Reviewerly/exHarmony huggingface dataset:
Deterministic, scalable, and entanglement efficient initialization of  arbitrary quantum states

Quantum computing promises to provide exponential speed-ups to certain classes of problems. In many such quantum algorithms, a classical vector $\mathbf{b}$ is encoded in the amplitudes of a quantum state $| b\rangle$. However, efficient amplitude encoding of an arbitrary vector $\mathbf{b}$ in $| b \rangle$ is known to be a difficult problem that can reverse the gains of such algorithms. More importantly, an arbitrary quantum state of $Q$ qubits generally requires $\sim 2^Q$ number of entangling gates, which is problematic for today's Noisy-Intermediate Scale Quantum (NISQ) computers where large numbers of such error-prone gates will result in significant decoherence before initialization is completed. In this work, we demonstrate a deterministic and scalable initialization algorithm that allows for states with low entanglement to be initialized on actual quantum computers with more than an order of magnitude less entangling gates and significantly shallower circuits as compared to isometric decomposition. For states with higher entanglement, optimally approximate states with lower entanglement may be found and initialized with similar performance. We show this to be true for various cases of interest such as the normal and log-normal distributions. Unlike variational approaches, our method requires no classical optimizations in high dimensional spaces and is not plagued with exponentially vanishing gradients or a multitude of local minimas.
LayerZero: Trustless Omnichain Interoperability Protocol

The proliferation of blockchains has given developers a variety of platforms on which to run their smart contracts based on application features and requirements for throughput, security, and cost. However, a consequence of this freedom is severe fragmentation; Each chain is isolated, forcing users to silo their liquidity and limiting options to move liquidity and state between walled ecosystems. This paper presents LayerZero, the first trustless omnichain interoperability protocol, which provides a powerful, low level communication primitive upon which a diverse set of cross-chain applications can be built. Using this new primitive, developers can implement seamless inter-chain applications like a cross-chain DEX or multi-chain yield aggregator without having to rely on a trusted custodian or intermediate transactions. Simply put, LayerZero is the first system to trustlessly enable direct transactions across all chains. Allowing transactions to flow freely between chains provides opportunities for users to consolidate fragmented pockets of liquidity while also making full use of applications on separate chains. With LayerZero, we provide the network fabric underlying the fully-connected omnichain ecosystem of the future.
A vectorized sea horizon edge filter for maritime video processing tasks

Accurate and fast sea horizon detection is vital for tasks in autonomous navigation and maritime security, such as video stabilization, target region reduction, precise tracking, and obstacle avoidance. This paper introduces a novel sea horizon detector from RGB videos, focusing on rapid and effective sea noise suppression while preserving weak horizon edges. Line fitting methods are subsequently employed on filtered edges for horizon detection. We address the filtering problem by extracting line segments with a very low edge threshold, ensuring the detection of line segments even in low-contrast horizon conditions. We show that horizon line segments have simple and relevant properties in RGB images, which we exploit to suppress noisy segments. Then we use the surviving segments to construct a filtered edge map and infer the horizon from the filtered edges. We propose a careful incorporation of temporal information for horizon inference and experimentally show its effectiveness. We address the computational constraint by providing a vectorized implementation for efficient CPU execution, and leveraging image downsizing with minimal loss of accuracy on the original size. Moreover, we contribute a public horizon line dataset to enrich existing data resources. Our algorithm's performance is rigorously evaluated against state-of-the-art methods, and its components are validated through ablation experiments. Source code and dataset files are available at:
CS-Rep: Making Speaker Verification Networks Embracing Re-parameterization

Automatic speaker verification (ASV) systems, which determine whether two speeches are from the same speaker, mainly focus on verification accuracy while ignoring inference speed. However, in real applications, both inference speed and verification accuracy are essential. This study proposes cross-sequential re-parameterization (CS-Rep), a novel topology re-parameterization strategy for multi-type networks, to increase the inference speed and verification accuracy of models. CS-Rep solves the problem that existing re-parameterization methods are unsuitable for typical ASV backbones. When a model applies CS-Rep, the training-period network utilizes a multi-branch topology to capture speaker information, whereas the inference-period model converts to a time-delay neural network (TDNN)-like plain backbone with stacked TDNN layers to achieve the fast inference speed. Based on CS-Rep, an improved TDNN with friendly test and deployment called Rep-TDNN is proposed. Compared with the state-of-the-art model ECAPA-TDNN, which is highly recognized in the industry, Rep-TDNN increases the actual inference speed by about 50% and reduces the EER by 10%. The code will be released.
Pyramidal Blur Aware X-Corner Chessboard Detector

