pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning Enabled Semantic Communication Systems for Video Transmission
Authors: Zhenguo Zhang, Qianqian Yang, Shibo He, Jiming Chen
Date: 2023-12-08 00:00:00
Tasks: Deep Learning, Optical Flow Estimation, Semantic Communication

Semantic communication has emerged as a promising approach for improving efficient transmission in the next generation of wireless networks. Inspired by the success of semantic communication in different areas, we aim to provide a new semantic communication scheme from the semantic level. In this paper, we propose a novel DL-based semantic communication system for video transmission, which compacts semantic-related information to improve transmission efficiency. In particular, we utilize the Bi-optical flow to estimate residual information of inter-frame details. We also propose a feature choice module and a feature fusion module to drop semantically redundant features while paying more attention to the important semantic-related content. We employ a frame prediction module to reconstruct semantic features of the prediction frame from the received signal at the receiver. To enhance the system's robustness, we propose a noise attention module that assigns different importance weights to the extracted features. Simulation results indicate that our proposed method outperforms existing approaches in terms of transmission efficiency, achieving about 33.3\% reduction in the number of transmitted symbols while improving the peak signal-to-noise ratio (PSNR) performance by an average of 0.56dB.
Transcriptome-supervised classification of tissue morphology using deep learning
Authors: Axel Andersson, Gabriele Partel, Leslie Solorzano, Carolina Wählby
Date: 2023-12-07 00:00:00
Tasks: Deep Learning

Deep learning has proven to successfully learn variations in tissue and cell morphology. Training of such models typically relies on expensive manual annotations. Here we conjecture that spatially resolved gene expression, e.i., the transcriptome, can be used as an alternative to manual annotations. In particular, we trained five convolutional neural networks with patches of different size extracted from locations defined by spatially resolved gene expression. The network is trained to classify tissue morphology related to two different genes, general tissue, as well as background, on an image of fluorescence stained nuclei in a mouse brain coronal section. Performance is evaluated on an independent tissue section from a different mouse brain, reaching an average Dice score of 0.51. Results may indicate that novel techniques for spatially resolved transcriptomics together with deep learning may provide a unique and unbiased way to find genotype-phenotype relationships.
Characterizing the Narrowband Ambiguity Function of Multi-Tone Sinusoidal Frequency Modulated Waveforms
Authors: David A. Hague
Date: 2023-12-08 00:00:00

This paper characterizes the mainlobe and sidelobe structure of the Multi-Tone Sinusoidal Frequency Modulated (MTSFM) transmit waveform's narrowband Ambiguity Function (AF) for active sonar applications. The MTSFM waveform's modulation function is represented as a Fourier series. The Fourier coefficients form a discrete set of parameters that are modified to synthesize waveforms with novel characteristics. The contour of the AF's mainlobe is well approximated as a coupled ellipse known as the Ellipse of Ambiguity (EOA). The EOA parameters determine whether a waveform is Doppler sensitive or Doppler tolerant. This paper derives exact closed form expressions for the EOA parameters of the MTSFM's AF. The MTSFM's design coefficients allow for fine control of its AF mainlobe width in range and Doppler as well as its RDCF. This fine control facilitates designing waveforms that can smoothly trade-off between possessing Doppler sensitive and Doppler tolerant characteristics. Additionally, this paper introduces a method to control the sidelobe structure of the MTSFM's Auto Correlation Function (ACF) while maintaining the waveform's AF mainlobe shape. This is achieved using a numerical optimization technique that minimizes the ratio of $\ell_2$-norms of the ACF mainlobe and sidelobe regions subject to constraints on the EOA parameters. Simulations demonstrate the effectiveness of this optimization technique.
Fractional Chirp-Slope-Shift-Keying for SDR-based Search and Rescue Applications
Authors: Daniel Gil Gaviria, Marcus Müller, Felix Artmann, Laurent Schmalen
Date: 2023-12-08 00:00:00

