Reviewerly/exHarmony huggingface dataset:
Multi-scale V-net architecture with deep feature CRF layers for brain extraction

Authors:
raw_author_name: Shreyas Fadnavis
raw_affiliation_string: Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
raw_author_name: Eleftherios Garyfallidis
raw_affiliation_string: Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, USA

Abstract: Abstract Background Brain extraction is a computational necessity for researchers using brain imaging data. However, the complex structure of the interfaces between the brain, meninges and human skull have not allowed a highly robust solution to emerge. While previous methods have used machine learning with structural and geometric priors in mind, with the development of Deep Learning (DL), there has been an increase in Neural Network based methods. Most proposed DL models focus on improving the training data despite the clear gap between groups in the amount and quality of accessible training data between. Methods We propose an architecture we call Efficient V-net with Additional Conditional Random Field Layers (EVAC+). EVAC+ has 3 major characteristics: (1) a smart augmentation strategy that improves training efficiency, (2) a unique way of using a Conditional Random Fields Recurrent Layer that improves accuracy and (3) an additional loss function that fine-tunes the segmentation output. We compare our model to state-of-the-art non-DL and DL methods. Results Results show that even with limited training resources, EVAC+ outperforms in most cases, achieving a high and stable Dice Coefficient and Jaccard Index along with a desirable lower Surface (Hausdorff) Distance. More importantly, our approach accurately segmented clinical and pediatric data, despite the fact that the training dataset only contains healthy adults. Conclusions Ultimately, our model provides a reliable way of accurately reducing segmentation errors in complex multi-tissue interfacing areas of the brain. We expect our method, which is publicly available and open-source, to be beneficial to a wide range of researchers.
SFL-MDrone: Synchronous federated learning enabled multi drones

Authors:
raw_author_name: Itika Sharma
raw_affiliation_string: School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Katra, (Jammu & Kashmir), UT, India
raw_author_name: Sachin Kumar Gupta
raw_affiliation_string: Department of Electronics and Communication Engineering, Central University of Jammu, Samba, Jammu, (UT of J&K), India

Abstract: UAVs or Drones can be used to support wireless communication by acting as flying or mobile Base Stations for the accumulation of the different types of data to train the models. However, in traditional or DL-based UAVs, the raw data is sent from the devices to the centralized server, which causes problems in terms of the privacy of the devices and the UAVs’ communication resources or limited processing. Therefore, the issue with DL-based UAVs is that sending the original data to the centralized body raises questions about security and privacy. The transmission of distributed, unprocessed data from the drones to the cloud, including interactive media information data types, requires a significant amount of network bandwidth and more energy, which has an enormous effect on several trade-offs, including communication rates and computation latencies. Data packet loss caused by asynchronous transmission, which doesn’t prevent peer-to-peer communication, is a concern with AFL-based UAVs. Therefore, in order to address the aforementioned issues, we have introduced SFL-based UAVs that focus on creating algorithms in which the models simultaneously update the server as they wait for all of the chosen devices to communicate. The proposed framework enables a variety of devices, including mobile and UAV devices, to train or learn their algorithms for machine learning before updating the models and parameters simultaneously to servers or manned aerial data centers for model buildup without transferring their original private information. This decreases packet loss and privacy threats while also enhancing round effectiveness as well as model accuracy. The comparative analysis of AFL and SFL techniques in terms of accuracy, global rounds, and communication rounds are offered. Simulation findings suggest that the proposed methodology improves in terms of global rounds and accuracy.
Stabilizer Subsystem Decompositions for Single- and Multimode Gottesman-Kitaev-Preskill Codes

Authors:
raw_author_name: Andrew C. Doherty
raw_affiliation_string: ARC Centre of Excellence for Engineered Quantum Systems, School of Physics, The University of Sydney, Sydney, NSW 2006, Australia
raw_author_name: Arne L. Grimsmo
raw_affiliation_string: ARC Centre of Excellence for Engineered Quantum Systems, School of Physics, The University of Sydney, Sydney, NSW 2006, Australia; AWS Center for Quantum Computing, Pasadena, California 91125, USA; California Institute of Technology, Pasadena, California 91125, USA

