Reviewerly/exHarmony huggingface dataset:
Reconfigurable Silicon Photonic Chip for the Generation Of Frequency-Bin-Entangled Qudits

Quantum optical microcombs in integrated ring resonators generate entangled photon pairs over many spectral modes, and allow the preparation of high-dimensional qudit states. Ideally, those sources should be programmable and have a high generation rate, with comb lines tightly spaced for the implementation of efficient qudit gates based on electro-optic frequency mixing. While these requirements cannot all be satisfied by a single resonator device, for which there is a trade-off between the high generation rate and tight bin spacing, a promising strategy is the use of multiple resonators, each generating photon pairs in specific frequency bins via spontaneous four-wave mixing. Based on this approach we present a programmable silicon photonics device for the generation of frequency-bin-entangled qudits, in which bin spacing, qudit dimension, and the bipartite quantum state can be reconfigured on chip. Using resonators with a radius of $22\phantom{\rule{0.2em}{0ex}}\text{\ensuremath{\mu}}\mathrm{m}$, we achieve a high brightness [about $\mathrm{MHz}/(\mathrm{mW}{)}^{2}$] per comb line with a bin spacing of 15 GHz, and fidelities above 85% with maximally entangled Bell states up to a Hilbert space dimension of 16. By individually addressing each spectral mode, we realize states that cannot be generated on chip using a single resonator. We measure the correlation matrices of maximally entangled two-qubit and two-qutrit states on a set of mutually unbiased bases, finding fidelities exceeding 98%, and indicating that the source can find application in high-dimensional secure communication protocols.
Counterfactuals and the logic of causal selection.

Everything that happens has a multitude of causes, but people make causal judgments effortlessly. How do people select one particular cause (e.g., the lightning bolt that set the forest ablaze) out of the set of factors that contributed to the event (the oxygen in the air, the dry weather … )? Cognitive scientists have suggested that people make causal judgments about an event by simulating alternative ways things could have happened. We argue that this counterfactual theory explains many features of human causal intuitions, given two simple assumptions. First, people tend to imagine counterfactual possibilities that are both a priori likely and similar to what actually happened. Second, people judge that a factor C caused effect E if C and E are highly correlated across these counterfactual possibilities. In a reanalysis of existing empirical data, and a set of new experiments, we find that this theory uniquely accounts for people's causal intuitions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
MedKPL: A heterogeneous knowledge enhanced prompt learning framework for transferable diagnosis

Artificial Intelligence (AI) based diagnosis systems have emerged as powerful tools to reform traditional medical care. Each clinician now wants to have his own intelligent diagnostic partner to expand the range of services he can provide. However, the implementation of intelligent decision support systems based on clinical note has been hindered by the lack of extensibility of end-to-end AI diagnosis algorithms. When reading a clinical note, expert clinicians make inferences with relevant medical knowledge, which serve as prompts for making accurate diagnoses. Therefore, external medical knowledge is commonly employed as an augmentation for medical text classification tasks. Existing methods, however, cannot integrate knowledge from various knowledge sources as prompts nor can fully utilize explicit and implicit knowledge. To address these issues, we propose a Medical Knowledge-enhanced Prompt Learning (MedKPL) diagnostic framework for transferable clinical note classification. Firstly, to overcome the heterogeneity of knowledge sources, such as knowledge graphs or medical QA databases, MedKPL uniform the knowledge relevant to the disease into text sequences of fixed format. Then, MedKPL integrates medical knowledge into the prompt designed for context representation. Therefore, MedKPL can integrate knowledge into the models to enhance diagnostic performance and effectively transfer to new diseases by using relevant disease knowledge. The results of our experiments on two medical datasets demonstrate that our method yields superior medical text classification results and performs better in cross-departmental transfer tasks under few-shot or even zero-shot settings. These findings demonstrate that our MedKPL framework has the potential to improve the interpretability and transferability of current diagnostic systems.
Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

This review article presents recent advancements in deep learning methodologies and applications for autonomous navigation.It analyzes state-of-the-art deep learning frameworks used in tasks like signal processing, attitude estimation, obstacle detection, scene perception, and path planning.The implementation and testing methodologies of these approaches are critically evaluated, highlighting their strengths, limitations, and areas for further development.The review emphasizes the interdisciplinary nature of autonomous navigation and addresses challenges posed by dynamic and complex environments, uncertainty, and obstacles.With a particular focus on mobile robots, self-driving cars, unmanned aerial vehicles, and space vehicles to underscore the importance of navigation in these domains.By synthesizing findings from multiple studies, the review aims to be a valuable resource for researchers and practitioners, contributing to the advancement of novel approaches.Key aspects covered include the classification of deep learning applications, recent advancements in methods, general applications in the field, innovations, challenges, and limitations associated with learning-based navigation systems.This review also explores current research trends and future directions in the field.This extensive overview, initiated in 2020, provides a valuable resource for researchers of all levels, from seasoned experts to newcomers.Its main purpose is to streamline the process of identifying, evaluating, and interpreting relevant research, ultimately contributing to the progress and development of autonomous navigation technologies.
YOLO-GW: Quickly and Accurately Detecting Pedestrians in a Foggy Traffic Environment

