Reviewerly/exHarmony huggingface dataset:
Scaling Superconducting Quantum Computers with Chiplet Architectures

Fixed-frequency transmon quantum computers (QCs) have advanced in coherence times, addressability, and gate fidelities. Unfortunately, these devices are restricted by the number of on-chip qubits, capping processing power and slowing progress toward fault-tolerance. Although emerging transmon devices feature over 100 qubits, building QCs large enough for meaningful demonstrations of quantum advantage requires overcoming many design challenges. For example, today’s transmon qubits suffer from significant variation due to limited precision in fabrication. As a result, barring significant improvements in current fabrication techniques, scaling QCs by building ever larger individual chips with more qubits is hampered by device variation. Severe device variation that degrades QC performance is referred to as a defect. Here, we focus on a specific defect known as a frequency collision. When transmon frequencies collide, their difference falls within a range that limits two-qubit gate fidelity. Frequency collisions occur with greater probability on larger QCs, causing collision-free yields to decline as the number of on-chip qubits increases. As a solution, we propose exploiting the higher yields associated with smaller QCs by integrating quantum chiplets within quantum multi-chip modules (MCMs). Yield, gate performance, and application-based analysis show the feasibility of QC scaling through modularity. Our results demonstrate that chiplet architectures, relative to monolithic designs, benefit from average yield improvements ranging from 9.6 – 92.6 × for ≲5 qubit machines. In addition, our simulations explore the design space of chiplet systems and discover configurations that demonstrate average two-qubit gate infidelity reductions that are at best 0.815 × their monolithic counterpart. Finally, we observe that carefully-selected modular systems achieve fidelity improvements on a range of benchmark circuits.
Architecture for Museums Location-Based Content Delivery using Augmented Reality and Beacons

The digital transformation applied to museum's context is somehow usual nowadays, presenting many benefits to the visitors. However, most of the applications provide static and basic information that is not appealing nor promotes the involvement of the visitor in the tour. This paper proposes an architecture of a location-based content delivery inside a museum, based on a dynamic approach that promotes the visitor to immerse in the tour's story. The solution was created as a response to a real case study for the Foz Cöa museum in Portugal. The proposed solution uses beacons, for the indoor location of visitors, and augmented reality for providing contents based on what the user is seeing, following a narrative that captures his attention at all times and providing artifact's details that the naked eye can not see. A comparative study was made to determine the properties and compare two augmented reality tools: Immersal AR and EasyAR. The precision of beacons detection, under different conditions and configurations, was also presented as preliminary results of this work.
Collaborative group embedding and decision aggregation based on attentive influence of individual members: A group recommendation perspective

A key group decision making task is to aggregate individual preferences. Conventional group decision methods adopt pre-defined and fixed strategies to aggregate individuals' preferences, which can be ineffective due to the varying importance and influence of individual group members. Recent studies have proposed to assign different weights to individual members automatically based on the level of consistency of their ratings with group assessment outcomes. However, they ignored the high-order influence relationship among individual group members on group decision making. In this study, from a group recommendation perspective, we propose a novel collaborative Group Embedding and Decision Aggregation (GEDA) approach by leveraging the graph neural network technique to address those limitations. Specifically, GEDA first deploys a graph convolution operation on user-item interaction and group-item interaction graphs to generate embedding representations of members, groups, and items. A novel multi-attention (MA) module then learns each member's decision weight by simultaneously considering the relationships among members for aggregating individual preferences into group preferences. The empirical evaluation using two real-world datasets demonstrates the advantage of the proposed GEDA model over the state-of-the-art group recommendation models.
Advances in online handwritten recognition in the last decades

The easy availability and rapid use of online devices like Take note, PDA, smartphones, etc. at an affordable price increase the demand for online handwriting recognition. In this recognition approach, people can provide information through those devices as freely as they are habituated with pen and paper. The advantage of using those devices is that the supplied information is directly stored as timely ordered stroke sequences.The information does not contain noises that may arise in offline recognition while scanning the paper filled up with information. Such advantages make online handwriting recognition a hot research topic over offline recognition. Certain factors affect writing on electronic devices, including the size, speed of writing, shape, angle of letter used, and type of medium, which in turn affect the recognition performance. In this paper, we have addressed various machine learning and deep learning-based approaches along with their performance for recognizing online handwritten characters, words, and texts in diverse scripts.We have elaborately discussed various feature extraction techniques used by the authors following machine learning approaches and described different deep learning architectures for recognition purposes. We have also discussed the advantages and challenges faced by the methodologies for online handwriting recognition and we believe that the findings of the survey will be informative to researchers.
Bending as Counterspeech