Abstract: The Gottesman-Kitaev-Preskill (GKP) error-correcting code encodes a finite-dimensional logical space in one or more bosonic modes, and has recently been demonstrated in trapped ions and superconducting microwave cavities. In this work we introduce a new subsystem decomposition for GKP codes that we call the stabilizer subsystem decomposition, analogous to the usual approach to quantum stabilizer codes. The decomposition has the defining property that a partial trace over the nonlogical stabilizer subsystem is equivalent to an ideal decoding of the logical state, distinguishing it from previous GKP subsystem decompositions. We describe how to decompose arbitrary states across the subsystem decomposition using a set of transformations that move between the decompositions of different GKP codes. Besides providing a convenient theoretical view on GKP codes, such a decomposition is also of practical use. We use the stabilizer subsystem decomposition to efficiently simulate noise acting on single-mode GKP codes, and in contrast to more conventional Fock basis simulations, we are able to consider essentially arbitrarily large photon numbers for realistic noise channels, such as loss and dephasing.5 MoreReceived 9 December 2022Revised 28 July 2023Accepted 10 January 2024DOI:https://doi.org/10.1103/PRXQuantum.5.010331Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum error correctionQuantum harmonic oscillatorQuantum simulationQuantum Information, Science & Technology
Data-driven discovery of statistically relevant information in quantum simulators

Authors:
raw_author_name: V. Vitale
raw_affiliation_string: SISSA—International School of Advanced Studies, Via Bonomea 265, 34136 Trieste, Italy; The Abdus Salam International Centre for Theoretical Physics (ICTP), Strada Costiera 11, 34151 Trieste, Italy; Université Grenoble Alpes, CNRS, Laboratoire de Physique et Modélisation des Milieux Condensés (LPMMC), Grenoble 38000, France
raw_author_name: E. D. Donkor
raw_affiliation_string: SISSA—International School of Advanced Studies, Via Bonomea 265, 34136 Trieste, Italy; The Abdus Salam International Centre for Theoretical Physics (ICTP), Strada Costiera 11, 34151 Trieste, Italy
raw_author_name: A. Rodriguez
raw_affiliation_string: Dipartimento di Matematica e Geoscienze, Universitá degli Studi di Trieste, via Alfonso Valerio 12/1, 34127, Trieste, Italy; The Abdus Salam International Centre for Theoretical Physics (ICTP), Strada Costiera 11, 34151 Trieste, Italy
raw_author_name: H. Strobel
raw_affiliation_string: Kirchhoff-Institut für Physik, Universität Heidelberg, Im Neuenheimer Feld 227, 69120 Heidelberg, Germany
raw_author_name: M. K. Oberthaler
raw_affiliation_string: Kirchhoff-Institut für Physik, Universität Heidelberg, Im Neuenheimer Feld 227, 69120 Heidelberg, Germany
raw_author_name: M. Dalmonte
raw_affiliation_string: SISSA—International School of Advanced Studies, Via Bonomea 265, 34136 Trieste, Italy; The Abdus Salam International Centre for Theoretical Physics (ICTP), Strada Costiera 11, 34151 Trieste, Italy

Abstract: Quantum simulators offer powerful means to investigate strongly correlated quantum matter. However, interpreting measurement outcomes in such systems poses significant challenges. Here, we present a theoretical framework for information extraction in synthetic quantum matter, illustrated for the case of a quantum quench in a spinor Bose-Einstein condensate experiment. Employing nonparametric unsupervised learning tools that provide different measures of information content, we demonstrate a theory-agnostic approach to identify dominant degrees of freedom. This enables us to rank operators according to their relevance, akin to effective field theory. To characterize the corresponding effective description, we then explore the intrinsic dimension of data sets as a measure of the complexity of the dynamics. This reveals a simplification of the data structure, which correlates with the emergence of time-dependent universal behavior in the studied system. Our assumption-free approach can be immediately applied in a variety of experimental platforms.
Style-Guided Adversarial Teacher for Cross-Domain Object Detection

Authors:
raw_author_name: Longfei Jia
raw_affiliation_string: Qingshuihe Campus, University of Electronic Science and Technology of China, Chengdu 611731, China
raw_author_name: Xianlong Tian
raw_affiliation_string: Qingshuihe Campus, University of Electronic Science and Technology of China, Chengdu 611731, China
raw_author_name: Yuguo Hu
raw_affiliation_string: Qingshuihe Campus, University of Electronic Science and Technology of China, Chengdu 611731, China
raw_author_name: Mengmeng Jing
raw_affiliation_string: Qingshuihe Campus, University of Electronic Science and Technology of China, Chengdu 611731, China
raw_author_name: Lin Zuo
raw_affiliation_string: Qingshuihe Campus, University of Electronic Science and Technology of China, Chengdu 611731, China
raw_author_name: Wen Li
raw_affiliation_string: Qingshuihe Campus, University of Electronic Science and Technology of China, Chengdu 611731, China