With camera resolution ever increasing and the need to rapidly recalibrate robotic platforms in less than ideal environments, there is a need for faster and more robust chessboard fiducial marker detectors. A new chessboard detector is proposed that is specifically designed for: high resolution images, focus/motion blur, harsh lighting conditions, and background clutter. This is accomplished using a new x-corner detector, where for the first time blur is estimated and used in a novel way to enhance corner localization, edge validation, and connectivity. Performance is measured and compared against other libraries using a diverse set of images created by combining multiple third party datasets and including new specially crafted scenarios designed to stress the state-of-the-art. The proposed detector has the best F1- Score of 0.97, runs 1.9x faster than next fastest, and is a top performer for corner accuracy, while being the only detector to have consistent good performance in all scenarios.
Nonparametric Matrix Estimation with One-Sided Covariates

Consider the task of matrix estimation in which a dataset $X \in \mathbb{R}^{n\times m}$ is observed with sparsity $p$, and we would like to estimate $\mathbb{E}[X]$, where $\mathbb{E}[X_{ui}] = f(\alpha_u, \beta_i)$ for some Holder smooth function $f$. We consider the setting where the row covariates $\alpha$ are unobserved yet the column covariates $\beta$ are observed. We provide an algorithm and accompanying analysis which shows that our algorithm improves upon naively estimating each row separately when the number of rows is not too small. Furthermore when the matrix is moderately proportioned, our algorithm achieves the minimax optimal nonparametric rate of an oracle algorithm that knows the row covariates. In simulated experiments we show our algorithm outperforms other baselines in low data regimes.
Defensive Tensorization

We propose defensive tensorization, an adversarial defence technique that leverages a latent high-order factorization of the network. The layers of a network are first expressed as factorized tensor layers. Tensor dropout is then applied in the latent subspace, therefore resulting in dense reconstructed weights, without the sparsity or perturbations typically induced by the randomization.Our approach can be readily integrated with any arbitrary neural architecture and combined with techniques like adversarial training. We empirically demonstrate the effectiveness of our approach on standard image classification benchmarks. We validate the versatility of our approach across domains and low-precision architectures by considering an audio classification task and binary networks. In all cases, we demonstrate improved performance compared to prior works.
The Role of the Scrum Master in an Industry based University Course

Background: Scrum is the most commonly used agile software development method, and the role of the Scrum Master (SM) in a Scrum environment is vital. Therefore, through an industry based university course for final year undergraduate and masters students, we aimed to give students both theoretical and practical understanding of the role of SM via hands-on experience on playing the role in real-world Scrum contexts. Method: We asked them (92 students) to share their experiences and learnings on the role of SM through reflective surveys and essays. Students participated in reflective surveys (311 survey responses) over 5 weeks in the course, and they submitted essays (92 essays) at the end of the course. We used a mixed-methods approach using Socio-Technical Grounded Theory analysis techniques and trend and regression based statistical analysis to analyse the survey responses and the essays. Findings: We identified the key responsibilities and duties of the SM, common challenges faced by the SM and the team due to the role of the SM, root causes of the challenges, strategies used by the SM and the team to overcome the challenges, and the overall experience of the students. Based on the results, we present recommendations for educators.
MoDeRNN: Towards Fine-grained Motion Details for Spatiotemporal Predictive Learning

Spatiotemporal predictive learning (ST-PL) aims at predicting the subsequent frames via limited observed sequences, and it has broad applications in the real world. However, learning representative spatiotemporal features for prediction is challenging. Moreover, chaotic uncertainty among consecutive frames exacerbates the difficulty in long-term prediction. This paper concentrates on improving prediction quality by enhancing the correspondence between the previous context and the current state. We carefully design Detail Context Block (DCB) to extract fine-grained details and improve the isolated correlation between upper context state and current input state. We integrate DCB with standard ConvLSTM and introduce Motion Details RNN (MoDeRNN) to capture fine-grained spatiotemporal features and improve the expression of latent states of RNNs to achieve significant quality. Experiments on Moving MNIST and Typhoon datasets demonstrate the effectiveness of the proposed method. MoDeRNN outperforms existing state-of-the-art techniques qualitatively and quantitatively with lower computation loads.
Optimal Model Averaging: Towards Personalized Collaborative Learning

