87 research outputs found
ISAC Meets SWIPT: Multi-functional Wireless Systems Integrating Sensing, Communication, and Powering
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
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
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
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
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
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
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
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
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
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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