Reviewerly/exHarmony huggingface dataset:
Sampling of Planar Curves: Theory and Fast Algorithms

We introduce a continuous domain framework for the recovery of a planar curve from a few samples. We model the curve as the zero level set of a trigonometric polynomial. We show that the exponential feature maps of the points on the curve lie on a low-dimensional subspace. We show that the null-space vector of the feature matrix can be used to uniquely identify the curve, given a sufficient number of samples. The worst-case theoretical guarantees show that the number of samples required for unique recovery depends on the bandwidth of the underlying trigonometric polynomial, which is a measure of the complexity of the curve. We introduce an iterative algorithm that relies on the low-rank property of the feature maps to recover the curves when the samples are noisy or when the true bandwidth of the curve is unknown. We also demonstrate the preliminary utility of the proposed curve representation in the context of image segmentation.
Dynamic type inference for gradual Hindley–Milner typing

Garcia and Cimini study a type inference problem for the ITGL, an implicitly and gradually typed language with let-polymorphism, and develop a sound and complete inference algorithm for it. Soundness and completeness mean that, if the algorithm succeeds, the input term can be translated to a well-typed term of an explicitly typed blame calculus by cast insertion and vice versa. However, in general, there are many possible translations depending on how type variables that were left undecided by static type inference are instantiated with concrete static types. Worse, the translated terms may behave differently—some evaluate to values but others raise blame. In this paper, we propose and formalize a new blame calculus λ B DTI that avoids such divergence as an intermediate language for the ITGL. A main idea is to allow a term to contain type variables (that have not been instantiated during static type inference) and defer instantiation of these type variables to run time. We introduce dynamic type inference (DTI) into the semantics of λ B DTI so that type variables are instantiated along reduction. The DTI-based semantics not only avoids the divergence described above but also is sound and complete with respect to the semantics of fully instantiated terms in the following sense: if the evaluation of a term succeeds (i.e., terminates with a value) in the DTI-based semantics, then there is a fully instantiated version of the term that also succeeds in the explicitly typed blame calculus and vice versa. Finally, we prove the gradual guarantee, which is an important correctness criterion of a gradually typed language, for the ITGL.
An efficient cloud scheduler design supporting preemptible instances

Maximizing resource utilization by performing an efficient resource provisioning is a key factor for any cloud provider: commercial actors can maximize their revenues, whereas scientific and non-commercial providers can maximize their infrastructure utilization. Traditionally, batch systems have allowed data centers to fill their resources as much as possible by using backfilling and similar techniques. However, in an IaaS cloud, where virtual machines are supposed to live indefinitely, or at least as long as the user is able to pay for them, these policies are not easily implementable. In this work we present a new scheduling algorithm for IaaS providers that is able to support preemptible instances, that can be stopped by higher priority requests without introducing large modifications in the current cloud schedulers. This scheduler enables the implementation of new cloud usage and payment models that allow more efficient usage of the resources and potential new revenue sources for commercial providers. We also study the correctness and the performace overhead of the proposed scheduler agains existing solutions.
Initial Centroids for K-Means Using Nearest Neighbors and Feature Means

K-means is a popularly used clustering algorithm. Results of k-means clustering algorithm are sensitive to initial centroids chosen that give different clustering results for different runs. The algorithm converges to local optima based on the initial centroids chosen and does not guarantee reaching the global optima. This paper proposes an algorithm for choosing the initial centroids where each initial centroid is determined using the feature means and eliminating its nearest neighbors for choosing the next centroid.
Revisiting AES SBox Composite Field Implementations for FPGAs

Composite fields are used for implementing the advanced encryption standard (AES) SBox when compact and side-channel resistant constructions are required. The prior art has investigated efficient implementations of such SBoxes for application specific integrated circuit (ASIC) platforms. On field programmable gate arrays (FPGAs); however, due to the considerably different structure compared with ASICs, these implementations perform poorly. In this letter, we revisit composite field AES SBox implementations for FPGAs. We show how design choices and optimizations can be made to better suit the granular look-up tables that are present in modern FPGAs. We investigate 2880 SBox constructions and show that about half of them are better than the state-of-the-art composite field implementation. Our best SBox implementation is 18% smaller compared with the state-of-the-art implementation on an FPGA.
Quantum networks for single photon detection

