the colored object with one additional pattern. 3-PSA-codes adopt the coding concept of 3-digit-codes, in which the code elements of three consecutive periods are treated as a unique code word for one period. However, to measure the colored object more effectively in the proposed method, each code element is embedded into the PSA domain and retrieved from the phase difference. Fringe patterns for the wrapped phase are artfully employed in the code element retrieval. Hence, for the first time, to the best of our knowledge, the code element related to the phase can be determined by one additional pattern. It breaks the constraint that temporal methods require multiple additional patterns to overcome the adverse effect of the surface color of objects on absolute phase retrieval. Experimental results demonstrate that the proposed 3-PSA-codes have strong robustness in the measurement of the colored object.
Learning from Both Experts and Data

In this work, we study the problem of inferring a discrete probability distribution using both expert knowledge and empirical data. This is an important issue for many applications where the scarcity of data prevents a purely empirical approach. In this context, it is common to rely first on an a priori from initial domain knowledge before proceeding to an online data acquisition. We are particularly interested in the intermediate regime, where we do not have enough data to do without the initial a priori of the experts, but enough to correct it if necessary. We present here a novel way to tackle this issue, with a method providing an objective way to choose the weight to be given to experts compared to data. We show, both empirically and theoretically, that our proposed estimator is always more efficient than the best of the two models (expert or data) within a constant.
Neural Non-Rigid Tracking

We introduce a novel, end-to-end learnable, differentiable non-rigid tracker that enables state-of-the-art non-rigid reconstruction by a learned robust optimization. Given two input RGB-D frames of a non-rigidly moving object, we employ a convolutional neural network to predict dense correspondences and their confidences. These correspondences are used as constraints in an as-rigid-as-possible (ARAP) optimization problem. By enabling gradient back-propagation through the weighted non-linear least squares solver, we are able to learn correspondences and confidences in an end-to-end manner such that they are optimal for the task of non-rigid tracking. Under this formulation, correspondence confidences can be learned via self-supervision, informing a learned robust optimization, where outliers and wrong correspondences are automatically down-weighted to enable effective tracking. Compared to state-of-the-art approaches, our algorithm shows improved reconstruction performance, while simultaneously achieving 85 times faster correspondence prediction than comparable deep-learning based methods. We make our code available.
Construction of a Software Development Model for Managing Final Year Projects in Information Technology Programmes

The final year project (FYP) is considered a capstone course in information technology (IT) programmes and involves the development of a software product. Currently, students are using the traditional software development life cycle approach to manage their FYPs. However, this approach can cause many difficulties. This paper proposes an alternative software development model for managing the FYP in an IT and IT-related degree programmes of study. First, a benchmarking exercise was undertaken to compare the software developed for business purposes with that developed for educational purposes, which took into account the ten project management knowledge areas. The result of this exercise indicated that the differences that exist between business and educational software necessitate the development of a new software development model that is specifically tailored to the development of educational software. Therefore, capability maturity model integration (CMMI) was modified to generate a new version of CMMI – named educational CMMI – which could be used to evaluate educational software projects and detailed mathematical descriptions of the proposed model were composed. As conclusions, the proposed model was then assessed by students’ results and by questionnaire feedback, the results of which showed that the proposed model was both useful and applicable for its intended target users and context.
Short Paper: Stress-SGX: Load and Stress Your Enclaves for Fun and Profit

The latest generation of Intel processors supports Software Guard Extensions (SGX), a set of instructions that implements a Trusted Execution Environment (TEE) right inside the CPU, by means of so-called enclaves. This paper presents Stress-SGX, an easy-to-use stress-test tool to evaluate the performance of SGX-enabled nodes. We build on top of the popular Stress-ng tool, while only keeping the workload injectors (stressors) that are meaningful in the SGX context. We report on several insights and lessons learned about porting legacy code to run inside an SGX enclave, as well as the limitations introduced by this process. Finally, we use Stress-SGX to conduct a study comparing the performance of different SGX-enabled machines.
Ranking Significant Discrepancies in Clinical Reports

Medical errors are a major public health concern and a leading cause of death worldwide. Many healthcare centers and hospitals use reporting systems where medical practitioners write a preliminary medical report and the report is later reviewed, revised, and finalized by a more experienced physician. The revisions range from stylistic to corrections of critical errors or misinterpretations of the case. Due to the large quantity of reports written daily, it is often difficult to manually and thoroughly review all the finalized reports to find such errors and learn from them. To address this challenge, we propose a novel ranking approach, consisting of textual and ontological overlaps between the preliminary and final versions of reports. The approach learns to rank the reports based on the degree of discrepancy between the versions. This allows medical practitioners to easily identify and learn from the reports in which their interpretation most substantially differed from that of the attending physician (who finalized the report). This is a crucial step towards uncovering potential errors and helping medical practitioners to learn from such errors, thus improving patient-care in the long run. We evaluate our model on a dataset of radiology reports and show that our approach outperforms both previously-proposed approaches and more recent language models by 4.5% to 15.4%.
Fast coefficient computation for algebraic power series in positive characteristic

