Reviewerly/exHarmony huggingface dataset:
On Affine Hilbert Functions of Unions of Layers in Finite Grids

The affine Hilbert function is a classical algebraic object that has been central, among other tools, to the development of the polynomial method in combinatorics. Owing to its concrete connections with Gr\"obner basis theory, as well as its applicability in several areas like computational complexity, combinatorial geometry, and coding theory, an important line of enquiry is to understand the affine Hilbert function of structured sets of points in the affine space. In this work, we determine the affine Hilbert function (over the reals) of arbitrary unions of layers of points in a uniform grid (a finite grid with the component sets having equispaced points), where each layer of points is determined by a fixed sum of components for all the points. This extends a result of Bernasconi and Egidi (Inf. Comput. 1999) from the Boolean cube setting to the uniform grid setting. Our proofs follow a similar outline as that of Bernasconi and Egidi. However, there are two bottlenecks that arise in the uniform grid setting. We resolve these by using (i) a classical fact that a symmetric Jordan basis exists for the function space on a uniform grid, and (ii) an extension to multisets of an algebraic interpretation by Friedl and R\'onyai (Discrete Math. 2003) of the notion of order shattering. The affine Hilbert function is, in fact, a stronger notion than the finite-degree Zariski closure, which is yet another important tool in the polynomial method toolkit. We conclude by giving an alternative proof of a combinatorial characterization of a variant of finite-degree Zariski closures, for unions of layers in uniform grids, obtained in an earlier work of the author (arXiv, 2021).
Optimal steering of matrix product states and quantum many-body scars

Ongoing development of quantum simulators allows for a progressively finer degree of control of quantum many-body systems. This motivates the development of efficient approaches to facilitate the control of such systems and enable the preparation of non-trivial quantum states. Here we formulate an approach to control quantum systems based on matrix product states~(MPS). We compare counter-diabatic and leakage minimization approaches to the so-called local steering problem, that consists in finding the best value of the control parameters for generating a unitary evolution of the specific MPS state in a given direction. In order to benchmark the different approaches, we apply them to the generalization of the PXP model known to exhibit coherent quantum dynamics due to quantum many-body scars. We find that the leakage-based approach generally outperforms the counter-diabatic framework and use it to construct a Floquet model with quantum scars. We perform the first steps towards global trajectory optimization and demonstrate entanglement steering capabilities in the generalized PXP model. Finally we apply our leakage minimization approach to construct quantum scars in the periodically driven non-integrable Ising model.
Adversarially robust segmentation models learn perceptually-aligned gradients

The effects of adversarial training on semantic segmentation networks has not been thoroughly explored. While previous work has shown that adversarially-trained image classifiers can be used to perform image synthesis, we have yet to understand how best to leverage an adversarially-trained segmentation network to do the same. Using a simple optimizer, we demonstrate that adversarially-trained semantic segmentation networks can be used to perform image inpainting and generation. Our experiments demonstrate that adversarially-trained segmentation networks are more robust and indeed exhibit perceptually-aligned gradients which help in producing plausible image inpaintings. We seek to place additional weight behind the hypothesis that adversarially robust models exhibit gradients that are more perceptually-aligned with human vision. Through image synthesis, we argue that perceptually-aligned gradients promote a better understanding of a neural network's learned representations and aid in making neural networks more interpretable.
The Unexplored Treasure Trove of Phabricator Code Review

Phabricator is a modern code collaboration tool used by popular projects like FreeBSD and Mozilla. However, unlike the other well-known code review environments, such as Gerrit or GitHub, there is no readily accessible public code review dataset for Phabricator. This paper describes our experience mining code reviews from five different projects that use Phabricator (Blender, FreeBSD, KDE, LLVM, and Mozilla). We discuss the challenges associated with the data retrieval process and our solutions, resulting in a dataset with details regarding 317,476 Phabricator code reviews. Our dataset is available in both JSON and MySQL database dump formats. The dataset enables analyses of the history of code reviews at a more granular level than other platforms. In addition, given that the projects we mined are publicly accessible via the Conduit API, our dataset can be used as a foundation to fetch additional details and insights.
Graph Neural Networks: a bibliometrics overview

Recently, graph neural networks have become a hot topic in machine learning community. This paper presents a Scopus based bibliometric overview of the GNNs research since 2004, when GNN papers were first published. The study aims to evaluate GNN research trend, both quantitatively and qualitatively. We provide the trend of research, distribution of subjects, active and influential authors and institutions, sources of publications, most cited documents, and hot topics. Our investigations reveal that the most frequent subject categories in this field are computer science, engineering, telecommunications, linguistics, operations research and management science, information science and library science, business and economics, automation and control systems, robotics, and social sciences. In addition, the most active source of GNN publications is Lecture Notes in Computer Science. The most prolific or impactful institutions are found in the United States, China, and Canada. We also provide must read papers and future directions. Finally, the application of graph convolutional networks and attention mechanism are now among hot topics of GNN research.
Object-Guided Day-Night Visual Localization in Urban Scenes