In practice, the object detection algorithm is limited by a complex detection environment, hardware costs, computing power, and chip running memory. The performance of the detector will be greatly reduced during operation. Determining how to realize real-time, fast, and high-precision pedestrian recognition in a foggy traffic environment is a very challenging problem. To solve this problem, the dark channel de-fogging algorithm is added to the basis of the YOLOv7 algorithm, which effectively improves the de-fogging efficiency of the dark channel through the methods of down-sampling and up-sampling. In order to further improve the accuracy of the YOLOv7 object detection algorithm, the ECA module and a detection head are added to the network to improve object classification and regression. Moreover, an 864 × 864 network input size is used for model training to improve the accuracy of the object detection algorithm for pedestrian recognition. Then the combined pruning strategy was used to improve the optimized YOLOv7 detection model, and finally, the optimization algorithm YOLO-GW was obtained. Compared with YOLOv7 object detection, YOLO-GW increased Frames Per Second (FPS) by 63.08%, mean Average Precision (mAP) increased by 9.06%, parameters decreased by 97.66%, and volume decreased by 96.36%. Smaller training parameters and model space make it possible for the YOLO-GW target detection algorithm to be deployed on the chip. Through analysis and comparison of experimental data, it is concluded that YOLO-GW is more suitable for pedestrian detection in a fog environment than YOLOv7.
Oases at the Gates of Hell: Hydrogeology, Cultural History and Ecology of the Mulligan River Springs, Far Western Queensland

The Mulligan River springs occur on the eastern edge of the Simpson Desert in far south-west Queensland, near the north-west margin of the Great Artesian Basin, and are associated with the Toomba Thrust Fault.The springs provide the only permanent surface water in the driest part of Australia.They have been focal points for human and animal activity for millennia, but despite their cultural and ecological interest, they have received relatively little attention compared to other Great Artesian Basin spring groups.Here we explore the hydrogeology, cultural history and ecology of these springs through a review of published literature, early explorer journals, diaries and letters of early settlers, books, and comprehensive field survey.Fragments of stories and dense surface archaeology indicate intensive occupation at many of the springs by the Wangkamadla people for thousands of years, but most of the knowledge about how people used, mythologised and managed the springs did not survive the frontier period that saw the area depopulated.From the 1880s, explorers and pastoralists marvelled at, relied upon and in many cases severely modified the springs.Shallow bores were sunk on or near springs, and others were excavated to improve cattle access.Today, all except three of 90 documented springs remain active, although many are highly modified and reductions in flow and wetland extent due to aquifer drawdown are likely to have occurred.No endemic species are known to be associated with the Mulligan River springs, but they support disjunct populations of some plants and fish.There appears to be considerable natural dynamism in spring activity and flow, but springs in some areas have emerged or become reactivated, apparently due to increased aquifer pressure following bore capping.Additional springs were found during the most recent surveys, and a small number probably remain undocumented.Improved understanding of recharge areas, aquifer connectivity and spring dynamism will inform future management of these isolated oases, while detailed archaeological work will shed light on patterns of Aboriginal use and better situate the springs in the wider cultural landscape.
A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection

With the rapid developments in electronic commerce and digital payment technologies, credit card transactions have increased significantly. Machine learning (ML) has been vital in analyzing customer data to detect and prevent fraud. However, the presence of redundant and irrelevant features in most real-world credit card data degrades the performance of ML classifiers. This study proposes a hybrid feature-selection technique consisting of filter and wrapper feature-selection steps to ensure that only the most relevant features are used for machine learning. The proposed method uses the information gain (IG) technique to rank the features, and the top-ranked features are fed to a genetic algorithm (GA) wrapper, which uses the extreme learning machine (ELM) as the learning algorithm. Meanwhile, the proposed GA wrapper is optimized for imbalanced classification using the geometric mean (G-mean) as the fitness function instead of the conventional accuracy metric. The proposed approach achieved a sensitivity and specificity of 0.997 and 0.994, respectively, outperforming other baseline techniques and methods in the recent literature.
Blockchain and Artificial Intelligence as Enablers of Cyber Security in the Era of IoT and IIoT Applications

The fifth revolution of the industrial era—or Industry 5 [...]
Proposal for practical Rydberg quantum gates using a native two-photon excitation