The use of modern software-defined radio (SDR) devices enables the implementation of efficient communication systems in numerous scenarios. Such technology comes especially handy in the context of search and rescue (SAR) systems, enabling the incorporation of additional communication data transmission into the otherwise sub-optimally used SAR bands at 121.5 and 243~MHz. In this work, we propose a novel low-complexity, energy-efficient modulation scheme that allows transmission of additional data within chirped homing signals, while still meeting the standards of international SAR systems such as COSPAS-SARSAT. The proposed method modulates information onto small deviations of the chirp slope with respect to the required unmodulated chirp, which can be easily detected at the receiver side using digital signal processing.
MagHT: a Magnetic Hough Transform for Fast Indoor Place Recognition
Authors: Iad Abdul Raouf, Vincent Gay-Bellile, Steve Bourgeois, Cyril Joly, Alexis Paljic
Date: 2023-12-08 00:00:00

This article proposes a novel indoor magnetic field-based place recognition algorithm that is accurate and fast to compute. For that, we modified the generalized ''Hough Transform'' to process magnetic data (MagHT). It takes as input a sequence of magnetic measures whose relative positions are recovered by an odometry system and recognizes the places in the magnetic map where they were acquired. It also returns the global transformation from the coordinate frame of the input magnetic data to the magnetic map reference frame. Experimental results on several real datasets in large indoor environments demonstrate that the obtained localization error, recall, and precision are similar to or are better than state-of-the-art methods while improving the runtime by several orders of magnitude. Moreover, unlike magnetic sequence matching-based solutions such as DTW, our approach is independent of the path taken during the magnetic map creation.
Low Noise Inverse Magnetoelectric Magnetic Field Sensor
Authors: L. Thormählen, P. Hayes, E. Elzenheimer, E. Spetzler, J. McCord, G. Schmidt, M. Höft, D. Meyners, E. Quandt
Date: 2023-12-08 00:00:00

In the development of any type of magnetic field sensor based on magnetic films, special consideration must be given to the magnetic layer component. The presented work investigates the use of flux closing magnetostrictive multilayers for inverse magnetoelectric sensors. In such a type of magnetic field sensor, highly sensitive AC and DC field detection relies on strong excitation of the incorporated magnetic layers by piezoelectrically driven cantilever oscillation at mechanical resonances. The provoked periodic flux change is influenced by the magnetic field to be measured and is picked up by a coil, which generates the measured output. The effect of the magnetic multilayer on linearity, noise behavior, and detection limit of DC and AC signals is investigated. This study demonstrates the next step for inverse magnetoelectric thin film sensors, which achieve one order of magnitude improved detection limits with less than $8 pT/Hz^{1/2}$ at $10 Hz$ and $18 pT/Hz^{1/2}$ at $DC$ using exchange bias stabilized magnetic multilayers for obtaining flux closure.
Latency versus Transmission Power Trade-off in Free-Space Optical (FSO) Satellite Networks with Multiple Inter-Continental Connections
Authors: Jintao Liang, Aizaz Chaudhry, John Chinneck, Halim Yanikomeroglu, Gunes Kurt, Peng Hu, Khaled Ahmed, Stephane Martel
Date: 2023-12-08 00:00:00

In free-space optical satellite networks (FSOSNs), satellites connected via laser inter-satellite links (LISLs), latency is a critical factor, especially for long-distance inter-continental connections. Since satellites depend on solar panels for power supply, power consumption is also a vital factor. We investigate the minimization of total network latency (i.e., the sum of the network latencies of all inter-continental connections in a time slot) in a realistic model of a FSOSN, the latest version of the Starlink Phase 1 Version 3 constellation. We develop mathematical formulations of the total network latency over different LISL ranges and different satellite transmission power constraints for multiple simultaneous inter-continental connections. We use practical system models for calculating network latency and satellite optical link transmission power, and we formulate the problem as a binary integer linear program. The results reveal that, for satellite transmission power limits set at 0.5 W, 0.3 W, and 0.1 W, the average total network latency for all five inter-continental connections studied in this work levels off at 339 ms, 361 ms, and 542 ms, respectively. Furthermore, the corresponding LISL ranges required to achieve these average total network latency values are 4500 km, 3000 km, and 1731 km, respectively. Different limitations on satellite transmission power exhibit varying effects on average total network latency (over 100 time slots), and they also induce differing changes in the corresponding LISL ranges. In the absence of satellite transmission power constraints, as the LISL range extends from the minimum feasible range of 1575 km to the maximum feasible range of 5016 km, the average total network latency decreases from 589 ms to 311 ms.
Free-Space Optical (FSO) Satellite Networks Performance Analysis: Transmission Power, Latency, and Outage Probability
Authors: Jintao Liang, Aizaz U. Chaudhry, Eylem Erdogan, Halim Yanikomeroglu, Gunes Karabulut Kurt, Peng Hu, Khaled Ahmed, Stephane Martel
Date: 2023-12-08 00:00:00