Abstract: The teacher–student framework is widely employed for cross-domain object detection. However, it suffers from two problems. One is that large distribution discrepancies will cause critical performance drops. The other is that the samples that deviate from the overall distributions of both domains will greatly mislead the model. To solve these problems, we propose a style-guided adversarial teacher (SGAT) method for domain adaptation. Specifically, on the domain level, we generate target-like images based on source images to effectively narrow the gaps between domains. On the sample level, we denoise samples by estimating the probability density ratio of the ‘target-style’ and target distributions, which could filter out the unrelated samples and highlight the related ones. In this way, we could guarantee reliable samples. With these reliable samples, we learn the domain-invariant features through teacher–student mutual learning and adversarial learning. Extensive experiments verify the effectiveness of our method. In particular, we achieve 52.9% mAP on Clipart1k and 42.7% on Comic2k, which are 6.4% and 5.0% higher than the compared baselines.
Improved Wildlife Recognition through Fusing Camera Trap Images and Temporal Metadata

Authors:
raw_author_name: Lei Liu
raw_affiliation_string: Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China; School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
raw_author_name: Chao Mou
raw_affiliation_string: Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China; School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
raw_author_name: Fu Xu
raw_affiliation_string: Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China; School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China

Abstract: Camera traps play an important role in biodiversity monitoring. An increasing number of studies have been conducted to automatically recognize wildlife in camera trap images through deep learning. However, wildlife recognition by camera trap images alone is often limited by the size and quality of the dataset. To address the above issues, we propose the Temporal-SE-ResNet50 network, which aims to improve wildlife recognition accuracy by exploiting the temporal information attached to camera trap images. First, we constructed the SE-ResNet50 network to extract image features. Second, we obtained temporal metadata from camera trap images, and after cyclical encoding, we used a residual multilayer perceptron (MLP) network to obtain temporal features. Finally, the image features and temporal features were fused in wildlife identification by a dynamic MLP module. The experimental results on the Camdeboo dataset show that the accuracy of wildlife recognition after fusing the image and temporal information is about 93.10%, which is an improvement of 0.53%, 0.94%, 1.35%, 2.93%, and 5.98%, respectively, compared with the ResNet50, VGG19, ShuffleNetV2-2.0x, MobileNetV3-L, and ConvNeXt-B models. Furthermore, we demonstrate the effectiveness of the proposed method on different national park camera trap datasets. Our method provides a new idea for fusing animal domain knowledge to further improve the accuracy of wildlife recognition, which can better serve wildlife conservation and ecological research.
Adaptive weighted dictionary representation using anchor graph for subspace clustering

Authors:
raw_author_name: Wenyi Feng
raw_affiliation_string: Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, PR China; Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, 200237, PR China; Qinghai University, Xining, 810016, PR China
raw_author_name: Zhe Wang
raw_affiliation_string: Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, PR China; Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, 200237, PR China
raw_author_name: Ting Xiao
raw_affiliation_string: Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, PR China; Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, 200237, PR China
raw_author_name: Mengping Yang
raw_affiliation_string: Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, PR China; Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, 200237, PR China

Abstract: Samples are commonly represented as sparse vectors in many dictionary representation algorithms. However, this method may result in loss of discriminatory information. Moreover, a redundant dictionary can increase the computational complexity of the algorithm. To tackle these challenges, we propose a novel method named Adaptive Weighted Dictionary Representation using Anchor Graph for Subspace Clustering (AWDR). First, AWDR constructs an anchor graph that encodes the classification information and establishes accurate connectivity components between anchors and clusters, thereby fully utilizing the discriminative information of the original samples. In addition, AWDR learns a complete-dictionary in the subspace to eliminate the noise and out-of-sample effects of the original sample space, while also improving computational efficiency. Finally, AWDR computes the coefficients for the samples in an adaptively weighted manner to find discriminative representation of the samples from the dictionary. Extensive experiments on real-world datasets demonstrate that our method is effective and efficient compared to the state-of-the-art methods.
Multi-Modal Sarcasm Detection with Sentiment Word Embedding

Authors:
raw_author_name: Hao Fu
raw_affiliation_string: Department of Computer Science, Shantou University, Shantou 515063, China
raw_author_name: Hao Liu
raw_affiliation_string: Department of Computer Science, Shantou University, Shantou 515063, China
raw_author_name: Hongling Wang
raw_affiliation_string: Shenzhen Tobo Software Co., Ltd., Shenzhen 518055, China
raw_author_name: Linyan Xu
raw_affiliation_string: Department of Computer Science, Shantou University, Shantou 515063, China
raw_author_name: Jiali Lin
raw_affiliation_string: Bussiness School, Shantou University, Shantou 515063, China
raw_author_name: Dazhi Jiang
raw_affiliation_string: Department of Computer Science, Shantou University, Shantou 515063, China