Abstract Rydberg quantum gate serving as an indispensable computing unit for neutral-atom quantum computation, has attracted intense research efforts for the last decade. However, the state-of-the-art experiments have not reached the high gate fidelity as predicted by most theories due to the unexpected large loss remaining in Rydberg and intermediate states. In this paper, we report our findings in constructing a native two-qubit controlled-NOT gate based on pulse optimization. We focus on the method of commonly-used two-photon Rydberg excitation with smooth Gaussian-shaped pulses which is straightforward for experimental demonstration. By utilizing optimized pulse shapes the scheme reveals a remarkable reduction in the decays from Rydberg and intermediate states, as well as a high-tolerance to the residual thermal motion of atoms. We extract a conservative lower bound for the gate fidelity <?CDATA ${\gt} 0.9921$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"><mml:mrow><mml:mo>&gt;</mml:mo></mml:mrow><mml:mn>0.9921</mml:mn></mml:math> after taking into account the experimental imperfections. Our results not only reduce the gap between experimental and theoretical prediction because of the optimal control, but also facilitate the connectivity of distant atomic qubits in a larger atom array by reducing the requirement of strong blockade, which is promising for developing multiqubit quantum computation in large-scale atomic arrays.
I’m Me, We’re Us, and I’m Us: Tri-directional Contrastive Learning on Hypergraphs

Although machine learning on hypergraphs has attracted considerable attention, most of the works have focused on (semi-)supervised learning, which may cause heavy labeling costs and poor generalization. Recently, contrastive learning has emerged as a successful unsupervised representation learning method. Despite the prosperous development of contrastive learning in other domains, contrastive learning on hypergraphs remains little explored. In this paper, we propose TriCL (Tri-directional Contrastive Learning), a general framework for contrastive learning on hypergraphs. Its main idea is tri-directional contrast, and specifically, it aims to maximize in two augmented views the agreement (a) between the same node, (b) between the same group of nodes, and (c) between each group and its members. Together with simple but surprisingly effective data augmentation and negative sampling schemes, these three forms of contrast enable TriCL to capture both node- and group-level structural information in node embeddings. Our extensive experiments using 14 baseline approaches, 10 datasets, and two tasks demonstrate the effectiveness of TriCL, and most noticeably, TriCL almost consistently outperforms not just unsupervised competitors but also (semi-)supervised competitors mostly by significant margins for node classification. The code and datasets are available at https://github.com/wooner49/TriCL.
LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception

LiDAR-based 3D object detection, semantic segmentation, and panoptic segmentation are usually implemented in specialized networks with distinctive architectures that are difficult to adapt to each other. This paper presents LidarMultiNet, a LiDAR-based multi-task network that unifies these three major LiDAR perception tasks. Among its many benefits, a multi-task network can reduce the overall cost by sharing weights and computation among multiple tasks. However, it typically underperforms compared to independently combined single-task models. The proposed LidarMultiNet aims to bridge the performance gap between the multi-task network and multiple single-task networks. At the core of LidarMultiNet is a strong 3D voxel-based encoder-decoder architecture with a Global Context Pooling (GCP) module extracting global contextual features from a LiDAR frame. Task-specific heads are added on top of the network to perform the three LiDAR perception tasks. More tasks can be implemented simply by adding new task-specific heads while introducing little additional cost. A second stage is also proposed to refine the first-stage segmentation and generate accurate panoptic segmentation results. LidarMultiNet is extensively tested on both Waymo Open Dataset and nuScenes dataset, demonstrating for the first time that major LiDAR perception tasks can be unified in a single strong network that is trained end-to-end and achieves state-of-the-art performance. Notably, LidarMultiNet reaches the official 1 place in the Waymo Open Dataset 3D semantic segmentation challenge 2022 with the highest mIoU and the best accuracy for most of the 22 classes on the test set, using only LiDAR points as input. It also sets the new state-of-the-art for a single model on the Waymo 3D object detection benchmark and three nuScenes benchmarks.
Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations

Knowledge tracing (KT) is a crucial technique to predict students’ future performance by observing their historical learning processes. Due to the powerful representation ability of deep neural networks, remarkable progress has been made by using deep learning techniques to solve the KT problem. The majority of existing approaches rely on the homogeneous question assumption that questions have equivalent contributions if they share the same set of knowledge components. Unfortunately, this assumption is inaccurate in real-world educational scenarios. Furthermore, it is very challenging to interpret the prediction results from the existing deep learning based KT models. Therefore, in this paper, we present QIKT, a question-centric interpretable KT model to address the above challenges. The proposed QIKT approach explicitly models students’ knowledge state variations at a ﬁne-grained level with question-sensitive cognitive representations that are jointly learned from a question-centric knowledge acquisition module and a question-centric problem solving module. Meanwhile, the QIKT utilizes an item response theory based prediction layer to generate interpretable prediction results. The proposed QIKT model is evaluated on three public real-world educational datasets. The results demonstrate that our approach is superior on the KT prediction task, and it outperforms a wide range of deep learning based KT models in terms of prediction accuracy with better model interpretability. To encourage reproducible results, we have provided all the datasets and code at https://pykt.org/.
Carburacy: Summarization Models Tuning and Comparison in Eco-Sustainable Regimes with a Novel Carbon-Aware Accuracy

