Reviewerly/exHarmony huggingface dataset:
“Caption” as a Coherence Relation: Evidence and Implications

We study verbs in image–text corpora, contrasting caption corpora, where texts are explicitly written to characterize image content, with depiction corpora, where texts and images may stand in more general relations. Captions show a distinctively limited distribution of verbs, with strong preferences for specific tense, aspect, lexical aspect, and semantic field. These limitations, which appear in data elicited by a range of methods, restrict the utility of caption corpora to inform image retrieval, multimodal document generation, and perceptually-grounded semantic models. We suggest that these limitations reflect the discourse constraints in play when subjects write texts to accompany imagery, so we argue that future development of image–text corpora should work to increase the diversity of event descriptions, while looking explicitly at the different ways text and imagery can be coherently related.
Automatic Modulation Classification of Digital Communication Signals Using SVM Based on Hybrid Features, Cyclostationary, and Information Entropy

Since digital communication signals are widely used in radio and underwater acoustic systems, the modulation classification of these signals has become increasingly significant in various military and civilian applications. However, due to the adverse channel transmission characteristics and low signal to noise ratio (SNR), the modulation classification of communication signals is extremely challenging. In this paper, a novel method for automatic modulation classification of digital communication signals using a support vector machine (SVM) based on hybrid features, cyclostationary, and information entropy is proposed. In this proposed method, by combining the theory of the cyclostationary and entropy, based on the existing signal features, we propose three other new features to assist the classification of digital communication signals, which are the maximum value of the normalized cyclic spectrum when the cyclic frequency is not zero, the Shannon entropy of the cyclic spectrum, and Renyi entropy of the cyclic spectrum respectively. Because these new features do not require any prior information and have a strong anti-noise ability, they are very suitable for the identification of communication signals. Finally, a one against one SVM is designed as a classifier. Simulation results show that the proposed method outperforms the existing methods in terms of classification performance and noise tolerance.
Common Fate for Animated Transitions in Visualization

The Law of Common Fate from Gestalt psychology states that visual objects moving with the same velocity along parallel trajectories will be perceived by a human observer as grouped. However, the concept of common fate is much broader than mere velocity; in this paper we explore how common fate results from coordinated changes in luminance and size. We present results from a crowdsourced graphical perception study where we asked workers to make perceptual judgments on a series of trials involving four graphical objects under the influence of conflicting static and dynamic visual factors (position, size and luminance) used in conjunction. Our results yield the following rankings for visual grouping: motion> (dynamic luminance, size, luminance); dynamic size> (dynamic luminance, position); and dynamic luminance > size. We also conducted a follow-up experiment to evaluate the three dynamic visual factors in a more ecologically valid setting, using both a Gapminder-like animated scatterplot and a thematic map of election data. The results indicate that in practice the relative grouping strengths of these factors may depend on various parameters including the visualization characteristics and the underlying data. We discuss design implications for animated transitions in data visualization.
Provably robust boosted decision stumps and trees against adversarial attacks

The problem of adversarial robustness has been studied extensively for neural networks. However, for boosted decision trees and decision stumps there are almost no results, even though they are widely used in practice (e.g. XGBoost) due to their accuracy, interpretability, and efficiency. We show in this paper that for boosted decision stumps the \textit{exact} min-max robust loss and test error for an $l_\infty$-attack can be computed in $O(T\log T)$ time per input, where $T$ is the number of decision stumps and the optimal update step of the ensemble can be done in $O(n^2\,T\log T)$, where $n$ is the number of data points. For boosted trees we show how to efficiently calculate and optimize an upper bound on the robust loss, which leads to state-of-the-art robust test error for boosted trees on MNIST (12.5\% for $\epsilon_\infty=0.3$), FMNIST (23.2\% for $\epsilon_\infty=0.1$), and CIFAR-10 (74.7\% for $\epsilon_\infty=8/255$). Moreover, the robust test error rates we achieve are competitive to the ones of provably robust convolutional networks. The code of all our experiments is available at \url{http://github.com/max-andr/provably-robust-boosting}.
Informationally restricted quantum correlations

Quantum communication leads to strong correlations, that can outperform classical ones. Complementary to previous works in this area, we investigate correlations in prepare-and-measure scenarios assuming a bound on the information content of the quantum communication, rather than on its Hilbert-space dimension. Specifically, we explore the extent of classical and quantum correlations given an upper bound on the one-shot accessible information. We provide a characterisation of the set of classical correlations and show that quantum correlations are stronger than classical ones. We also show that limiting information rather than dimension leads to stronger quantum correlations. Moreover, we present device-independent tests for placing lower bounds on the information given observed correlations. Finally, we show that quantum communication carrying $\log d$ bits of information is at least as strong a resource as $d$-dimensional classical communication assisted by pre-shared entanglement.
Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits

Machine learning is seen as a promising application of quantum computation. For near-term noisy intermediate-scale quantum (NISQ) devices, parametrized quantum circuits (PQCs) have been proposed as machine learning models due to their robustness and ease of implementation. However, the cost function is normally calculated classically from repeated measurement outcomes, such that it is no longer encoded in a quantum state. This prevents the value from being directly manipulated by a quantum computer. To solve this problem, we give a routine to embed the cost function for machine learning into a quantum circuit, which accepts a training dataset encoded in superposition or an easily preparable mixed state. We also demonstrate the ability to evaluate the gradient of the encoded cost function in a quantum state.
On the Brownian separable permuton

Abstract The Brownian separable permuton is a random probability measure on the unit square, which was introduced by Bassino, Bouvel, Féray, Gerin and Pierrot (2016) as the scaling limit of the diagram of the uniform separable permutation as size grows to infinity. We show that, almost surely, the permuton is the pushforward of the Lebesgue measure on the graph of a random measure-preserving function associated to a Brownian excursion whose strict local minima are decorated with independent and identically distributed signs. As a consequence, its support is almost surely totally disconnected, has Hausdorff dimension one, and enjoys self-similarity properties inherited from those of the Brownian excursion. The density function of the averaged permuton is computed and a connection with the shuffling of the Brownian continuum random tree is explored.
Einstein-Podolsky-Rosen Energy-Time Entanglement of Narrow-Band Biphotons

We report the direct characterization of energy-time entanglement of narrow-band biphotons produced from spontaneous four-wave mixing in cold atoms. The Stokes and anti-Stokes two-photon temporal correlation is measured by single-photon counters with nanosecond temporal resolution, and their joint spectrum is determined by using a narrow linewidth optical cavity. The energy-time entanglement is verified by the joint frequency-time uncertainty product of $0.063\ifmmode\pm\else\textpm\fi{}0.0044$, which does not only violate the separability criterion but also satisfies the continuous variable Einstein-Podolsky-Rosen steering inequality.
Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$

In recent years several adversarial attacks and defenses have been proposed. Often seemingly robust models turn out to be non-robust when more sophisticated attacks are used. One way out of this dilemma are provable robustness guarantees. While provably robust models for specific $l_p$-perturbation models have been developed, we show that they do not come with any guarantee against other $l_q$-perturbations. We propose a new regularization scheme, MMR-Universal, for ReLU networks which enforces robustness wrt $l_1$- \textit{and} $l_\infty$-perturbations and show how that leads to the first provably robust models wrt any $l_p$-norm for $p\geq 1$.
On the Performance of Reed-Muller Codes with respect to Random Errors and Erasures

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)On the Performance of Reed-Muller Codes with respect to Random Errors and ErasuresOri Sberlo and Amir ShpilkaOri Sberlo and Amir Shpilkapp.1357 - 1376Chapter DOI:https://doi.org/10.1137/1.9781611975994.82PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract This work proves new results on the ability of binary Reed-Muller codes to decode from random errors and erasures. Specifically, we prove that RM codes with m variables and degree γm, for some explicit constant γ achieve capacity for random erasures (i.e. for the binary erasure channel) and for random errors (for the binary symmetric channel). Earlier, it was known that RM codes achieve capacity for the binary symmetric channel for degrees r = o(m). For the binary erasure channel it was known that RM codes achieve capacity for degree . Thus, our results provide a new range of parameters for which RM achieve capacity for these two well studied channels. In addition, our results imply that for every ϵ > 0 (in fact we can get up to RM codes of degree r < (1/2 – ϵ)m can correct a fraction of 1 – o(1) random erasures with high probability. We also show that, information theoretically, such codes can handle a fraction of random errors with high probability. For example, given noisy evaluations of a degree 0.499m polynomial, it is possible to interpolate it even if a random 0. 499 fraction of the evaluations were corrupted, with high probability. While the o(1) terms are not the correct ones to ensure capacity, these results show that RM codes of rates up to 1/poly(log n) (where n = 2m is the block length) are is some sense as good as capacity achieving codes. We obtain these results by proving improved bounds on the weight distribution of Reed-Muller codes of high degrees. Namely, given weight β ϵ (0, 1) we prove an upper bound on the number of codewords of relative weight at most β. We obtain new results in two different settings: for weights β < 1/2 and for weights that are close to 1/2. Our results for weights close to 1/2 also answer an open problem posed by Beame et al. [10]. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Statistical convergence of the EM algorithm on Gaussian mixture models

