Reviewerly/exHarmony huggingface dataset:
RAW-GNN: RAndom Walk Aggregation based Graph Neural Network

Graph-Convolution-based methods have been successfully applied to representation learning on homophily graphs where nodes with the same label or similar attributes tend to connect with one another. Due to the homophily assumption of Graph Convolutional Networks (GCNs) that these methods use, they are not suitable for heterophily graphs where nodes with different labels or dissimilar attributes tend to be adjacent. Several methods have attempted to address this heterophily problem, but they do not change the fundamental aggregation mechanism of GCNs because they rely on summation operators to aggregate information from neighboring nodes, which is implicitly subject to the homophily assumption. Here, we introduce a novel aggregation mechanism and develop a RAndom Walk Aggregation-based Graph Neural Network (called RAW-GNN) method. The proposed approach integrates the random walk strategy with graph neural networks. The new method utilizes breadth-first random walk search to capture homophily information and depth-first search to collect heterophily information. It replaces the conventional neighborhoods with path-based neighborhoods and introduces a new path-based aggregator based on Recurrent Neural Networks. These designs make RAW-GNN suitable for both homophily and heterophily graphs. Extensive experimental results showed that the new method achieved state-of-the-art performance on a variety of homophily and heterophily graphs.
Heisenberg-Limited Metrology via Weak-Value Amplification without Using Entangled Resources

Weak-value amplification (WVA) provides a way for amplified detection of a tiny physical signal at the expense of a lower detection probability. Despite this trade-off, due to its robustness against certain types of noise, WVA has advantages over conventional measurements in precision metrology. Moreover, it has been shown that WVA-based metrology can reach the Heisenberg limit using entangled resources, but preparing macroscopic entangled resources remains challenging. Here, we demonstrate a novel WVA scheme based on iterative interactions, achieving the Heisenberg-limited precision scaling without resorting to entanglement. This indicates that the perceived advantages of the entanglement-assisted WVA are in fact due to iterative interactions between each particle of an entangled system and a meter, rather than coming from the entanglement itself. Our work opens a practical pathway for achieving the Heisenberg-limited WVA without using fragile and experimentally demanding entangled resources.
Are Vision-Language Transformers Learning Multimodal Representations? A Probing Perspective

In recent years, joint text-image embeddings have significantly improved thanks to the development of transformer-based Vision-Language models. Despite these advances, we still need to better understand the representations produced by those models. In this paper, we compare pre-trained and fine-tuned representations at a vision, language and multimodal level. To that end, we use a set of probing tasks to evaluate the performance of state-of-the-art Vision-Language models and introduce new datasets specifically for multimodal probing. These datasets are carefully designed to address a range of multimodal capabilities while minimizing the potential for models to rely on bias. Although the results confirm the ability of Vision-Language models to understand color at a multimodal level, the models seem to prefer relying on bias in text data for object position and size. On semantically adversarial examples, we find that those models are able to pinpoint fine-grained multimodal differences. Finally, we also notice that fine-tuning a Vision-Language model on multimodal tasks does not necessarily improve its multimodal ability. We make all datasets and code available to replicate experiments.
Stagnation Detection Meets Fast Mutation

Two mechanisms have recently been proposed that can significantly speed up finding distant improving solutions via mutation, namely using a random mutation rate drawn from a heavy-tailed distribution ("fast mutation", Doerr et al. (2017)) and increasing the mutation strength based on a stagnation detection mechanism (Rajabi and Witt (2020)). Whereas the latter can obtain the asymptotically best probability of finding a single desired solution in a given distance, the former is more robust and performs much better when many improving solutions in some distance exist. In this work, we propose a mutation strategy that combines ideas of both mechanisms. We show that it can also obtain the best possible probability of finding a single distant solution. However, when several improving solutions exist, it can outperform both the stagnation-detection approach and fast mutation. The new operator is more than an interleaving of the two previous mechanisms and it outperforms any such interleaving.
Fingerprints of Qubit Noise in Transient Cavity Transmission