Abstract In this paper, we identify and examine an overlooked strategy to counter bigoted speech on the spot. Such a strategy we call ‘bending’. To ‘bend’, in our sense, is to deliberately give a distorted response to a speaker’s harmful move – precisely, an ameliorative response, which may turn that move into a different, less harmful, contribution. To substantiate our proposal, we distinguish two ideas of uptake – interpretation and response – and argue for the general claim that a distorted response on the hearer’s part may end up transforming a speaker’s contribution. Patterns of distortion have been analyzed in the literature as unjustly undermining speakers’ agency and exacerbating oppression. Our analysis shows that, under certain circumstances, distortion can be employed to derail bigoted speech and thus serve the purposes of social justice. We close by discussing the virtues and limits of bending vis-à-vis a different, much-discussed, counterspeech strategy, i.e. ‘blocking’ (Langton 2018).
Combining Evolution and Deep Reinforcement Learning for Policy Search: A Survey

Deep neuroevolution and deep Reinforcement Learning have received a lot of attention over the past few years. Some works have compared them, highlighting their pros and cons, but an emerging trend combines them so as to benefit from the best of both worlds. In this article, we provide a survey of this emerging trend by organizing the literature into related groups of works and casting all the existing combinations in each group into a generic framework. We systematically cover all easily available papers irrespective of their publication status, focusing on the combination mechanisms rather than on the experimental results. In total, we cover 45 algorithms more recent than 2017. We hope this effort will favor the growth of the domain by facilitating the understanding of the relationships between the methods, leading to deeper analyses, outlining missing useful comparisons and suggesting new combinations of mechanisms.
Simulability of High-Dimensional Quantum Measurements

We investigate the compression of quantum information with respect to a given set $\mathcal{M}$ of high-dimensional measurements. This leads to a notion of simulability, where we demand that the statistics obtained from $\mathcal{M}$ and an arbitrary quantum state $\rho$ are recovered exactly by first compressing $\rho$ into a lower dimensional space, followed by some quantum measurements. A full quantum compression is possible, i.e., leaving only classical information, if and only if the set $\mathcal{M}$ is jointly measurable. Our notion of simulability can thus be seen as a quantification of measurement incompatibility in terms of dimension. After defining these concepts, we provide an illustrative examples involving mutually unbiased basis, and develop a method based on semi-definite programming for constructing simulation models. In turn we analytically construct optimal simulation models for all projective measurements subjected to white noise or losses. Finally, we discuss how our approach connects with other concepts introduced in the context of quantum channels and quantum correlations.
FairTraDEX

We present FairTraDEX, a decentralized exchange (DEX) protocol based on frequent batch auctions (FBAs), which provides formal game-theoretic guarantees against extractable value. FBAs, when run by a trusted third-party, ensure that the unique game-theoretic optimal strategy for all players is to trade at the true market-implied price of the underlying token swap, excluding explicit, pre-determined fees. FairTraDEX replicates the key features of an FBA that provide these game-theoretic guarantees using a combination of set-membership in zero-knowledge protocols and an escrow-enforced commit-reveal mechanism. We extend the results of FBAs to handle monopolistic and/or malicious liquidity providers. We provide real-world examples that demonstrate that the costs of executing orders in existing academic and industry-standard protocols become prohibitive as order size increases due to basic value extraction techniques, popularized as maximal extractable value. We further demonstrate that FairTraDEX protects against these execution costs, guaranteeing a fixed fee model independent of order size, the first guarantee of it's kind for a DEX protocol. We also provide detailed Solidity and pseudo-code implementations of FairTraDEX, making FairTraDEX a novel and practical contribution.
Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module

Multimodal sentiment analysis (MSA), which supposes to improve text-based sentiment analysis with associated acoustic and visual modalities, is an emerging research area due to its potential applications in Human-Computer Interaction (HCI). However, existing researches observe that the acoustic and visual modalities contribute much less than the textual modality, termed as text-predominant. Under such circumstances, in this work, we emphasize making non-verbal cues matter for the MSA task. Firstly, from the resource perspective, we present the CH-SIMS v2.0 dataset, an extension and enhancement of the CH-SIMS. Compared with the original dataset, the CH-SIMS v2.0 doubles its size with another 2121 refined video segments containing both unimodal and multimodal annotations and collects 10161 unlabelled raw video segments with rich acoustic and visual emotion-bearing context to highlight non-verbal cues for sentiment prediction. Secondly, from the model perspective, benefiting from the unimodal annotations and the unsupervised data in the CH-SIMS v2.0, the Acoustic Visual Mixup Consistent (AV-MC) framework is proposed. The designed modality mixup module can be regarded as an augmentation, which mixes the acoustic and visual modalities from different videos. Through drawing unobserved multimodal context along with the text, the model can learn to be aware of different non-verbal contexts for sentiment prediction. Our evaluations demonstrate that both CH-SIMS v2.0 and AV-MC framework enable further research for discovering emotion-bearing acoustic and visual cues and pave the path to interpretable end-to-end HCI applications for real-world scenarios. The full dataset and code are available for use at https://github.com/thuiar/ch-sims-v2.
The DeepMotion entry to the GENEA Challenge 2022