Single photon detection generally consists of several stages: the photon has to interact with one or more charged particles, its excitation energy will be converted into other forms of energy, and amplification to a macroscopic signal must occur, thus leading to a ``click.'' We focus here on the part of the detection process before amplification (which we have studied in a separate publication). We discuss how networks consisting of coupled discrete quantum states and structured continua (e.g., band gaps) provide generic models for that first part of the detection process. The input to the network is a single continuum (the continuum of single photon states); the output is again a single continuum describing the next irreversible step. The process of a single photon entering the network, its energy propagating through that network, and finally exiting into another output continuum of modes can be described by a single dimensionless complex transmission amplitude, $T(\ensuremath{\omega})$. We discuss how to obtain from $T(\ensuremath{\omega})$ the photodetection efficiency, how to find sets of parameters that maximize this efficiency, as well as expressions for other input-independent quantities such as the frequency-dependent group delay and spectral bandwidth. We then study a variety of networks, discuss how to engineer different transmission functions $T(\ensuremath{\omega})$ amenable to photodetection, and discuss implications for single photon detection technology.
Research on Quickpass Payment Terminal Application System Based on dynamic QR Code

A dynamic QR code quickpass terminal application system is designed in this paper, the system is a convenient receiving application system for QR code scanning and being scanned, the dynamic QR code (each bill a single code) can be generated and displayed based on the network and the real-time order information, replacing the commonly used forged static QR code patches, so as to improve the previous payment security environment and greatly reduce unnecessary losses for merchants and operators.
Iterative minimal residual method provides optimal regularization parameter for extreme learning machines

Extreme Learning Machine (ELM) is a single hidden layer feed-forward neural network with the learning speed is much faster than the traditional neural network architecture. The main reason is attributed to the application of slow gradient-based algorithms, and the model parameter of the networks is estimated iteratively and hence, ELM has been widely used in real-life applications due to its fast and easy way of learning. The ill-conditioning of the input-to–hidden layer matrix reduces the stability of ELM and the regularization is employed to overcome this problem. In general, Tikhonov (ℓ2-norm based) and Lasso (ℓ1-norm based) techniques are widely used regularization techniques applied in the current work. The choice of the regularization parameter affects the performance of either of the regularized ELM. The singular value decomposition based generalized cross-validation method is also used in the literature for automatically estimating the value of the regularization parameter. This method increases the computational complexity, as it involves the repeated inversion of a large matrix. In the proposed methods, the regularized Minimum Residual Method (MRM) is adopted along with the Golden-section line search to find the optimal value of the regularization parameter. One of the important contributions of the proposed work is to suitably modify MRM to estimate the regularization parameter for the Lasso technique. MT-ELM is a ℓ2-norm regularization based ELM method where the regularization parameter is estimated using MRM. Similarly, ML-ELM is a ℓ1-norm regularization based ELM method where the regularization parameter is estimated using a suitable MRM. In order to evaluate the performance of the proposed algorithms, experiments are carried out on several benchmark classification data sets and are compared on several performance metrics such as accuracy, precision, recall, and F1-score. Through the extensive numerical results and floating point operation counts, it is shown that the proposed MRM-based methods are capable of estimating the regularization parameter optimally compare to the existing ones. It is also observed that the proposed methods improve the accuracy measure by seven per cent compared to the conventional ELM, thus enhancing the capability of ELM. Further, the proposed methods are verified statistically.
Microscopic modeling of general time-dependent quantum Markov processes

Master equations are typically adopted to describe the dynamics of open quantum systems. Such equations are either in integro-differential or in time-local form, with the latter class more frequently adopted due to the simpler numerical methods developed to obtain the corresponding solution. Here we show that any time-local master equation with positive rates in the generator, i.e., any completely positive divisible quantum process, admits a microscopic model whose reduced dynamics is well described by the given equation.
Applying UTAUT Model for an Acceptance Study Alluding the Use of Augmented Reality in Archaeological Sites

Looking forward to enhance visitors’ experience among cultural heritage sites, the use of new technologies within these spaces has seen a fast growth among the last decades. Regarding the increasing technological developments, the importance of understanding the acceptance of technology and the intention to use it in cultural heritage sites, also arises. The existing variety of acceptance models found in literature relatively to the use of technology, and the uncertainty about selecting a suitable model, sparked this research. Accordingly, the current study aims to select, evaluate and analyse an acceptance model, targeted to understand the behavioural intention to use augmented reality technology in archaeological sites. The findings of this research revealed UTAUT as a suitable model. However, regarding the collected data, some moderators’ impact presented in the original study may change significantly. In addition, more constructs can be considered for wider understandings.
Tight upper bound for the maximal expectation value of the Mermin operators