We introduce Object-Guided Localization (OGuL) based on a novel method of local-feature matching. Direct matching of local features is sensitive to significant changes in illumination. In contrast, object detection often survives severe changes in lighting conditions. The proposed method first detects semantic objects and establishes correspondences of those objects between images. Object correspondences provide local coarse alignment of the images in the form of a planar homography. These homographies are consequently used to guide the matching of local features. Experiments on standard urban localization datasets (Aachen, Extended-CMU-Season, RobotCar-Season) show that OGuL significantly improves localization results with as simple local features as SIFT, and its performance competes with the state-of-the-art CNN-based methods trained for day-to-night localization.
Transparent Human Evaluation for Image Captioning

We establish a rubric-based human evaluation protocol for image captioning models. Our scoring rubrics and their definitions are carefully developed based on machine- and human-generated captions on the MSCOCO dataset. Each caption is evaluated along two main dimensions in a tradeoff (precision and recall) as well as other aspects that measure the text quality (fluency, conciseness, and inclusive language). Our evaluations demonstrate several critical problems of the current evaluation practice. Human-generated captions show substantially higher quality than machine-generated ones, especially in coverage of salient information (i.e., recall), while all automatic metrics say the opposite. Our rubric-based results reveal that CLIPScore, a recent metric that uses image features, better correlates with human judgments than conventional text-only metrics because it is more sensitive to recall. We hope that this work will promote a more transparent evaluation protocol for image captioning and its automatic metrics.
Squeezing of nonlinear spin observables by one axis twisting in the  presence of decoherence: An analytical study

In an ensemble of two-level atoms that can be described in terms of a collective spin, entangled states can be used to enhance the sensitivity of interferometric precision measurements. While non-Gaussian spin states can produce larger quantum enhancements than spin-squeezed Gaussian states, their use requires the measurement of observables that are nonlinear functions of the three components of the collective spin. In this paper we develop strategies that achieve the optimal quantum enhancements using non-Gaussian states produced by a nonlinear one-axis-twisting Hamiltonian, and show that measurement-after-interaction techniques, known to amplify the output signals in quantum parameter estimation protocols, are effective in measuring nonlinear spin observables. Including the presence of the relevant decoherence processes from atomic experiments, we determine analytically the quantum enhancement of non-Gaussian over-squeezed states as a function of the noise parameters for arbitrary atom numbers.
Building a Privacy-Preserving Smart Camera System

Millions of consumers depend on smart camera systems to remotely monitor their homes and businesses. However, the architecture and design of popular commercial systems require users to relinquish control of their data to untrusted third parties, such as service providers (e.g., the cloud). Third parties therefore can (and in some instances have) access the video footage without the users' knowledge or consent -- violating the core tenet of user privacy. In this paper, we present CaCTUs, a privacy-preserving smart Camera system Controlled Totally by Users. CaCTUs returns control to the user; the root of trust begins with the user and is maintained through a series of cryptographic protocols, designed to support popular features, such as sharing, deleting, and viewing videos live. We show that the system can support live streaming with a latency of 2s at a frame rate of 10fps and a resolution of 480p. In so doing, we demonstrate that it is feasible to implement a performant smart-camera system that leverages the convenience of a cloud-based model while retaining the ability to control access to (private) data.
Smart Video based Threat Analysis using CNN

In recent years, more and more video surveillance devices like drones, CCTV's have been deployed due to an increase in demands related to public security and smart cities. There is a need to overcome the existing drawbacks of post-investigation techniques of video surveillance systems by providing a pre-alert generation system. The video surveillance system has become an important part of the security and protection of modern cities. So we are going to focus on video surveillance by giving video contents containing early fire events detection, suspicious activities and smart parking systems, and crowd estimation. Smart monitoring cameras equipped with intelligent video analytics techniques can monitor and pre-alert systems by capturing suspicious activity and events. Our work is based on deep learning techniques for video analysis with better performance and event detection with the advantages of alert generation.
Efficient quantum interpolation of smooth distributions