In free-space optical satellite networks (FSOSNs), satellites can have different laser inter-satellite link (LISL) ranges for connectivity. Greater LISL ranges can reduce network latency of the path but can also result in an increase in transmission power for satellites on the path. Consequently, this tradeoff between satellite transmission power and network latency should be investigated, and in this work we examine it in FSOSNs drawing on the Starlink Phase 1 Version 3 and Kuiper Shell 2 constellations for different LISL ranges and different inter-continental connections. We use appropriate system models for calculating the average satellite transmission power and network latency. The results show that the mean network latency decreases and mean average satellite transmission power increases with an increase in LISL range. For the Toronto--Sydney inter-continental connection in an FSOSN with Starlink's Phase 1 Version 3 constellation, when the LISL range is approximately 2,900 km, the mean network latency and mean average satellite transmission power intersect are approximately 135 ms and 380 mW, respectively. For an FSOSN with the Kuiper Shell 2 constellation in this inter-continental connection, this LISL range is around 3,800 km, and the two parameters are approximately 120 ms and 700 mW, respectively. For the Toronto--Istanbul and Toronto--London inter-continental connections, the LISL ranges at the intersection are different and vary from 2,600 km to 3,400 km. Furthermore, we analyze outage probability performance of optical uplink/downlink due to atmosphere attenuation and turbulence.
Insomnia impairs muscle function via regulating protein degradation and muscle clock
Authors: Hui Ouyang, Hong Jiang, Jin Huang, Zunjing Liu
Date: 2023-12-08 00:00:00

Background: Insomnia makes people more physically unable of doing daily duties, which results in a lack of strength, leads to lacking in strength. However, the effects of insomnia on muscle function have not yet been thoroughly investigated. So, the objectives of this study were to clarify how insomnia contributes to the decrease of muscular function and to investigate the mechanisms behind this phenomenon. Methods: To understand how insomnia influence muscle function, we analyzed the expression level of factors associated with muscle protein degradation, muscle protein synthesis , protein synthesis and degradation pathways and muscle clock. Results: The results showed that lower BMI and grip strength were observed in insomnia patients. The mice in the sleep deprivation(SD) group saw a 7.01 g loss in body mass. The SD group's tibialis anterior and gastrocnemius muscle mass decreased after 96 h of SD). The grip strength reduced in SD group. Using the RT-PCR approaches, we found a significant increase in muscle degradation factors expression in SD group versus normal control group. Conclusions: Insomnia can impair muscle function. The mechanism may be associated with the increased expression of muscle degradation related factors , as well as the abnormal expression of Clock gene.
Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects
Authors: Tianyu He, Guanghui Fu, Yijing Yu, Fan Wang, Jianqiang Li, Qing Zhao, Changwei Song, Hongzhi Qi, Dan Luo, Huijing Zou, Bing Xiang Yang
Date: 2023-12-01 00:00:00