We study the convergence behavior of the Expectation Maximization (EM) algorithm on Gaussian mixture models with an arbitrary number of mixture components and mixing weights. We show that as long as the means of the components are separated by at least $\Omega (\sqrt{\min \{M,d\}})$, where $M$ is the number of components and $d$ is the dimension, the EM algorithm converges locally to the global optimum of the log-likelihood. Further, we show that the convergence rate is linear and characterize the size of the basin of attraction to the global optimum.
Toward Specular Removal from Natural Images Based on Statistical Reflection Models

Removing specular reflections from images is critical for improving the performance of computer vision algorithms. Recently, state-of-the-art methods have demonstrated remarkably good performance at removing specular reflections from chromatic images. These methods are typically based on the chromatic pixels assumption; therefore, they are prone to failure in the achromatic regions. This paper presents a novel method that is applicable to natural images, because it is effective for both chromatic and achromatic regions. The proposed method is based on modeling the general properties of diffuse and specular reflections in a solid convex optimization framework. Considering the physical constraints, we determine the global optimal solution using the split Bregman method. Experimental results demonstrate the effectiveness of the proposed method, particularly for the achromatic regions, and its competence as a state-of-the-art method for removing specular reflections from the chromatic regions.
Experimental Optimal Orienteering via Parallel and Antiparallel Spins

Antiparallel spins are superior in orienteering to parallel spins. This intriguing phenomenon is tied to entanglement associated with quantum measurements rather than quantum states. Using photonic systems, we experimentally realize the optimal orienteering protocols based on parallel spins and antiparallel spins, respectively. The optimal entangling measurements for decoding the direction information from parallel spins and antiparallel spins are realized using photonic quantum walks, which is a useful idea that is of wide interest in quantum information processing and foundational studies. Our experiments clearly demonstrate the advantage of antiparallel spins over parallel spins in orienteering. In addition, entangling measurements can extract more information than local measurements even if no entanglement is present in the quantum states.
The Performance of Quality Metrics in Assessing Error-Concealed Video Quality

In highly-interactive video streaming applications such as video conferencing, tele-presence, or tele-operation, retransmission is typically not used, due to the tight deadline of the application. In such cases, the lost or erroneous data must be concealed. While various error concealment techniques exist, there is no defined rule to compare their perceived quality. In this paper, the performance of 16 existing image and video quality metrics (PSNR, SSIM, VQM, etc.) evaluating errorconcealed video quality is studied. The encoded video is subjected to packet loss and the loss is concealed using various error concealment techniques. We show that the subjective quality of the video cannot be necessarily predicted from the visual quality of the error-concealed frame alone. We then apply the metrics to the error-concealed images/videos and evaluate their success in predicting the scores reported by human subjects. The errorconcealed videos are judged by image quality metrics applied on the lossy frame, or by video quality metrics applied on the video clip containing that lossy frame; this way, the impact of error propagation is also considered by the objective metrics. The measurement and comparison of the results show that, mostly though not always, measuring the objective quality of the video is a better way to judge the error concealment performance. Moreover, our experiments show that when the objective quality metrics are used for the assessment of the performance of an error concealment technique, they do not behave as they would for general quality assessment. In fact, some newly developed metrics show the correct decision only about 60% of the time, leading to an unacceptable error rate of as much as 40%. Our analysis shows which specific quality metrics are relatively more suitable for error-concealed videos.
Multimodal Entity Linking for Tweets

In many information extraction applications, entity linking (EL) has emerged as a crucial task that allows leveraging information about named entities from a knowledge base. In this paper, we address the task of multimodal entity linking (MEL), an emerging research field in which textual and visual information is used to map an ambiguous mention to an entity in a knowledge base (KB). First, we propose a method for building a fully annotated Twitter dataset for MEL, where entities are defined in a Twitter KB. Then, we propose a model for jointly learning a representation of both mentions and entities from their textual and visual contexts. We demonstrate the effectiveness of the proposed model by evaluating it on the proposed dataset and highlight the importance of leveraging visual information when it is available.
Privacy-Preserving Image Retrieval and Sharing in Social Multimedia Applications