We present an efficient method to interpolate smooth distributions with a quantum computer that aims to complement data uploading techniques. The quantum algorithm operates in four steps: i) a small sample of the distribution is uploaded onto a reduced set of qubits; ii) a Quantum Fourier Transform (QFT) is applied; iii) qubits encoding vanishing high frequencies are added to the register; and iv) an inverse QFT is operated on the total register. This leverages on the efficiency of the QFT and opens the door of quantum advantage to a relevant family of distributions. We demonstrate the power of the method with an efficient interpolation of a toy gaussian model, discussing uploading methods and precision. We then apply the QFT interpolation technique to quantum encoded images and present further advantages when processing data in superposition.
EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring

Volumetric cell segmentation in fluorescence microscopy images is important to study a wide variety of cellular processes. Applications range from the analysis of cancer cells to behavioral studies of cells in the embryonic stage. Like in other computer vision fields, most recent methods use either large convolutional neural networks (CNNs) or vision transformer models (ViTs). Since the number of available 3D microscopy images is typically limited in applications, we take a different approach and introduce a small CNN for volumetric cell segmentation. Compared to previous CNN models for cell segmentation, our model is efficient and has an asymmetric encoder-decoder structure with very few parameters in the decoder. Training efficiency is further improved via transfer learning. In addition, we introduce Context Aware Pseudocoloring to exploit spatial context in z-direction of 3D images while performing volumetric cell segmentation slice-wise. We evaluated our method using different 3D datasets from the Cell Segmentation Benchmark of the Cell Tracking Challenge. Our segmentation method achieves top-ranking results, while our CNN model has an up to 25x lower number of parameters than other top-ranking methods. Code and pretrained models are available at: https://github.com/roydenwa/efficient-cell-seg
Are Writers Metaconscious when they Write? A Look at Genders

Writing is a demanding skill which entails writers to sync and blend with some writing elements such as vocabulary, syntax, grammar, sentence structure, strategies and lexical.This skill requires the ESL writers to be adequately proficient and expressive in using the language while writing the essays.In the ESL writing, writers rely on some strategies to regulate them the entire writing process.One of the known strategies that helps the writers to compose is Metacognitive writing strategies.Metacognitive writing strategies establish three main components namely planning, monitoring, and evaluating and they function as helpful writing regulator, enabling the ESL writers to excel in their writing.However, there are some variations in the act of using planning, monitoring, and evaluating by male and female writers.Hence, this study was conducted to explore on how male and female writers plan, monitor, and evaluate their writing and to see how these strategies assist them completely.It is a crucial for the writers to get acknowledged of these three components and know on how to apply them in their writing.To obtain the pertinent data, a male and a female student were selected from one public university in Johor to participate in the study.This qualitative study used semistructured interview as the main method in collecting pertinent data.From the findings, it was found that both male and female ESL writers had different ways and techniques when planning, monitoring, and evaluating their writing.Results of this study have given a great impact on teaching and learning of ESL academic writing.
Does MAML Only Work via Feature Re-use? A Data Centric Perspective

Recent work has suggested that a good embedding is all we need to solve many few-shot learning benchmarks. Furthermore, other work has strongly suggested that Model Agnostic Meta-Learning (MAML) also works via this same method - by learning a good embedding. These observations highlight our lack of understanding of what meta-learning algorithms are doing and when they work. In this work, we provide empirical results that shed some light on how meta-learned MAML representations function. In particular, we identify three interesting properties: 1) In contrast to previous work, we show that it is possible to define a family of synthetic benchmarks that result in a low degree of feature re-use - suggesting that current few-shot learning benchmarks might not have the properties needed for the success of meta-learning algorithms; 2) meta-overfitting occurs when the number of classes (or concepts) are finite, and this issue disappears once the task has an unbounded number of concepts (e.g., online learning); 3) more adaptation at meta-test time with MAML does not necessarily result in a significant representation change or even an improvement in meta-test performance - even when training on our proposed synthetic benchmarks. Finally, we suggest that to understand meta-learning algorithms better, we must go beyond tracking only absolute performance and, in addition, formally quantify the degree of meta-learning and track both metrics together. Reporting results in future work this way will help us identify the sources of meta-overfitting more accurately and help us design more flexible meta-learning algorithms that learn beyond fixed feature re-use. Finally, we conjecture the core challenge of re-thinking meta-learning is in the design of few-shot learning data sets and benchmarks - rather than in the algorithms, as suggested by previous work.
Multi-Expert Human Action Recognition with Hierarchical Super-Class  Learning

