87 research outputs found

    ISAC Meets SWIPT: Multi-functional Wireless Systems Integrating Sensing, Communication, and Powering

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    This paper unifies integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT), by investigating a new multi-functional multiple-input multiple-output (MIMO) system integrating wireless sensing, communication, and powering. In this system, one multi-antenna hybrid access point (H-AP) transmits wireless signals to communicate with one multi-antenna information decoding (ID) receiver, wirelessly charge one multi-antenna energy harvesting (EH) receiver, and perform radar target sensing based on the echo signal at the same time. Under this setup, we aim to reveal the fundamental performance tradeoff limits among sensing, communication, and powering, in terms of the estimation Cramer-Rao bound (CRB), achievable communication rate, and harvested energy level, respectively. In particular, we consider two different target models for radar sensing, namely the point and extended targets, for which we are interested in estimating the target angle and the complete target response matrix, respectively. For both models, we define the achievable CRB-rate-energy (C-R-E) region and characterize its Pareto boundary by maximizing the achievable rate at the ID receiver, subject to the estimation CRB requirement for target sensing, the harvested energy requirement at the EH receiver, and the maximum transmit power constraint at the H-AP. We obtain the well-structured optimal transmit covariance solutions to the two formulated problems by applying advanced convex optimization techniques. Numerical results show the optimal C-R-E region boundary achieved by our proposed design, as compared to the benchmark schemes based on time switching and eigenmode transmission (EMT).Comment: 30 pages, 9 figures, submitted to IEEE TCOM. arXiv admin note: substantial text overlap with arXiv:2210.1671

    Optimal Transmit Beamforming for Integrated Sensing and Communication

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    This paper studies the transmit beamforming in a downlink integrated sensing and communication (ISAC) system, where a base station (BS) equipped with a uniform linear array (ULA) sends combined information-bearing and dedicated radar signals to simultaneously perform downlink multiuser communication and radar target sensing. Under this setup, we maximize the radar sensing performance (in terms of minimizing the beampattern matching errors or maximizing the minimum weighted beampattern gains), subject to the communication users' minimum signal-to-interference-plus-noise ratio (SINR) requirements and the BS's transmit power constraints. In particular, we consider two types of communication receivers, namely Type-I and Type-II receivers, which do not have and do have the capability of cancelling the interference from the {\emph{a-priori}} known dedicated radar signals, respectively. Under both Type-I and Type-II receivers, the beampattern matching and minimum weighted beampattern gain maximization problems are globally optimally solved via applying the semidefinite relaxation (SDR) technique together with the rigorous proof of the tightness of SDR for both Type-I and Type-II receivers under the two design criteria. It is shown that at the optimality, radar signals are not required with Type-I receivers under some specific conditions, while radar signals are always needed to enhance the performance with Type-II receivers. Numerical results show that the minimum weighted beampattern gain maximization leads to significantly higher beampattern gains at the worst-case sensing angles with a much lower computational complexity than the beampattern matching design. We show that by exploiting the capability of canceling the interference caused by the radar signals, the case with Type-II receivers results in better sensing performance than that with Type-I receivers and other conventional designs.Comment: submitted for possible journal publicatio

    MicroNAS: Zero-Shot Neural Architecture Search for MCUs

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    Neural Architecture Search (NAS) effectively discovers new Convolutional Neural Network (CNN) architectures, particularly for accuracy optimization. However, prior approaches often require resource-intensive training on super networks or extensive architecture evaluations, limiting practical applications. To address these challenges, we propose MicroNAS, a hardware-aware zero-shot NAS framework designed for microcontroller units (MCUs) in edge computing. MicroNAS considers target hardware optimality during the search, utilizing specialized performance indicators to identify optimal neural architectures without high computational costs. Compared to previous works, MicroNAS achieves up to 1104x improvement in search efficiency and discovers models with over 3.23x faster MCU inference while maintaining similar accurac

    Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection

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    It becomes urgent to design effective anti-spoofing algorithms for vulnerable automatic speaker verification systems due to the advancement of high-quality playback devices. Current studies mainly treat anti-spoofing as a binary classification problem between bonafide and spoofed utterances, while lack of indistinguishable samples makes it difficult to train a robust spoofing detector. In this paper, we argue that for anti-spoofing, it needs more attention for indistinguishable samples over easily-classified ones in the modeling process, to make correct discrimination a top priority. Therefore, to mitigate the data discrepancy between training and inference, we propose to leverage a balanced focal loss function as the training objective to dynamically scale the loss based on the traits of the sample itself. Besides, in the experiments, we select three kinds of features that contain both magnitude-based and phase-based information to form complementary and informative features. Experimental results on the ASVspoof2019 dataset demonstrate the superiority of the proposed methods by comparison between our systems and top-performing ones. Systems trained with the balanced focal loss perform significantly better than conventional cross-entropy loss. With complementary features, our fusion system with only three kinds of features outperforms other systems containing five or more complex single models by 22.5% for min-tDCF and 7% for EER, achieving a min-tDCF and an EER of 0.0124 and 0.55% respectively. Furthermore, we present and discuss the evaluation results on real replay data apart from the simulated ASVspoof2019 data, indicating that research for anti-spoofing still has a long way to go.Comment: This work has been accepted by the 25th International Conference on Pattern Recognition (ICPR2020

    Ground state phase transition in the Nilsson mean-field plus standard pairing model

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    The ground state phase transition in Nd, Sm, and Gd isotopes is investigated by using the Nilsson mean-field plus standard pairing model based on the exact solutions obtained from the extended Heine-Stieltjes correspondence. The results of the model calculations successfully reproduce the critical phenomena observed experimentally in the odd-even mass differences, odd-even differences of two-neutron separation energy, and the α-decay and double β - decay energies of these isotopes. Since the odd-even effects are the most important signatures of pairing interactions in nuclei, the model calculations yield microscopic insight into the nature of the ground state phase transition manifested by the standard pairing interaction

    Enhancing Generalizable 6D Pose Tracking of an In-Hand Object with Tactile Sensing

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    While holding and manipulating an object, humans track the object states through vision and touch so as to achieve complex tasks. However, nowadays the majority of robot research perceives object states just from visual signals, hugely limiting the robotic manipulation abilities. This work presents a tactile-enhanced generalizable 6D pose tracking design named TEG-Track to track previously unseen in-hand objects. TEG-Track extracts tactile kinematic cues of an in-hand object from consecutive tactile sensing signals. Such cues are incorporated into a geometric-kinematic optimization scheme to enhance existing generalizable visual trackers. To test our method in real scenarios and enable future studies on generalizable visual-tactile tracking, we collect a real visual-tactile in-hand object pose tracking dataset. Experiments show that TEG-Track significantly improves state-of-the-art generalizable 6D pose trackers in both synthetic and real cases

    Partial Confirmation of Single katG and katE Knockouts and Double katG/katE Knockouts Created from Isogenic Background of Escherichia coli K-12 Strains

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    Presumptive knockouts of katE and katG catalases were constructed from BW25113 E.coli K-12 strain background via Lambda Red recombination system to generate katE/katG double knockout for a better assessment of the roles of the individual catalases (Narita and Peng, JEMI, 16, 123-8, 2012). The kanamycin resistance cassettes were then removed through FLP-FRT recombination system for consistent antibiotic sensitivity across the laboratory strains. In this study, our goal was to confirm the genotype and phenotype of these knockout strains by PCR, and catalase activity assay with 30% or 2% hydrogen peroxide (H 2 O 2 ). The katG single knockout and double knockout strains, as expected, were catalase positive and negative, respectively. The katE single knockout strain was only catalase positive when the test was done with 2% hydrogen peroxide suggesting a threshold concentration of hydrogen peroxide required for katG expression. The PCR results confirmed the continued existence of katE knockout during the process of creating double knockouts. It also identified that the kanR gene insert is present in the presumptive double knockout strain PN11W-4a

    DPL: Decoupled Prompt Learning for Vision-Language Models

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    Prompt learning has emerged as an efficient and effective approach for transferring foundational Vision-Language Models (e.g., CLIP) to downstream tasks. However, current methods tend to overfit to seen categories, thereby limiting their generalization ability for unseen classes. In this paper, we propose a new method, Decoupled Prompt Learning (DPL), which reformulates the attention in prompt learning to alleviate this problem. Specifically, we theoretically investigate the collaborative process between prompts and instances (i.e., image patches/text tokens) by reformulating the original self-attention into four separate sub-processes. Through detailed analysis, we observe that certain sub-processes can be strengthened to bolster robustness and generalizability by some approximation techniques. Furthermore, we introduce language-conditioned textual prompting based on decoupled attention to naturally preserve the generalization of text input. Our approach is flexible for both visual and textual modalities, making it easily extendable to multi-modal prompt learning. By combining the proposed techniques, our approach achieves state-of-the-art performance on three representative benchmarks encompassing 15 image recognition datasets, while maintaining parameter-efficient. Moreover, our DPL does not rely on any auxiliary regularization task or extra training data, further demonstrating its remarkable generalization ability.Comment: 11 pages, 5 figures, 8 table