Abstract: Sarcasm poses a significant challenge for detection due to its unique linguistic phenomenon where the intended meaning is often opposite of the literal expression. Current sarcasm detection technology primarily utilizes multi-modal processing, but the connotative semantic information provided by the modality itself is limited. It is a challenge to mine the semantic information contained in the combination of sarcasm samples and external commonsense knowledge. Furthermore, as the essence of sarcasm detection lies in measuring emotional inconsistency, the rich semantic information may introduce excessive noise to inconsistency measurement. To mitigate these limitations, we propose a hierarchical framework in this paper. Specifically, to enrich the semantic information of each modality, our approach uses sentiment dictionaries to obtain the sentiment vectors by evaluating the words extracted from various modalities, and then combines them with each modality. Furthermore, in order to mine the joint semantic information implied in the modalities and improve measurement of emotional inconsistency, the emotional information representation obtained by fusing each modality’s data is concatenated with the sentiment vector. Then, cross-modal fusion is performed through cross-attention, and, finally, the sarcasm is recognized by fusing low-level information in the cross-modal fusion layer. Our model is evaluated on a public multi-modal sarcasm detection dataset based on Twitter, and the results demonstrate its superiority.
Efficiency optimization in quantum computing: balancing thermodynamics and computational performance

Authors:
raw_author_name: Tomasz Śmierzchalski
raw_affiliation_string: Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Ba ltycka 5, Gliwice, 44-100, Poland.
raw_author_name: Zakaria Mzaouali
raw_affiliation_string: Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Ba ltycka 5, Gliwice, 44-100, Poland.
raw_author_name: Sebastian Deffner
raw_affiliation_string: Department of Physics, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
raw_author_name: Bartłomiej Gardas
raw_affiliation_string: Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Ba ltycka 5, Gliwice, 44-100, Poland.

Abstract: We investigate the computational efficiency and thermodynamic cost of the D-Wave quantum annealer under reverse-annealing with and without pausing. Our demonstration on the D-Wave 2000Q annealer shows that the combination of reverse-annealing and pausing leads to improved computational efficiency while minimizing the thermodynamic cost compared to reverse-annealing alone. Moreover, we find that the magnetic field has a positive impact on the performance of the quantum annealer during reverse-annealing but becomes detrimental when pausing is involved. Our results, which are reproducible, provide strategies for optimizing the performance and energy consumption of quantum annealing systems employing reverse-annealing protocols.
ViT-UperNet: a hybrid vision transformer with unified-perceptual-parsing network for medical image segmentation

Authors:
raw_author_name: Liu Kun
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qianwangang Road, Qingdao, 266590, Shandong, China
raw_author_name: Xu Shaohua
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qianwangang Road, Qingdao, 266590, Shandong, China
raw_author_name: Yin Jian
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qianwangang Road, Qingdao, 266590, Shandong, China
raw_author_name: Zhang Zhen
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qianwangang Road, Qingdao, 266590, Shandong, China

Abstract: Abstract The existing image semantic segmentation models have low accuracy in detecting tiny targets or multi-targets at overlapping regions. This work proposes a hybrid vision transformer with unified-perceptual-parsing network (ViT-UperNet) for medical image segmentation. A self-attention mechanism is embedded in a vision transformer to extract multi-level features. The image features are extracted hierarchically from low to high dimensions using 4 groups of Transformer blocks with different numbers. Then, it uses a unified-perceptual-parsing network based on a feature pyramid network (FPN) and a pyramid pooling module (PPM) for the fusion of multi-scale contextual features and semantic segmentation. FPN can naturally use hierarchical features, and generate strong semantic information on all scales. PPM can better use the global prior knowledge to understand complex scenes, and extract features with global context information to improve segmentation results. In the training process, a scalable self-supervised learner named masked autoencoder is used for pre-training, which strengthens the visual representation ability and improves the efficiency of the feature learning. Experiments are conducted on cardiac magnetic resonance image segmentation where the left and right atrium and ventricle are selected for segmentation. The pixels accuracy is 93.85%, the Dice coefficient is 92.61% and Hausdorff distance is 11.16, which are improved compared with the other methods. The results show the superiority of Vit-UperNet in medical images segmentation, especially for the low-recognition and serious-occlusion targets.
Towards data privacy in a fog of things

Authors:
raw_author_name: Cassio Prazeres
raw_affiliation_string: Institute of Computing, Federal University of Bahia Bahia Brazil

Abstract: Abstract Data from the Internet of Things (IoT) devices enable the design of new business models and services, improving user experience and satisfaction. However, these devices also collect personal data and place them on centralized servers without transparency and user control, enhancing data privacy concerns (such as identification, localization and tracking, profiling, and linkage). We propose the Fog of Things (FoT)‐Personal Data Stores (PDS) paradigm, an extension of the (FoT) paradigm tailored to protect personal data privacy in the IoT, specifically in the FoT. We propose a data decentralization solution through Personal Data Stores, empowering users with control over their data. We present an architectural framework based on this concept and an implementation of a usage scenario.