The complexity of psychological principles underscore a significant societal challenge, given the vast social implications of psychological problems. Bridging the gap between understanding these principles and their actual clinical and real-world applications demands rigorous exploration and adept implementation. In recent times, the swift advancement of highly adaptive and reusable artificial intelligence (AI) models has emerged as a promising way to unlock unprecedented capabilities in the realm of psychology. This paper emphasizes the importance of performance validation for these large-scale AI models, emphasizing the need to offer a comprehensive assessment of their verification from diverse perspectives. Moreover, we review the cutting-edge advancements and practical implementations of these expansive models in psychology, highlighting pivotal work spanning areas such as social media analytics, clinical nursing insights, vigilant community monitoring, and the nuanced exploration of psychological theories. Based on our review, we project an acceleration in the progress of psychological fields, driven by these large-scale AI models. These future generalist AI models harbor the potential to substantially curtail labor costs and alleviate social stress. However, this forward momentum will not be without its set of challenges, especially when considering the paradigm changes and upgrades required for medical instrumentation and related applications.
IcoCap: Improving Video Captioning by Compounding Images
Authors: Yuanzhi Liang, Linchao Zhu, Xiaohan Wang, Yi Yang
Date: 2023-10-05 00:00:00
Proceeding: IEEE Transactions on Multimedia 2023 10
Tasks: Image Captioning, Video Captioning

Video captioning is a more challenging task compared to image captioning, primarily due to differences in content density. Video data contains redundant visual content, making it difficult for captioners to generalize diverse content and avoid being misled by irrelevant elements. Moreover, redundant content is not well-trimmed to match the corresponding visual semantics in the ground truth, further increasing the difficulty of video captioning. Current research in video captioning predominantly focuses on captioner design, neglecting the impact of content density on captioner performance. Considering the differences between videos and images, there exists another line to improve video captioning by leveraging concise and easily-learned image samples to further diversify video samples. This modification to content density compels the captioner to learn more effectively against redundancy and ambiguity. In this paper, we propose a novel approach called Image-Compounded learning for video Captioners (IcoCap) to facilitate better learning of complex video semantics. IcoCap comprises two components: the Image-Video Compounding Strategy (ICS) and Visual-Semantic Guided Captioning (VGC). ICS compounds easily-learned image semantics into video semantics, further diversifying video content and prompting the network to generalize contents in a more diverse sample. Besides, learning with the sample compounded with image contents, the captioner is compelled to better extract valuable video cues in the presence of straightforward image semantics. This helps the captioner further focus on relevant information while filtering out extraneous content. Then, VGC guides the network in flexibly learning ground truth captions based on the compounded samples, helping to mitigate the mismatch between the ground truth and ambiguous semantics in video samples. Our experimental results demonstrate the effectiveness of IcoCap in improving the learning of video captioners. Applied to the widely-used MSVD, MSR-VTT, and VATEX datasets, our approach achieves competitive or superior results compared to state-of-the-art methods, illustrating its capacity to handle redundant and ambiguous video data.
GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language Models
Authors: Ghazaleh Mahmoudi, Yeganeh Morshedzadeh, Sauleh Eetemadi
Date: 2023-12-07 00:00:00
Proceeding: ArabicNLP at EMNLP 2023
Tasks: Multi-Task Learning, Passage Retrieval, Question Answering, Reading Comprehension, Retrieval, Sentence, Transfer Learning

This work addresses the challenges of question answering for vintage texts like the Quran. It introduces two tasks: passage retrieval and reading comprehension. For passage retrieval, it employs unsupervised fine-tuning sentence encoders and supervised multi-task learning. In reading comprehension, it fine-tunes an Electra-based model, demonstrating significant improvements over baseline models. Our best AraElectra model achieves 46.1% partial Average Precision (pAP) on the unseen test set, outperforming the baseline by 23%.
RealTime QA: What's the Answer Right Now?
Authors: Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, Ronan Le Bras, Akari Asai, Xinyan Yu, Dragomir Radev, Noah A. Smith, Yejin Choi, Kentaro Inui
Date: 2022-07-27 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Information Retrieval, Question Answering, Retrieval