Noise affects the coherence of qubits and thereby places a bound on the performance of quantum computers. We theoretically study a generic two-level system with fluctuating control parameters in a photonic cavity and find that basic features of the noise spectral density are imprinted in the transient transmission through the cavity. We obtain analytical expressions for generic noise and proceed to study the cases of quasistatic, white and 1/f^{α} noise in more detail. Additionally, we propose a way of extracting the noise power spectral density in a frequency band only bounded by the range of the qubit-cavity detuning and with an exponentially decaying error due to finite measurement times. Our results suggest that measurements of the time-dependent transmission probability represent a novel way of extracting noise characteristics.
Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation

Existing continual relation learning (CRL) methods rely on plenty of labeled training data for learning a new task, which can be hard to acquire in real scenario as getting large and representative labeled data is often expensive and time-consuming.It is therefore necessary for the model to learn novel relational patterns with very few labeled data while avoiding catastrophic forgetting of previous task knowledge.In this paper, we formulate this challenging yet practical problem as continual few-shot relation learning (CFRL).Based on the finding that learning for new emerging few-shot tasks often results in feature distributions that are incompatible with previous tasks' learned distributions, we propose a novel method based on embedding space regularization and data augmentation.Our method generalizes to new fewshot tasks and avoids catastrophic forgetting of previous tasks by enforcing extra constraints on the relational embeddings and by adding extra relevant data in a self-supervised manner.With extensive experiments we demonstrate that our method can significantly outperform previous state-of-the-art methods in CFRL task settings. 1
Early-exit deep neural networks for distorted images: providing an efficient edge offloading

Edge offloading for deep neural networks (DNNs) can be adaptive to the input&#x0027;s complexity by using early-exit DNNs. These DNNs have side branches throughout their architecture, allowing the inference to end earlier in the edge. The branches estimate the accuracy for a given input. If this estimated accuracy reaches a threshold, the inference ends on the edge. Otherwise, the edge offloads the inference to the cloud to process the remaining DNN layers. However, DNNs for image classification deals with distorted images, which negatively impact the branches&#x0027; estimated accuracy. Consequently, the edge offloads more inferences to the cloud. This work introduces expert side branches trained on a particular distortion type to improve robustness against image distortion. The edge detects the distortion type and selects appropriate expert branches to perform the inference. This approach increases the estimated accuracy on the edge, improving the offloading decisions. We validate our proposal in a realistic scenario, in which the edge offloads DNN inference to Amazon EC2 instances.
How to Simulate Quantum Measurement without Computing Marginals

We describe and analyze algorithms for classically simulating measurement of an n-qubit quantum state in the standard basis, that is, sampling a bit string from the probability distribution determined by the Born rule. Our algorithms reduce the sampling task to computing poly(n) amplitudes of n-qubit states; unlike previously known techniques they do not require computation of marginal probabilities. Two classes of quantum states are considered: output states of polynomial-size quantum circuits, and ground states of local Hamiltonians with an inverse polynomial spectral gap. We show that our algorithms can significantly accelerate quantum circuit simulations based on tensor network contraction or low-rank stabilizer decompositions. As another striking consequence we obtain the first efficient classical simulation algorithm for measurement-based quantum computation with the surface code resource state on any planar graph and any schedule of measurements.
Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks

Retrieval-augmented generation models have shown state-of-the-art performance across many knowledge-intensive NLP tasks such as open-domain question answering and fact verification.These models are trained to generate a final output given retrieved passages that can be irrelevant to an input query, leading to learning spurious cues or memorization.This work introduces a method to incorporate evidentiality of passages-whether a passage contains correct evidence to support the outputinto training the generator.We introduce a multi-task learning framework to jointly generate the final output and predict the evidentiality of each passage.Furthermore, we introduce a new task-agnostic method for obtaining high-quality silver evidentiality labels, addressing the issues of gold evidentiality labels being unavailable in most domains.Our experiments on five datasets across three knowledgeintensive tasks show that our new evidentialityguided generator significantly outperforms its direct counterpart on all of them, and advances the state of the art on three of them.Our analysis shows that the multi-task learning and silver evidentiality mining play key roles.
Rule-Based Classification Based on Ant Colony Optimization: A Comprehensive Review