In still image human action recognition, existing studies have mainly leveraged extra bounding box information along with class labels to mitigate the lack of temporal information in still images; however, preparing extra data with manual annotation is time-consuming and also prone to human errors. Moreover, the existing studies have not addressed action recognition with long-tailed distribution. In this paper, we propose a two-phase multi-expert classification method for human action recognition to cope with long-tailed distribution by means of super-class learning and without any extra information. To choose the best configuration for each super-class and characterize inter-class dependency between different action classes, we propose a novel Graph-Based Class Selection (GCS) algorithm. In the proposed approach, a coarse-grained phase selects the most relevant fine-grained experts. Then, the fine-grained experts encode the intricate details within each super-class so that the inter-class variation increases. Extensive experimental evaluations are conducted on various public human action recognition datasets, including Stanford40, Pascal VOC 2012 Action, BU101+, and IHAR datasets. The experimental results demonstrate that the proposed method yields promising improvements. To be more specific, in IHAR, Sanford40, Pascal VOC 2012 Action, and BU101+ benchmarks, the proposed approach outperforms the state-of-the-art studies by 8.92%, 0.41%, 0.66%, and 2.11 % with much less computational cost and without any auxiliary annotation information. Besides, it is proven that in addressing action recognition with long-tailed distribution, the proposed method outperforms its counterparts by a significant margin.
SpeechPainter: Text-conditioned Speech Inpainting

We propose SpeechPainter, a model for filling in gaps of up to one second in speech samples by leveraging an auxiliary textual input. We demonstrate that the model performs speech inpainting with the appropriate content, while maintaining speaker identity, prosody and recording environment conditions, and generalizing to unseen speakers. Our approach significantly outperforms baselines constructed using adaptive TTS, as judged by human raters in side-by-side preference and MOS tests.
Quantum steering and quantum discord under noisy channels and  entanglement swapping

Quantum entanglement, discord, and EPR-steering are properties which are considered as valuable resources for fuelling quantum information-theoretic protocols. EPR-steering is a property that is more general than Bell-nonlocality and yet more restrictive than entanglement. Quantum discord on the other hand, captures non-classical behaviour beyond that of entanglement, and its study has remained of active research interest during the past two decades. Exploring the behaviour of these quantum properties in different physical scenarios, like those simulated by open quantum systems, is therefore of crucial importance for understanding their viability for quantum technologies. In this work, we analyse the behaviour of EPR-steering, entanglement, and quantum discord, for two-qubit states under various quantum processes. First, we consider the three noisy channel scenarios of; phase damping, generalised amplitude damping and stochastic dephasing channel. Second, we explore the behaviour of these quantum properties in an entanglement swapping scenario. We quantify EPR-steering by means of an inequality with three-input two-output measurement settings, and address quantum discord as the interferometric power of quantum states. Our findings are the following. First, we show that some of the relatively straightforward noisy channels here considered, can induce non-trivial dynamics such as sudden death as well as death and revival of EPR-steering and entanglement. Second, we find that although noisy channels in general reduce the amount of correlations present in the system, the swapping protocol on the other hand displays scenarios where these quantum correlations can be enhanced. These results therefore illustrate that quantum processes do not exclusively affect the quantum properties of physical systems in a negative manner, but that they can also have positive effects on such properties.
Augmentation of base classifier performance via HMMs on a handwritten  character data set

This paper presents results of a study of the performance of several base classifiers for recognition of handwritten characters of the modern Latin alphabet. Base classification performance is further enhanced by utilizing Viterbi error correction by determining the Viterbi sequence. Hidden Markov Models (HMMs) models exploit relationships between letters within a word to determine the most likely sequence of characters. Four base classifiers are studied along with eight feature sets extracted from the handwritten dataset. The best classification performance after correction was 89.8%, and the average was 68.1%
AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorch

Current state-of-the-art employs approximate multipliers to address the highly increased power demands of DNN accelerators. However, evaluating the accuracy of approximate DNNs is cumbersome due to the lack of adequate support for approximate arithmetic in DNN frameworks. We address this inefficiency by presenting AdaPT, a fast emulation framework that extends PyTorch to support approximate inference as well as approximation-aware retraining. AdaPT can be seamlessly deployed and is compatible with the most DNNs. We evaluate the framework on several DNN models and application fields including CNNs, LSTMs, and GANs for a number of approximate multipliers with distinct bitwidth values. The results show substantial error recovery from approximate re-training and reduced inference time up to 53.9x with respect to the baseline approximate implementation.
Survey on Large Scale Neural Network Training

Modern Deep Neural Networks (DNNs) require significant memory to store weight, activations, and other intermediate tensors during training. Hence, many models do not fit one GPU device or can be trained using only a small per-GPU batch size. This survey provides a systematic overview of the approaches that enable more efficient DNNs training. We analyze techniques that save memory and make good use of computation and communication resources on architectures with a single or several GPUs. We summarize the main categories of strategies and compare strategies within and across categories. Along with approaches proposed in the literature, we discuss available implementations.