In federated learning, differences in the data or objectives between the participating nodes motivate approaches to train a personalized machine learning model for each node. One such approach is weighted averaging between a locally trained model and the global model. In this theoretical work, we study weighted model averaging for arbitrary scalar mean estimation problems under minimal assumptions on the distributions. In a variant of the bias-variance trade-off, we find that there is always some positive amount of model averaging that reduces the expected squared error compared to the local model, provided only that the local model has a non-zero variance. Further, we quantify the (possibly negative) benefit of weighted model averaging as a function of the weight used and the optimal weight. Taken together, this work formalizes an approach to quantify the value of personalization in collaborative learning and provides a framework for future research to test the findings in multivariate parameter estimation and under a range of assumptions.
Algorithms for the Communication of Samples

The efficient communication of noisy data has applications in several areas of machine learning, such as neural compression or differential privacy, and is also known as reverse channel coding or the channel simulation problem. Here we propose two new coding schemes with practical advantages over existing approaches. First, we introduce ordered random coding (ORC) which uses a simple trick to reduce the coding cost of previous approaches. This scheme further illuminates a connection between schemes based on importance sampling and the so-called Poisson functional representation. Second, we describe a hybrid coding scheme which uses dithered quantization to more efficiently communicate samples from distributions with bounded support.
Findings from Experiments of On-line Joint Reinforcement Learning of Semantic Parser and Dialogue Manager with real Users

Design of dialogue systems has witnessed many advances lately, yet acquiring huge set of data remains an hindrance to their fast development for a new task or language. Besides, training interactive systems with batch data is not satisfactory. On-line learning is pursued in this paper as a convenient way to alleviate these difficulties. After the system modules are initiated, a single process handles data collection, annotation and use in training algorithms. A new challenge is to control the cost of the on-line learning borne by the user. Our work focuses on learning the semantic parsing and dialogue management modules (speech recognition and synthesis offer ready-for-use solutions). In this context we investigate several variants of simultaneous learning which are tested in user trials. In our experiments, with varying merits, they can all achieve good performance with only a few hundreds of training dialogues and overstep a handcrafted system. The analysis of these experiments gives us some insights, discussed in the paper, into the difficulty for the system's trainers to establish a coherent and constant behavioural strategy to enable a fast and good-quality training phase.
RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network

Modern digital cameras and smartphones mostly rely on image signal processing (ISP) pipelines to produce realistic colored RGB images. However, compared to DSLR cameras, low-quality images are usually obtained in many portable mobile devices with compact camera sensors due to their physical limitations. The low-quality images have multiple degradations i.e., sub-pixel shift due to camera motion, mosaick patterns due to camera color filter array, low-resolution due to smaller camera sensors, and the rest information are corrupted by the noise. Such degradations limit the performance of current Single Image Super-resolution (SISR) methods in recovering high-resolution (HR) image details from a single low-resolution (LR) image. In this work, we propose a Raw Burst Super-Resolution Iterative Convolutional Neural Network (RBSRICNN) that follows the burst photography pipeline as a whole by a forward (physical) model. The proposed Burst SR scheme solves the problem with classical image regularization, convex optimization, and deep learning techniques, compared to existing black-box data-driven methods. The proposed network produces the final output by an iterative refinement of the intermediate SR estimates. We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments that generalize robustly to real LR burst inputs with onl synthetic burst data available for training.
Improving the Diversity of Unsupervised Paraphrasing with Embedding Outputs

We present a novel technique for zero-shot paraphrase generation. The key contribution is an end-to-end multilingual paraphrasing model that is trained using translated parallel corpora to generate paraphrases into "meaning spaces" -- replacing the final softmax layer with word embeddings. This architectural modification, plus a training procedure that incorporates an autoencoding objective, enables effective parameter sharing across languages for more fluent monolingual rewriting, and facilitates fluency and diversity in generation. Our continuous-output paraphrase generation models outperform zero-shot paraphrasing baselines when evaluated on two languages using a battery of computational metrics as well as in human assessment.
Benchmarking Quantum Simulators

Time-averaged mixed-state equivalence (TAME) is used to benchmark quantum simulators with classical computing resources. The classical computation is feasible even if direct computation of the real-time dynamics is prohibitively costly.
RoBin: Facilitating the Reproduction of Configuration-Related Vulnerability