The Ant Colony Optimization (ACO) algorithms have been well-studied by the Operations Research community for solving combinatorial optimization problems. A handful of researchers in the Data Science community have successfully implemented various ACO methodologies for rule-based classification. This family of ACO algorithms is referred to as AntMiner algorithms. Due to the flexibility of the framework, and the availability of alternative strategies at the modular level, a systematic review on the AntMiner algorithms can benefit the broader community of researchers and practitioners interested in highly interpretable classification techniques. In this paper, we provided a comprehensive review of each module of the AntMiner algorithms. Our motivation is to provide insight into the current practices and future research scope in the context of the rule-based classification. Our discussions address ACO methodologies, rule construction strategies, candidate selection metrics, rule quality evaluation functions, rule pruning strategies, methods to address continuous attributes, parameter selection, and experimental settings. This review also reports a summary of real-life implementations of the rule-based classifiers in diverse domains including medical, genetics, portfolio analysis, geographic information system (GIS), human-machine interaction (HMI), autonomous driving, ICT, quality, and reliability engineering. These implementations demonstrate the potential application domains that can be benefitted from the methodological contributions to the rule-based classification technique.
Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity

Recent self-supervised video representation learning methods have found significant success by exploring essential properties of videos, e.g. speed, temporal order, etc. This work exploits an essential yet under-explored property of videos, the \textit{video continuity}, to obtain supervision signals for self-supervised representation learning. Specifically, we formulate three novel continuity-related pretext tasks, i.e. continuity justification, discontinuity localization, and missing section approximation, that jointly supervise a shared backbone for video representation learning. This self-supervision approach, termed as Continuity Perception Network (CPNet), solves the three tasks altogether and encourages the backbone network to learn local and long-ranged motion and context representations. It outperforms prior arts on multiple downstream tasks, such as action recognition, video retrieval, and action localization. Additionally, the video continuity can be complementary to other coarse-grained video properties for representation learning, and integrating the proposed pretext task to prior arts can yield much performance gains.
A Survey of the Analysis of Complex Systems based on Complex Network Theory and Deep Learning

From the perspective of complex network theory, complex systems can be characterized by the interaction of microscopic units through nonlinear effects, yielding macroscopic emergent behavior.In light of the powerful capability of deep learning in feature extraction and model fitting from large amount of datasets, we try to overview the benefits of combining the complex network analysis with deep learning techniques to investigate complex systems.We first explore the existence of complexity in complex systems.In what followed, we first give a brief description of complex network theory.Then, we present an overview of deep learning technology.Subsequently, we focus on the research advances and applications in the analysis of complex systems based on complex network theory and deep learning.The last section is further discussion and prospects for the combination of these two methods.In a nutshell, the development of deep learning combined with complex network theory allows for exploring the complexity in complex systems at a higher level.
Few-Shot Semantic Parsing with Language Models Trained on Code

Large language models can perform semantic parsing with little training data, when prompted with in-context examples.It has been shown that this can be improved by formulating the problem as paraphrasing into canonical utterances, which casts the underlying meaning representation into a controlled natural languagelike representation.Intuitively, such models can more easily output canonical utterances as they are closer to the natural language used for pre-training.Recently, models also pretrained on code, like OpenAI Codex, have risen in prominence.For semantic parsing tasks where we map natural language into code, such models may prove more adept at it.In this paper, we test this hypothesis and find that Codex performs better on such tasks than equivalent GPT-3 models.We evaluate on Overnight and SMCalFlow and find that unlike GPT-3, Codex performs similarly when targeting meaning representations directly, perhaps because meaning representations are structured similar to code in these datasets.
Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases.Magnetic resonance imaging (MRI) is a widely utilized tool for the classification and detection of prostate cancer.Since the manual screening process of prostate cancer is difficult, automated diagnostic methods become essential.This study develops a novel Deep Learning based Prostate Cancer Classification (DTL-PSCC) model using MRI images.The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors.In addition, the fuzzy k-nearest neighbour (FKNN) model is utilized for classification process where the class labels are allotted to the input MRI images.Moreover, the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm (KHA) which results in improved classification performance.In order to demonstrate the good classification outcome of the DTL-PSCC technique, a wide range of simulations take place on benchmark MRI datasets.The extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%.
Metaverses for Нuman or Human for Metaverses

