Self-Supervised Visual Representation Learning with Semantic Grouping

In this paper, we tackle the problem of learning visual representations from unlabeled scene-centric data. Existing works have demonstrated the potential of utilizing the underlying complex structure within scene-centric data; still, they commonly rely on hand-crafted objectness priors or specialized pretext tasks to build a learning framework, which may harm generalizability. Instead, we propose contrastive learning from data-driven semantic slots, namely SlotCon, for joint semantic grouping and representation learning. The semantic grouping is performed by assigning pixels to a set of learnable prototypes, which can adapt to each sample by attentive pooling over the feature and form new slots. Based on the learned data-dependent slots, a contrastive objective is employed for representation learning, which enhances the discriminability of features, and conversely facilitates grouping semantically coherent pixels together. Compared with previous efforts, by simultaneously optimizing the two coupled objectives of semantic grouping and contrastive learning, our approach bypasses the disadvantages of hand-crafted priors and is able to learn object/group-level representations from scene-centric images. Experiments show our approach effectively decomposes complex scenes into semantic groups for feature learning and significantly benefits downstream tasks, including object detection, instance segmentation, and semantic segmentation. Code is available at: https://github.com/CVMI-Lab/SlotCon.Comment: Accepted at NeurIPS 202

On the secrecy performance of land mobile satellite communication systems

In this paper, we investigate the secrecy performance against eavesdropping of a land mobile satellite (LMS) system, where the satellite employs the spot beam technique, and both the terrestrial user and eavesdropper are equipped with multiple antennas and utilize maximal ratio combining (MRC) to receive the confidential message. Specifically, in terms of the availability of the eavesdropper’s CSI at the satellite, we consider both passive (Scenario I) and active (Scenario II) eavesdropping. For Scenario I where the eavesdropper’s channel state information (CSI) is unknown to the satellite, closed-form expressions for the probability of non-zero secrecy capacity and secrecy outage probability are derived. Furthermore, expressions for the asymptotic secrecy outage probability are also presented to reveal the secrecy diversity order and array gain of the considered system. For Scenario II where the eavesdropper’s CSI is available at the satellite, novel expressions for the exact and asymptotic average secrecy capacity are obtained. Based on a simple asymptotic formula, we can characterize the high signalto- noise ratio (SNR) slope and high SNR power offset of the LMS systems. Finally, simulations are provided to validate our theoretical analysis and show the effect of different parameters on the system performance

Towards Understanding the Adoption and Social Experience of Digital Wallet Systems

For millions around the globe, digital wallets are replacing cash and credit cards. These services support user-to-user payments, and add a social component to transactions. However, there is little understanding of the key factors behind digital wallets’ rapid growth in US (Venmo) and China (WeChat Pay). What are the factors that led to their success? How social relationships play a role in their adoption? We conduct a mixed methods study, using a comprehensive survey (N=879) and semi-structured interviews (N=41) to explore the interplay of the two roles of these digital wallets, i.e., a payment system and a social platform. Our analysis suggests that the network effect does benefit their adoption and retention, but through different mechanisms. In return, transaction activities performed in digital wallets help strengthen existing social ties. We also present design implications for future social payment services

Performance Analysis of NOMA-Based Land Mobile Satellite Networks

Non-orthogonal multiple access (NOMA) scheme, which has the ability to superpose information in the power domain and serve multiple users on the same time/frequency resource, is regarded as an effective solution to increase transmit rate and fairness. In this paper, we introduce the NOMA scheme in a downlink land mobile satellite (LMS) network and present a comprehensive performance analysis for the considered system. Specifically, we first obtain the power allocation coefficients by maximizing the sum rate while meeting the predefined target rates of each NOMA user. Then, we derive the theoretical expressions for the ergodic capacity and the energy efficiency of the considered system. Moreover, the outage probability (OP) and average symbol error rate performances of NOMA users are derived analytically. To gain further insights, we derive the asymptotic OP at the high signal-to-noise ratio regime to characterize the diversity orders and coding gains of NOMA users. Finally, simulation results are provided to validate the theoretical analysis as well as the superiority of employing the NOMA scheme in the LMS system, and show the impact of key parameters, such as fading configurations and user selection strategy on the performance of NOMA users

Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

The emergent few-shot reasoning capabilities of Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous successful applications, the underlying mechanism of such in-context capabilities still remains unclear. In this work, we hypothesize that the learned \textit{semantics} of language tokens do the most heavy lifting during the reasoning process. Different from human's symbolic reasoning process, the semantic representations of LLMs could create strong connections among tokens, thus composing a superficial logical chain. To test our hypothesis, we decouple semantics from the language reasoning process and evaluate three kinds of reasoning abilities, i.e., deduction, induction and abduction. Our findings reveal that semantics play a vital role in LLMs' in-context reasoning -- LLMs perform significantly better when semantics are consistent with commonsense but struggle to solve symbolic or counter-commonsense reasoning tasks by leveraging in-context new knowledge. The surprising observations question whether modern LLMs have mastered the inductive, deductive and abductive reasoning abilities as in human intelligence, and motivate research on unveiling the magic existing within the black-box LLMs. On the whole, our analysis provides a novel perspective on the role of semantics in developing and evaluating language models' reasoning abilities. Code is available at {\url{https://github.com/XiaojuanTang/ICSR}}

Hybrid satellite terrestrial relay networks with cooperative non-orthogonal multiple access