The violation of the Mermin inequality (MI) for multipartite quantum states guarantees the existence of nonlocality between either few or all parties. The detection of optimal MI violation is fundamentally important, but current methods only involve numerical optimizations, thus hard to find even for three-qubit states. In this paper, we provide a simple and elegant analytical method to achieve the upper bound of Mermin operator for arbitrary three-qubit states. Also, the necessary and sufficient conditions for the tightness of the bound for some class of tri-partite states has been stated. Finally, we suggest an extension of this result for up to n qubits.
Learning semantic sentence representations from visually grounded language without lexical knowledge

Abstract Current approaches to learning semantic representations of sentences often use prior word-level knowledge. The current study aims to leverage visual information in order to capture sentence level semantics without the need for word embeddings. We use a multimodal sentence encoder trained on a corpus of images with matching text captions to produce visually grounded sentence embeddings. Deep Neural Networks are trained to map the two modalities to a common embedding space such that for an image the corresponding caption can be retrieved and vice versa. We show that our model achieves results comparable to the current state of the art on two popular image-caption retrieval benchmark datasets: Microsoft Common Objects in Context (MSCOCO) and Flickr8k. We evaluate the semantic content of the resulting sentence embeddings using the data from the Semantic Textual Similarity (STS) benchmark task and show that the multimodal embeddings correlate well with human semantic similarity judgements. The system achieves state-of-the-art results on several of these benchmarks, which shows that a system trained solely on multimodal data, without assuming any word representations, is able to capture sentence level semantics. Importantly, this result shows that we do not need prior knowledge of lexical level semantics in order to model sentence level semantics. These findings demonstrate the importance of visual information in semantics.
Resource Management in a Time of Fiscal Scarcity: Combining Qualitative and Quantitative Assessment for Journal Package Cancellations

As a result of continual resource inflation and a decreasing budget, Kansas State University Libraries were required to conduct a large-scale electronic journal cancellation project. The current organizational model does not require librarian subject specialists to perform comprehensive collection development duties; therefore, content development librarians developed a methodology of collecting quantitative and qualitative statistics to collaboratively evaluate journals. This article will demonstrate the methodology of assessment, and serve as a working model for libraries operating under circumstances of labor shortages, budget cuts, and leadership restructuring.
Periods on the Cascade Connection of an LFSR and an NFSR

We study the periods of sequences produced by the cascade connection of two Feedback shift registers (FSRs). The period of the cascade connection is the period of the longest sequences it produces. An upper bound for the period of the cascade connection of a Nonlinear feedback shift register (NFSR) into a Linear feedback shift register (LFSR) is established. In addition, the cascade connection of an n-stage maximum-length LFSR into an n-stage NFSR is called an (n + n)-stage Grain-like NFSR, and we propose two families of (n + n)stage Grain-like NFSRs such that the minimal period 2n - 1 is achievable for a positive integer n.
Deterministic secure quantum communication with and without entanglement

We present a protocol for sending a message over a quantum channel with different layers of security that will prevent an eavesdropper from deciphering the message without being detected. The protocol has two versions where the bits are encoded in either pairs of entangled photons or separate photons. Unlike many other protocols, it requires a one-way, rather than a two-way, quantum channel and does not require a quantum memor. A quantum key is used to encrypt the message and both the key and the message are sent over the quantum channle with the same quantum encoding technique. The key is sent only if no eavesdropper is detected.
Self and Body Part Localization in Virtual Reality: Comparing a Headset and a Large-Screen Immersive Display

It is currently not fully understood where people precisely locate themselves in their bodies, particularly in virtual reality. To investigate this, we asked participants to point directly at themselves and to several of their body parts with a virtual pointer, in two virtual reality (VR) setups, a VR headset and a large-screen immersive display (LSID). There was a difference in distance error in pointing to body parts depending on VR setup. Participants pointed relatively accurately to many of their body parts (i.e. eyes, nose, chin, shoulders and waist). However, in both VR setups when pointing to the feet and the knees they pointed too low, and for the top of the head too high (to larger extents in the VR headset). Taking these distortions into account, the locations found for pointing to self were considered in terms of perceived bodies, based on where the participants had pointed to their body parts in the two VR setups. Pointing to self in terms of the perceived body was mostly to the face, the upper followed by the lower, as well as some to the torso regions. There was no significant overall effect of VR condition for pointing to self in terms of the perceived body (but there was a significant effect of VR if only the physical body (as measured) was considered). In a paper-and-pencil task outside of VR, performed by pointing on a picture of a simple body outline (body template task), participants pointed most to the upper torso. Possible explanations for the differences between pointing to self in the VR setups and the body template task are discussed. The main finding of this study is that the VR setup influences where people point to their body parts, but not to themselves, when perceived and not physical body parts are considered.
An Improved Vibe Based on Gaussian Pyramid