    T-Rex: Text-assisted Retrosynthesis Prediction

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    As a fundamental task in computational chemistry, retrosynthesis prediction aims to identify a set of reactants to synthesize a target molecule. Existing template-free approaches only consider the graph structures of the target molecule, which often cannot generalize well to rare reaction types and large molecules. Here, we propose T-Rex, a text-assisted retrosynthesis prediction approach that exploits pre-trained text language models, such as ChatGPT, to assist the generation of reactants. T-Rex first exploits ChatGPT to generate a description for the target molecule and rank candidate reaction centers based both the description and the molecular graph. It then re-ranks these candidates by querying the descriptions for each reactants and examines which group of reactants can best synthesize the target molecule. We observed that T-Rex substantially outperformed graph-based state-of-the-art approaches on two datasets, indicating the effectiveness of considering text information. We further found that T-Rex outperformed the variant that only use ChatGPT-based description without the re-ranking step, demonstrate how our framework outperformed a straightforward integration of ChatGPT and graph information. Collectively, we show that text generated by pre-trained language models can substantially improve retrosynthesis prediction, opening up new avenues for exploiting ChatGPT to advance computational chemistry. And the codes can be found at https://github.com/lauyikfung/T-Rex

    Case report: Sintilimab combined with anlotinib as neoadjuvant chemotherapy for metastatic bone tumor resection in patients with PSC

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    BackgroundPulmonary sarcomatoid carcinoma (PSC) is a rare subtype of non-small-cell lung cancer (NSCLC), which is resistant to chemotherapy and radiotherapy with a poor prognosis. PSC is highly malignant and is prone to recurrence even after surgery. The programmed death-ligand 1 (PD-L1) tumor cell proportion score (TPS) 5%, TERT and TP53 gene mutations were detected in this patient accompanied by multiple metastatic sites. The anlotinib is a novel multitarget tyrosine kinase inhibitor (TKI) that could be effective for advanced NSCLC and some sarcoma patients. Limited clinical trials and case reports have shown that PSC patients with gene mutations and PD-L1 expression have good responses to multitarget antiangiogenic drug and immune checkpoint inhibitors (ICIs). In this article, we reported a case with metastatic PSC diagnosed by Computed Tomography (CT)-guided needle biopsy treated with immunotherapy combined with antiangiogenic drugs as a neoadjuvant chemotherapy (NACT). PSC is controlled and the patient achieves successfully limb salvage treatment by surgical resection. Therefore, targeted therapy and immunotherapy can provide sufficient surgical opportunities for limb salvage in the treatment of metastatic PSC patients.Case summaryA 69-year-old male diagnosed with malignant bone tumor in the proximal femur was admitted to our hospital in June 2022 with recurrent fever as well as swelling and pain in the left thigh for twenty days. The initial computed tomography (CT) scan of the chest showed a pulmonary cavity (20 mm × 30 mm) and scattered lung masses. Subsequently, he underwent a CT-guided needle biopsy to distinguish the essence of osteolytic bone destruction and soft tissue mass in the left proximal femur which showed metastatic sarcomatoid carcinoma histology. Genetic testing revealed TERT c.-124C mutation (abundance 8.81%), TP53 p.R342 mutation (abundance 11.35%), tumor mutational burden (TMB) 7.09 muts/Mb, microsatellite stability (MSS), and PD-L1 (SP263) TPS 5% were also detected. The patient was tentatively treated with a combination of antiangiogenic drug and PD-1 inhibitor. After one course, the tumor volume significantly reduced in magnetic resonance imaging (MRI) and pathological fracture occurred in the femur after combined treatment. The patient received proximal femoral tumor resection and prosthesis replacement after defervescence. Sequentially sintilimab with anlotinib were administered for over 1 year. Finally, the local tumor was well controlled, and no obvious drug-related adverse reactions were observed. The lesions in the lung remained in partial response (PR) for more than 16 months and complete response (CR) of metastatic tumor in the proximal femur was observed through imaging examinations.ConclusionThis is the first reported case of a metastatic PSC in femur showing a favorable response to the treatment consisting of anlotinib combined with sintilimab. This case suggests that antiangiogenic therapy combined with immunotherapy may benefit patients with metastatic PSC in the preoperative adjuvant therapy for limb salvage
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