Generative transformer-based models have reached cutting-edge performance in long document summarization. Nevertheless, this task is witnessing a paradigm shift in developing ever-increasingly computationally-hungry solutions, focusing on effectiveness while ignoring the economic, environmental, and social costs of yielding such results. Accordingly, such extensive resources impact climate change and raise barriers to small and medium organizations distinguished by low-resource regimes of hardware and data. As a result, this unsustainable trend has lifted many concerns in the community, which directs the primary efforts on the proposal of tools to monitor models' energy costs. Despite their importance, no evaluation measure considering models' eco-sustainability exists yet. In this work, we propose Carburacy, the first carbon-aware accuracy measure that captures both model effectiveness and eco-sustainability. We perform a comprehensive benchmark for long document summarization, comparing multiple state-of-the-art quadratic and linear transformers on several datasets under eco-sustainable regimes. Finally, thanks to Carburacy, we found optimal combinations of hyperparameters that let models be competitive in effectiveness with significantly lower costs.
Demystify the Gravity Well in the Optimization Landscape (Student Abstract)

We provide both empirical and theoretical insights to demystify the gravity well phenomenon in the optimization landscape. We start from describe the problem setup and theoretical results (an escape time lower bound) of the Softmax Gravity Well (SGW) in the literature. Then we move toward the understanding of a recent observation called ASR gravity well. We provide an explanation of why normal distribution with high variance can lead to suboptimal plateaus from an energy function point of view. We also contribute to the empirical insights of curriculum learning by comparison of policy initialization by different normal distributions. Furthermore, we provide the ASR escape time lower bound to understand the ASR gravity well theoretically. Future work includes more specific modeling of the reward as a function of time and quantitative evaluation of normal distribution’s influence on policy initialization.
An Improved YOLOv7 Lightweight Detection Algorithm for Obscured Pedestrians

The detection algorithm commonly misses obscured pedestrians in traffic scenes with a high pedestrian density because mutual occlusion among pedestrians reduces the prediction box score of the concealed pedestrians. The paper uses the YOLOv7 algorithm as the baseline and makes the following three improvements by investigating the variables influencing the detection method’s performance: First, the backbone network of the YOLOv7 algorithm is replaced with the lightweight feature extraction network Mobilenetv3 since the pedestrian detection algorithm frequently needs to be deployed in driverless mobile, which requires a fast operating speed of the algorithm; second, a high-resolution feature pyramid structure is suggested for the issue of missed detection of hidden pedestrians, which upscales the feature maps generated from the feature pyramid to increase the resolution of the output feature maps and introduces shallow feature maps to strengthen the distinctions between adjacent sub-features to enhance the network’s ability to extract features for the visible area of hidden pedestrians and small-sized pedestrians in order to produce deeper features with greater differentiation for pedestrians; and the third is to suggest a detection head based on an attention mechanism that is employed to lower the confidence level of target neighboring sub-features, lower the quantity of redundant detection boxes, and lower the following NMS computation. The mAP of the suggested approach in this work achieves 89.75%, which is 9.5 percentage points better than the YOLOv7 detection algorithm, according to experiments on the CrowdHuman pedestrian-intensive dataset. The algorithm proposed in this paper can considerably increase the detection performance of the detection algorithm, particularly for obscured pedestrians and small-sized pedestrians in the dataset, according to the experimental effect plots.
Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning

Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes. Most related prior works are on incremental object detection that rely on the availability of abundant training samples per novel class that substantially limits the scalability to real-world setting where novel data can be scarce. In this paper, we propose the Incremental-DETR that does incremental few-shot object detection via fine-tuning and self-supervised learning on the DETR object detector. To alleviate severe over-fitting with few novel class data, we first fine-tune the class-specific components of DETR with self-supervision from additional object proposals generated using Selective Search as pseudo labels. We further introduce an incremental few-shot fine-tuning strategy with knowledge distillation on the class-specific components of DETR to encourage the network in detecting novel classes without forgetting the base classes. Extensive experiments conducted on standard incremental object detection and incremental few-shot object detection settings show that our approach significantly outperforms state-of-the-art methods by a large margin. Our source code is available at https://github.com/dongnana777/Incremental-DETR.