In view of the ghost and time cost of the Vibe, an improved Vibe algorithm based on Difference of Gaussian (DoG) is proposed in this paper. Firstly, video frames are processed by Gauss Pyramid, the original layer, the first layer and the second layer of three images are obtained. Secondly, three-frame difference is performed and fused, then rough moving target is yielded. Thirdly, the target pixel is judged based on the improved vibe algorithm and the complete moving target is achieved. The improvement on the traditional Vibe algorithm is to enlarge the range of sample pixels selection, avoiding repeated selection of error samples. On the other hand, the background of DoG images is updated and moving objects is segmented. Finally, the result is fused by three-frame difference and the improved Vibe algorithm. The experiment results show that ghost phenomenon can be restrained efficiently by the proposed algorithm and the calculation speed is also improved. The feature of real time can also be ensured.
On the tensor rank of multiplication in finite extensions of finite fields and related issues in algebraic geometry

Abstract In this paper, we give a survey of the known results concerning the tensor rank of multiplication in finite extensions of finite fields, enriched with some unpublished recent results, and we analyze these to enhance the qualitative understanding of the research area. In particular, we identify and clarify certain partially proved results and emphasise links with open problems in number theory, algebraic geometry, and coding theory. Bibliography: 92 titles.
Gradual session types

Abstract Session types are a rich type discipline, based on linear types, that lifts the sort of safety claims that come with type systems to communications. However, web-based applications and microservices are often written in a mix of languages, with type disciplines in a spectrum between static and dynamic typing. Gradual session types address this mixed setting by providing a framework which grants seamless transition between statically typed handling of sessions and any required degree of dynamic typing. We propose Gradual GV as a gradually typed extension of the functional session type system GV. Following a standard framework of gradual typing, Gradual GV consists of an external language, which relaxes the type system of GV using dynamic types; an internal language with casts, for which operational semantics is given; and a cast-insertion translation from the former to the latter. We demonstrate type and communication safety as well as blame safety, thus extending previous results to functional languages with session-based communication. The interplay of linearity and dynamic types requires a novel approach to specifying the dynamics of the language.
Featured Snippets Results in Google Web Search: An Exploratory Study

In this paper authors analyzed 163412 keywords and results with featured snippets collected from localized Polish Google search engine. A method-ology for retrieving data from Google search engine was proposed in terms of obtaining necessary data to study featured snippets. It was observed that almost half of featured snippets (48%) is taken from result on first ranking position. Furthermore, some correlations between prepositions and the most often appearing content words in keywords was discovered. Results show that featured snippets are often taken from trustworthy websites like e.g., Wikipedia and are mainly presented in form of a paragraph. Paragraph can be read by Google Assistant or Home Assistant with voice search. We conclude our findings with discussion and research limitations.
Representation transfer for differentially private drug sensitivity prediction

Motivation: Human genomic datasets often contain sensitive information that limits use and sharing of the data. In particular, simple anonymisation strategies fail to provide sufficient level of protection for genomic data, because the data are inherently identifiable. Differentially private machine learning can help by guaranteeing that the published results do not leak too much information about any individual data point. Recent research has reached promising results on differentially private drug sensitivity prediction using gene expression data. Differentially private learning with genomic data is challenging because it is more difficult to guarantee the privacy in high dimensions. Dimensionality reduction can help, but if the dimension reduction mapping is learned from the data, then it needs to be differentially private too, which can carry a significant privacy cost. Furthermore, the selection of any hyperparameters (such as the target dimensionality) needs to also avoid leaking private information. Results: We study an approach that uses a large public dataset of similar type to learn a compact representation for differentially private learning. We compare three representation learning methods: variational autoencoders, PCA and random projection. We solve two machine learning tasks on gene expression of cancer cell lines: cancer type classification, and drug sensitivity prediction. The experiments demonstrate significant benefit from all representation learning methods with variational autoencoders providing the most accurate predictions most often. Our results significantly improve over previous state-of-the-art in accuracy of differentially private drug sensitivity prediction.