This paper describes the method and evaluation results of our DeepMotion entry to the GENEA Challenge 2022. One difficulty in data-driven gesture synthesis is that there may be multiple viable gesture motions for the same speech utterance. Therefore the deterministic regression methods can not resolve the conflicting samples and may produce more damped motions. We proposed a two-stage model to address this uncertainty issue in gesture synthesis. Inspired by recent text-to-image synthesis methods, our gesture synthesis system utilizes a VQ-VAE model to first extract smaller gesture units as codebook vectors from training data. An autoregressive model based on GPT-2 transformer is then applied to model the probability distribution on the discrete latent space of VQ-VAE. The user evaluation results show the proposed method is able to produce gesture motions with reasonable human-likeness and gesture appropriateness.
VLCAP: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning

In this paper, we leverage the human perceiving process, that involves vision and language interaction, to generate a coherent paragraph description of untrimmed videos. We propose vision-language (VL) features consisting of two modalities, i.e., (i) vision modality to capture global visual content of the entire scene and (ii) language modality to extract scene elements description of both human and non-human objects (e.g. animals, vehicles, etc), visual and non-visual elements (e.g. relations, activities, etc). Furthermore, we propose to train our proposed VLCap under a contrastive learning VL loss. The experiments and ablation studies on ActivityNet Captions and YouCookII datasets show that our VLCap outperforms existing SOTA methods on both accuracy and diversity metrics. Source code: https://github.com/UARK-AICV/VLCAP
Computationally-Efficient Vision Transformer for Medical Image Semantic Segmentation Via Dual Pseudo-Label Supervision

Ubiquitous accumulation of large volumes of data, and increased availability of annotated medical data in particular, has made it possible to show the many and varied benefits of deep learning to the semantic segmentation of medical images. Nevertheless, data access and annotation come at a high cost in clinician time. The power of Vision Transformer (ViT) is well-documented for generic computer vision tasks involving millions of images of every day objects, of which only relatively few have been annotated. Its translation to relatively more modest (i.e. thousands of images of) medical data is not immediately straightforward. This paper presents practical avenues for training a Computationally-Efficient Semi-Supervised Vision Transformer (CESS-ViT) for medical image segmentation task.We propose a self-attention-based image segmentation network which requires only limited computational resources. Additionally, we develop a dual pseudo-label supervision scheme for use with semi-supervision in a simple pure ViT.Our method has been evaluated on a publicly available cardiac MRI dataset with direct comparison against other semi-supervised methods. Our results illustrate the proposed ViT-based semi-supervised method outperforms the existing methods in the semantic segmentation of cardiac ventricles.
ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results

Manual examination of faecal smear samples to identify the existence of parasitic eggs is very time-consuming and can only be done by specialists.Therefore, an automated system is required to tackle this problem since it can relate to serious intestinal parasitic infections.This paper reviews the ICIP 2022 Challenge on parasitic egg detection and classification in microscopic images.We describe a new dataset for this application, which is the largest dataset of its kind.The methods used by participants in the challenge are summarised and discussed along with their results.
Depthformer: Multiscale Vision Transformer for Monocular Depth Estimation with Global Local Information Fusion

Attention-based models such as transformers have shown outstanding performance on dense prediction tasks, such as semantic segmentation, owing to their capability of capturing long-range dependency in an image. However, the benefit of transformers for monocular depth prediction has seldom been explored so far. This paper benchmarks var-ious transformer-based models for the depth estimation task on an indoor NYUV2 dataset and an outdoor KITTI dataset. We propose a novel attention-based architecture, Depthformer for monocular depth estimation that uses multi-head self-attention to produce the multiscale feature maps, which are effectively combined by our proposed de-coder network. We also propose a Transbins module that divides the depth range into bins whose center value is estimated adaptively per image. The final depth estimated is a linear combination of bin centers for each pixel. Trans-bins module takes advantage of the global receptive field using the transformer module in the encoding stage. Experimental results on NYUV2 and KITTI depth estimation benchmark demonstrate that our proposed method improves the state-of-the-art by 3.3%, and 3.3% respectively in terms of Root Mean Squared Error (RMSE). Code is available at https://github.com/ashutosh1807/Depthformer.git.
Spatial Sensitive GRAD-CAM: Visual Explanations for Object Detection by Incorporating Spatial Sensitivity