The article researches the grounds on which the leading technology companies seek to transform the mankind’ way of life of and the nature of the &amp;quot;human&amp;quot;, using the idea of &amp;quot;metaverses&amp;quot; as a promising project for the development of the Internet. The authors compare this idea with the concept of the &amp;quot;Panopticon&amp;quot; and, as a result of the analysis, draw attention to the opportunities and the threats that metauniverses pose for humanity in the conditions of uncontrolled development of technologies. The authors come to the conclusion that for successful development it is necessary to form and disseminate a new socio-humanitarian rationality, which will allow to ensure control over the activities of technology companies.
Deconfounded Visual Grounding

We focus on the confounding bias between language and location in the visual grounding pipeline, where we find that the bias is the major visual reasoning bottleneck. For example, the grounding process is usually a trivial languagelocation association without visual reasoning, e.g., grounding any language query containing sheep to the nearly central regions, due to that most queries about sheep have ground-truth locations at the image center. First, we frame the visual grounding pipeline into a causal graph, which shows the causalities among image, query, target location and underlying confounder. Through the causal graph, we know how to break the grounding bottleneck: deconfounded visual grounding. Second, to tackle the challenge that the confounder is unobserved in general, we propose a confounder-agnostic approach called: Referring Expression Deconfounder (RED), to remove the confounding bias. Third, we implement RED as a simple language attention, which can be applied in any grounding method. On popular benchmarks, RED improves various state-of-the-art grounding methods by a significant margin. Code is available at: https://github.com/JianqiangH/Deconfounded_VG.
Distributed aggregative optimization with quantized communication

In this paper, we focus on an aggregative optimization problem under the communication bottleneck.The aggregative optimization is to minimize the sum of local cost functions.Each cost function depends on not only local state variables but also the sum of functions of global state variables.The goal is to solve the aggregative optimization problem through distributed computation and local efficient communication over a network of agents without a central coordinator.Using the variable tracking method to seek the global state variables and the quantization scheme to reduce the communication cost spent in the optimization process, we develop a novel distributed quantized algorithm, called D-QAGT, to track the optimal variables with finite bits communication.Although quantization may lose transmitting information, our algorithm can still achieve the exact optimal solution with linear convergence rate.Simulation experiments on an optimal placement problem is carried out to verify the correctness of the theoretical results.
Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction

Software security is a very important aspect for software development organizations who wish to provide high-quality and dependable software to their consumers. A crucial part of software security is the early detection of software vulnerabilities. Vulnerability prediction is a mechanism that facilitates the identification (and, in turn, the mitigation) of vulnerabilities early enough during the software development cycle. The scientific community has recently focused a lot of attention on developing Deep Learning models using text mining techniques for predicting the existence of vulnerabilities in software components. However, there are also studies that examine whether the utilization of statically extracted software metrics can lead to adequate Vulnerability Prediction Models. In this paper, both software metrics- and text mining-based Vulnerability Prediction Models are constructed and compared. A combination of software metrics and text tokens using deep-learning models is examined as well in order to investigate if a combined model can lead to more accurate vulnerability prediction. For the purposes of the present study, a vulnerability dataset containing vulnerabilities from real-world software products is utilized and extended. The results of our analysis indicate that text mining-based models outperform software metrics-based models with respect to their F2-score, whereas enriching the text mining-based models with software metrics was not found to provide any added value to their predictive performance.
Augmenting Ear Accessories for Facial Gesture Input Using Infrared Distance Sensor Array

Simple hands-free input methods using ear accessories have been proposed to broaden the range of scenarios in which information devices can be operated without hands. Although many previous studies use canal-type earphones, few studies focused on the following two points: (1) A method applicable to ear accessories other than canal-type earphones. (2) A method enabling various ear accessories with different styles to have the same hands-free input function. To realize these two points, this study proposes a method to recognize the user’s facial gesture using an infrared distance sensor attached to the ear accessory. The proposed method detects skin movement around the ear and face, which differs for each facial expression gesture. We created a prototype system for three ear accessories for the root of the ear, earlobe, and tragus. The evaluation results for nine gestures and 10 subjects showed that the F-value of each device was 0.95 or more, and the F-value of the pattern combining multiple devices was 0.99 or more, which showed the feasibility of the proposed method. Although many ear accessories could not interact with information devices, our findings enable various ear accessories with different styles to have eye-free and hands-free input ability based on facial gestures.