We introduce REALTIME QA, a dynamic question answering (QA) platform that announces questions and evaluates systems on a regular basis (weekly in this version). REALTIME QA inquires about the current world, and QA systems need to answer questions about novel events or information. It therefore challenges static, conventional assumptions in open-domain QA datasets and pursues instantaneous applications. We build strong baseline models upon large pretrained language models, including GPT-3 and T5. Our benchmark is an ongoing effort, and this paper presents real-time evaluation results over the past year. Our experimental results show that GPT-3 can often properly update its generation results, based on newly-retrieved documents, highlighting the importance of up-to-date information retrieval. Nonetheless, we find that GPT-3 tends to return outdated answers when retrieved documents do not provide sufficient information to find an answer. This suggests an important avenue for future research: can an open-domain QA system identify such unanswerable cases and communicate with the user or even the retrieval module to modify the retrieval results? We hope that REALTIME QA will spur progress in instantaneous applications of question answering and beyond.
Cell-Free versus Cellular Massive MIMO:What Processing is Needed for Cell-Free to Win?
Authors: Emil Björnson∗, Luca Sanguinett
Date: 2019-08-29 00:00:00
Proceeding: IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019 8

—Communication by joint signal processing from
many distributed access points, called Cell-free Massive MIMO,
is a potential beyond-5G network infrastructure. The aim of
this paper is to provide the first comprehensive comparison
with Cellular Massive MIMO. The uplink spectral efficiencies
of four different cell-free implementations are analyzed, with
spatially correlated fading and arbitrary processing. It turns out
that it is possible to outperform cellular networks by a wide
margin, but only using the right signal processing. A centralized
implementation with optimal processing maximizes performance
and, surprisingly, also reduces the fronthaul signaling
Object-centric Learning with Cyclic Walks between Parts and Whole
Authors: Ziyu Wang, Mike Zheng Shou, Mengmi Zhang
Date: 2023-02-16 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Decoder, Object

Learning object-centric representations from complex natural environments enables both humans and machines with reasoning abilities from low-level perceptual features. To capture compositional entities of the scene, we proposed cyclic walks between perceptual features extracted from vision transformers and object entities. First, a slot-attention module interfaces with these perceptual features and produces a finite set of slot representations. These slots can bind to any object entities in the scene via inter-slot competitions for attention. Next, we establish entity-feature correspondence with cyclic walks along high transition probability based on the pairwise similarity between perceptual features (aka "parts") and slot-binded object representations (aka "whole"). The whole is greater than its parts and the parts constitute the whole. The part-whole interactions form cycle consistencies, as supervisory signals, to train the slot-attention module. Our rigorous experiments on \textit{seven} image datasets in \textit{three} \textit{unsupervised} tasks demonstrate that the networks trained with our cyclic walks can disentangle foregrounds and backgrounds, discover objects, and segment semantic objects in complex scenes. In contrast to object-centric models attached with a decoder for the pixel-level or feature-level reconstructions, our cyclic walks provide strong learning signals, avoiding computation overheads and enhancing memory efficiency. Our source code and data are available at: \href{https://github.com/ZhangLab-DeepNeuroCogLab/Parts-Whole-Object-Centric-Learning/}{link}.
Calibration by Distribution Matching: Trainable Kernel Calibration Metrics
Authors: Charles Marx, Sofian Zalouk, Stefano Ermon
Date: 2023-10-31 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Decision Making, regression

Calibration ensures that probabilistic forecasts meaningfully capture uncertainty by requiring that predicted probabilities align with empirical frequencies. However, many existing calibration methods are specialized for post-hoc recalibration, which can worsen the sharpness of forecasts. Drawing on the insight that calibration can be viewed as a distribution matching task, we introduce kernel-based calibration metrics that unify and generalize popular forms of calibration for both classification and regression. These metrics admit differentiable sample estimates, making it easy to incorporate a calibration objective into empirical risk minimization. Furthermore, we provide intuitive mechanisms to tailor calibration metrics to a decision task, and enforce accurate loss estimation and no regret decisions. Our empirical evaluation demonstrates that employing these metrics as regularizers enhances calibration, sharpness, and decision-making across a range of regression and classification tasks, outperforming methods relying solely on post-hoc recalibration.