Visual explanations are important to increase models’ transparency. Grad-CAM [1] is an effective method because of its high class discrimination, no requirement of architectural changes, and so on. However, in detection tasks, because Grad-CAM only focuses on the importance of features but does not have spatial sensitivity, it generates heatmaps in which not related regions to detected objects are also highlighted. In this study, we propose Spatial Sensitive Grad-CAM (SSGrad-CAM), which can generate appropriate heatmaps for object detectors. SSGrad-CAM modifies the heatmap generated from Grad-CAM with space maps computed by normalizing the magnitude of gradients. In this manner, SSGrad-CAM can incorporate spatial sensitivity and focus on the importance of both features and space. Through experiments, we confirm SSGrad-CAM can generate appropriate heatmaps for detection results, and also confirm it can generate when models detect objects by paying high attention to their peripheral regions, as well.
Temporally Precise Action Spotting in Soccer Videos Using Dense Detection Anchors

We present a model for temporally precise action spotting in videos, which uses a dense set of detection anchors, predicting a detection confidence and corresponding fine-grained temporal displacement for each anchor. We experiment with two trunk architectures, both of which are able to incorporate large temporal contexts while preserving the smaller-scale features required for precise localization: a one-dimensional version of a u-net, and a Transformer encoder (TE). We also suggest best practices for training models of this kind, by applying Sharpness-Aware Minimization (SAM) and mixup data augmentation. We achieve a new state-of-the-art on SoccerNet-v2, the largest soccer video dataset of its kind, with marked improvements in temporal localization. Additionally, our ablations show: the importance of predicting the temporal displacements; the trade-offs between the u-net and TE trunks; and the benefits of training with SAM and mixup.
Balanced Affinity Loss for Highly Imbalanced Baggage Threat Contour-Driven Instance Segmentation

Autonomous detection of threat items from baggage X-ray imagery is one of the most vital and challenging tasks. Manual detection of these items is a cumbersome, slow, and error-ridden process which is also limited by the examination capacity of the security inspector. To overcome these limitations, many researchers have proposed deep learning-driven approaches to recognize suspicious objects from the baggage X-ray scans. However, threat items are rarely seen in the real world compared to innocuous baggage content. Therefore, when trained with imbalanced data, the performance of the conventional threat detection models drastically decreases. This paper addresses these issues with a contour-driven instance segmentation model optimized with a novel combined loss function, dubbed balanced affinity loss function. In addition to mitigating the class imbalance, this function best handles the fine-grained classification aspect inferred by contours and the instance segmentation. We validated the proposed system on three public baggage X-ray datasets, where it outperformed state-of-the-art methods by 7.76%, 25.81%, and 8.78% in terms of intersection-over-union score.
Temperature guided network for 3D joint segmentation of the pancreas and tumors

Accurate and automatic segmentation of pancreatic tumors and organs from medical images is important for clinical diagnoses and making treatment plans for patients with pancreatic cancer. Although deep learning methods have been widely adopted for this task, the segmentation accuracy, especially for pancreatic tumors, still needs to be further improved because (1) phenotypic differences, such as volumes, tend to make the models focus on pancreatic learning, resulting in insufficient tumor feature selection; (2) deep learning models may fall into local optima, leading to unsatisfactory segmentation results for tumors and pancreas. To alleviate the above issues, in this paper, we propose a 3D fully convolutional neural network with three temperature guided modules, namely, balance temperature loss, rigid temperature optimizer and soft temperature indictor, to realize joint segmentation of the pancreas and tumors. Specifically, balance temperature loss is designed to dynamically adjust the learning points between tumors and the pancreas to balance the selected features, and it is aimed at improving the accuracy of tumor segmentation without losing pancreas information. Rigid temperature optimizer is proposed to accept nonimproving moves probabilistically to adaptively avoid local optima. To further refine the segmentation results, we propose the soft temperature indictor to guide the network into a fine-tuning state automatically when the model tends to stability. Our experimental results are more accurate than the fourteen top-ranking methods in pancreas and tumors segmentation on the MSD pancreas dataset and six top-ranking methods in brain tumors segmentation. Ablation studies verify the effectiveness of the three temperature guided modules.