Vulnerability reproduction paves a way in debugging software failures, which need intensive manual efforts. However, some key factors (e.g., software configuration, trigger method) are often missing, so we can not directly reproduce the failure without extra attempts. Even worse, highly customized configuration options of programs create a barrier for reproducing the vulnerabilities that only appear under some specific combinations of configurations. In this paper, we address the problem mentioned above -- reproducing the configuration-related vulnerability. We try to solve it by proposing a binary similarity-based method to infer the specific building configurations via the binary from crash report. The main challenges are as follows: precise compilation option inference, program configuration inference, and source-code-to-binary matching. To achieve the goal, we implement RoBin, a binary similarity-based building configuration inference tool. To demonstrate the effectiveness, we test RoBin on 21 vulnerable cases upon 4 well-known open-source programs. It shows a strong ability in pinpointing the building configurations causing the vulnerability. The result can help developers reproduce and diagnose the vulnerability, and finally, patch the programs.
Multitask Adaptation by Retrospective Exploration with Learned World Models

Model-based reinforcement learning (MBRL) allows solving complex tasks in a sample-efficient manner. However, no information is reused between the tasks. In this work, we propose a meta-learned addressing model called RAMa that provides training samples for the MBRL agent taken from continuously growing task-agnostic storage. The model is trained to maximize the expected agent's performance by selecting promising trajectories solving prior tasks from the storage. We show that such retrospective exploration can accelerate the learning process of the MBRL agent by better informing learned dynamics and prompting agent with exploratory trajectories. We test the performance of our approach on several domains from the DeepMind control suite, from Metaworld multitask benchmark, and from our bespoke environment implemented with a robotic NVIDIA Isaac simulator to test the ability of the model to act in a photorealistic, ray-traced environment.
PREC: semantic translation of property graphs

Converting property graphs to RDF graphs allows to enhance the interoperability of knowledge graphs. But existing tools perform the same conversion for every graph, regardless of its content. In this paper, we propose PREC, a user-configured conversion of property graphs to RDF graphs to better capture the semantics of the content.
Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching

Despite the remarkable progress of deep learning in stereo matching, there exists a gap in accuracy between real-time models and slower state-of-the-art models which are suitable for practical applications. This paper presents an iterative multi-scale coarse-to-fine refinement (iCFR) framework to bridge this gap by allowing it to adopt any stereo matching network to make it fast, more efficient and scalable while keeping comparable accuracy. To reduce the computational cost of matching, we use multi-scale warped features to estimate disparity residuals and push the disparity search range in the cost volume to a minimum limit. Finally, we apply a refinement network to recover the loss of precision which is inherent in multi-scale approaches. We test our iCFR framework by adopting the matching networks from state-of-the art GANet and AANet. The result is 49$\times$ faster inference time compared to GANetdeep and 4$\times$ less memory consumption, with comparable error. Our best performing network, which we call FRSNet is scalable even up to an input resolution of 6K on a GTX 1080Ti, with inference time still below one second and comparable accuracy to AANet+. It out-performs all real-time stereo methods and achieves competitive accuracy on the KITTI benchmark.
Self-Supervised Knowledge Transfer via Loosely Supervised Auxiliary Tasks

Knowledge transfer using convolutional neural networks (CNNs) can help efficiently train a CNN with fewer parameters or maximize the generalization performance under limited supervision. To enable a more efficient transfer of pretrained knowledge under relaxed conditions, we propose a simple yet powerful knowledge transfer methodology without any restrictions regarding the network structure or dataset used, namely self-supervised knowledge transfer (SSKT), via loosely supervised auxiliary tasks. For this, we devise a training methodology that transfers previously learned knowledge to the current training process as an auxiliary task for the target task through self-supervision using a soft label. The SSKT is independent of the network structure and dataset, and is trained differently from existing knowledge transfer methods; hence, it has an advantage in that the prior knowledge acquired from various tasks can be naturally transferred during the training process to the target task. Furthermore, it can improve the generalization performance on most datasets through the proposed knowledge transfer between different problem domains from multiple source networks. SSKT outperforms the other transfer learning methods (KD, DML, and MAXL) through experiments under various knowledge transfer settings. The source code will be made available to the public.
Conditional Generation of Periodic Signals with Fourier-Based Decoder

Periodic signals play an important role in daily lives. Although conventional sequential models have shown remarkable success in various fields, they still come short in modeling periodicity; they either collapse, diverge or ignore details. In this paper, we introduce a novel framework inspired by Fourier series to generate periodic signals. We first decompose the given signals into multiple sines and cosines and then conditionally generate periodic signals with the output components. We have shown our model efficacy on three tasks: reconstruction, imputation and conditional generation. Our model outperforms baselines in all tasks and shows more stable and refined results.