Every day social multimedia applications generate millions of images. To handle such huge amount of images, an optimal solution is using the public cloud, since it has powerful storage capability. Images usually contain a wealth of sensitive information, therefore social service providers need not only to provide services such as retrieval and sharing but also to protect the privacies of the images. In this paper, we propose a privacy-preserving scheme for content-based image retrieval and sharing in social multimedia applications. First, the users extract visual features from the images, and perform locality-sensitive hashing functions on visual features to generate image profile vectors. We then model the retrieval on the images as the equality search on the image profile vectors. To enable accurate and efficient retrieval, we design the secure index structure based on cuckoo hashing, which has constant lookup time. To meet the requirements of dynamic image updating, we enrich our service with image insertion and deletion. In order to reduce the key management overhead and the access control overhead in social applications, we process keys using secret sharing techniques to enable the users holding similar images to query and decrypt images independently. Finally we implement the prototype of the proposed scheme, and perform experiments over encrypted image databases.
Quantum illumination via quantum-enhanced sensing

Quantum-enhanced sensing has a goal of enhancing a parameter sensitivity with input quantum states, while quantum illumination has a goal of enhancing a target detection capability with input entangled states in a heavy noise environment. Here we propose a concatenation between quantum-enhanced sensing and quantum illumination that can take quantum advantage over the classical limit. First, phase sensing in an interferometry is connected to a target sensing via quantum Fisher information. Second, the target sensitivity is investigated in noisy quantum-enhanced sensing. Under the same input state energy, for example, $N$-photon entangled states can exhibit better performance than a two-mode squeezed vacuum state and a separable coherent state. Incorporating a photon-number difference measurement, finally, the noisy target sensitivity is connected to a signal-to-noise ratio which is associated with a minimum error probability of discriminating the presence and absence of the target. We show that both the target sensitivity and the signal-to-noise ratio can be enhanced with increasing thermal noise.
Efficient QR Code Secret Embedding Mechanism Based on Hamming Code

QR code is designed as machine readable symbol, which is widely used in various fields of life due to its large message capacity and fast decoding speed. However, as a public standard, it will give rise to the security issue when delivering sensitive information with QR code. To overcome this weakness, this paper explores the characteristic of QR code to propose an efficient secret hiding mechanism to protect the sensitive information within QR code. The secret message would be embedded into cover QR code based on (8, 4) Hamming code. The error correction capacity (ECC) of QR code would correct the errors produced in the secret embedding procedure, and the valid marked QR code would reduce people’s curious. Compared to the state-of-art works, the proposed scheme achieves a better performance on the aspects of secret payload and embedding efficiency.
Relation Extraction from Clinical Narratives Using Pre-trained Language Models.

Natural language processing (NLP) is useful for extracting information from clinical narratives, and both traditional machine learning methods and more-recent deep learning methods have been successful in various clinical NLP tasks. These methods often depend on traditional word embeddings that are outputs of language models (LMs). Recently, methods that are directly based on pre-trained language models themselves, followed by fine-tuning on the LMs (e.g. the Bidirectional Encoder Representations from Transformers (BERT)), have achieved state-of-the-art performance on many NLP tasks. Despite their success in the open domain and biomedical literature, these pre-trained LMs have not yet been applied to the clinical relation extraction (RE) task. In this study, we developed two different implementations of the BERT model for clinical RE tasks. Our results show that our tuned LMs outperformed previous state-of-the-art RE systems in two shared tasks, which demonstrates the potential of LM-based methods on the RE task.
SLSGD: Secure and Efficient Distributed On-device Machine Learning

We consider distributed on-device learning with limited communication and security requirements. We propose a new robust distributed optimization algorithm with efficient communication and attack tolerance. The proposed algorithm has provable convergence and robustness under non-IID settings. Empirical results show that the proposed algorithm stabilizes the convergence and tolerates data poisoning on a small number of workers.
Accurate bounding-box regression with distance-IoU loss for visual tracking

Most existing trackers are based on using a classifier and multi-scale estimation to estimate the target state. Consequently, and as expected, trackers have become more stable while tracking accuracy has stagnated. While trackers adopt a maximum overlap method based on an intersection-over-union (IoU) loss to mitigate this problem, there are defects in the IoU loss itself, that make it impossible to continue to optimize the objective function when a given bounding box is completely contained within/without another bounding box; this makes it very challenging to accurately estimate the target state. Accordingly, in this paper, we address the above-mentioned problem by proposing a novel tracking method based on a distance-IoU (DIoU) loss, such that the proposed tracker consists of target estimation and target classification. The target estimation part is trained to predict the DIoU score between the target ground-truth bounding-box and the estimated bounding-box. The DIoU loss can maintain the advantage provided by the IoU loss while minimizing the distance between the center points of two bounding boxes, thereby making the target estimation more accurate. Moreover, we introduce a classification part that is trained online and optimized with a Conjugate-Gradient-based strategy to guarantee real-time tracking speed. Comprehensive experimental results demonstrate that the proposed method achieves competitive tracking accuracy when compared to state-of-the-art trackers while with a real-time tracking speed.