We revisit Christol's theorem on algebraic power series in positive characteristic and propose yet another proof for it. This new proof combines several ingredients and advantages of existing proofs, which make it very well-suited for algorithmic purposes. We apply the construction used in the new proof to the design of a new efficient algorithm for computing the $N$th coefficient of a given algebraic power series over a perfect field of characteristic~$p$. It has several nice features: it is more general, more natural and more efficient than previous algorithms. Not only the arithmetic complexity of the new algorithm is linear in $\log N$ and quasi-linear in~$p$, but its dependency with respect to the degree of the input is much smaller than in the previously best algorithm. {Moreover, when the ground field is finite, the new approach yields an even faster algorithm, whose bit complexity is linear in $\log N$ and quasi-linear in~$\sqrt{p}$}.
Rescaling Interactions for Quantum Control

A powerful control method in experimental quantum computing is the use of spin echoes, employed to select a desired term in the system's internal Hamiltonian, while refocusing others. Here, we address a more general problem, describing a method to not only turn on and off particular interactions but also to rescale their strengths so that we can generate any desired effective internal Hamiltonian. We propose an algorithm based on linear programming for achieving time-optimal rescaling solutions in fully coupled systems of tens of qubits, which can be modified to obtain near-time-optimal solutions for rescaling systems with hundreds of qubits.
EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention Fusion

Panoptic segmentation is a scene parsing task which unifies semantic segmentation and instance segmentation into one single task. However, the current state-of-the-art studies did not take too much concern on inference time. In this work, we propose an Efficient Panoptic Segmentation Network (EPSNet) to tackle the panoptic segmentation tasks with fast inference speed. Basically, EPSNet generates masks based on simple linear combination of prototype masks and mask coefficients. The light-weight network branches for instance segmentation and semantic segmentation only need to predict mask coefficients and produce masks with the shared prototypes predicted by prototype network branch. Furthermore, to enhance the quality of shared prototypes, we adopt a module called "cross-layer attention fusion module", which aggregates the multi-scale features with attention mechanism helping them capture the long-range dependencies between each other. To validate the proposed work, we have conducted various experiments on the challenging COCO panoptic dataset, which achieve highly promising performance with significantly faster inference speed (53ms on GPU).
Training conformal predictors.

Efficiency criteria for conformal prediction, such as \emph{observed fuzziness} (i.e., the sum of p-values associated with false labels), are commonly used to \emph{evaluate} the performance of given conformal predictors. Here, we investigate whether it is possible to exploit efficiency criteria to \emph{learn} classifiers, both conformal predictors and point classifiers, by using such criteria as training objective functions. The proposed idea is implemented for the problem of binary classification of hand-written digits. By choosing a 1-dimensional model class (with one real-valued free parameter), we can solve the optimization problems through an (approximate) exhaustive search over (a discrete version of) the parameter space. Our empirical results suggest that conformal predictors trained by minimizing their observed fuzziness perform better than conformal predictors trained in the traditional way by minimizing the \emph{prediction error} of the corresponding point classifier. They also have a reasonable performance in terms of their prediction error on the test set.
Cybercultural Transgressions in Comments by Iranian Instagram Users

Abstract The ease of communication and anonymity that computer-mediated communication provides has created fertile ground for transgressive (socially non-normative) online behaviours. The purpose of the current research is to explore the types of transgressive comments on Iranian celebrities’ Instagram pages. Within a qualitative descriptive approach, the current research employs Braun and Clarke’s ‘thematic analysis’ to analyze transgressive user comments. Using a judgmental (purposive) non-probability sampling technique, we selected posts from 22 Iranian celebrities’ Instagram pages. Afterwards, we analyzed 53,066 comments in these posts and extracted four dominant transgressive themes: ‘unwanted advertisement,’ ‘partner seeking,’ ‘attention seeking,’ and ‘emotional release’; we then extracted their sub-themes. Transgressive behaviours disrupt the communicative experience on social networks, endanger users’ security and safety, and can lead to mental problems and even offline harassment and violence. Recognizing these behaviours and their patterns can contribute to more effective use of social control means.