In this letter, we investigate the outage probability (OP) and ergodic capacity of the downlink hybrid satellite terrestrial relay networks (HSTRNs) with a cooperative non-orthogonal multiple access (C-NOMA) scheme, in which a user with better channel condition acts as a relay node and forwards information to other users, thus alleviating the masking effect of users with poor channel conditions in heavy shadowing. Specifically, the exact analytical expression for the OP of the considered system is derived. Furthermore, the ergodic capacity expression is also developed to facilitate performance evaluation of the proposed framework. Finally, the simulations are provided to show the impact of key parameters on the considered system and the superiority of introducing the C-NOMA scheme to the HSTRNs

Development and progress of fluorescence imaging technology in diagnosis and treatment of oral cancer

The main clinical problems in treatment of oral cancer are tumor recurrence and metastasis after surgery. To improve the efficiency of resection and the prognosis of patients is a key to curing oral cancer. Fluorescence imaging, as an adjuvant in the diagnosis and treatment of oral cancer with no invasion and radiation, can provide real-time and ultrahigh-resolution graphics for preoperative tumor diagnosis, intraoperative tumor margin determination and postoperative tumor bed checking, which can help reduce the recurrent rate, enhance the survival rate and improve the life quality of patients. This article reviewed several typical fluorescent probes and discussed their characteristics, limitations and application prospects in clinic or research for oral cancer

Quantifying Wetting Dynamics with Triboelectrification

Wetting is often perceived as an intrinsic surface property of materials, but determining its evolution is complicated by its complex dependence on roughness across the scales. The Wenzel state, where liquids have intimate contact with the rough substrate, and the Cassie-Baxter state, where liquids sit onto air pockets formed between asperities, are only two states among the plethora of wetting behaviors. Furthermore, transitions from the Cassie-Baxter to the Wenzel state dictate completely different surface performance, such as anti-contamination, anti-icing, drag reduction etc.; however, little is known about how transition occurs during time between the several wetting modes. In this paper, we show that wetting dynamics can be accurately quantified and tracked using solid-liquid triboelectrification. Theoretical underpinning reveals how surface micro-/nano-geometries regulate stability/infiltration, also demonstrating the generality of our theoretical approach in understanding wetting transitions.Comment: Both Main and SI uploaded in a single fil

Optimized sample preparation for two-dimensional gel electrophoresis of soluble proteins from chicken bursa of Fabricius

<p>Abstract</p> <p>Background</p> <p>Two-dimensional gel electrophoresis (2-DE) is a powerful method to study protein expression and function in living organisms and diseases. This technique, however, has not been applied to avian bursa of Fabricius (BF), a central immune organ. Here, optimized 2-DE sample preparation methodologies were constructed for the chicken BF tissue. Using the optimized protocol, we performed further 2-DE analysis on a soluble protein extract from the BF of chickens infected with virulent avibirnavirus. To demonstrate the quality of the extracted proteins, several differentially expressed protein spots selected were cut from 2-DE gels and identified by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS).</p> <p>Results</p> <p>An extraction buffer containing 7 M urea, 2 M thiourea, 2% (w/v) 3-[(3-cholamidopropyl)-dimethylammonio]-1-propanesulfonate (CHAPS), 50 mM dithiothreitol (DTT), 0.2% Bio-Lyte 3/10, 1 mM phenylmethylsulfonyl fluoride (PMSF), 20 U/ml Deoxyribonuclease I (DNase I), and 0.25 mg/ml Ribonuclease A (RNase A), combined with sonication and vortex, yielded the best 2-DE data. Relative to non-frozen immobilized pH gradient (IPG) strips, frozen IPG strips did not result in significant changes in the 2-DE patterns after isoelectric focusing (IEF). When the optimized protocol was used to analyze the spleen and thymus, as well as avibirnavirus-infected bursa, high quality 2-DE protein expression profiles were obtained. 2-DE maps of BF of chickens infected with virulent avibirnavirus were visibly different and many differentially expressed proteins were found.</p> <p>Conclusion</p> <p>These results showed that method C, in concert extraction buffer IV, was the most favorable for preparing samples for IEF and subsequent protein separation and yielded the best quality 2-DE patterns. The optimized protocol is a useful sample preparation method for comparative proteomics analysis of chicken BF tissues.</p

Inhibition of A/Human/Hubei/3/2005 (H3N2) influenza virus infection by silver nanoparticles in vitro and in vivo

AbstractSilver nanoparticles (AgNPs) have attracted much attention as antimicrobial agents and have demonstrated efficient inhibitory activity against various viruses, including human immunodeficiency virus, hepatitis B virus, and Tacaribe virus. In this study, we investigated if AgNPs could have antiviral and preventive effects in A/Human/Hubei/3/2005 (H3N2) influenza virus infection. Madin-Darby canine kidney cells infected with AgNP-treated H3N2 influenza virus showed better viability (P,0.05 versus influenza virus control) and no obvious cytopathic effects compared with an influenza virus control group and a group treated with the solvent used for preparation of the AgNPs. Hemagglutination assay indicated that AgNPs could significantly inhibit growth of the influenza virus in Madin-Darby canine kidney cells (P,0.01 versus the influenza virus control). AgNPs significantly reduced cell apoptosis induced by H3N2 influenza virus at three different treatment pathways (P,0.05 versus influenza virus control). H3N2 influenza viruses treated with AgNPs were analyzed by transmission electron microscopy and found to interact with each other, resulting in destruction of morphologic viral structures in a time-dependent manner in a time range of 30 minutes to 2 hours. In addition, intranasal AgNP administration in mice significantly enhanced survival after infection with the H3N2 influenza virus. Mice treated with AgNPs showed lower lung viral titer levels and minor pathologic lesions in lung tissue, and had a marked survival benefit during secondary intranasal passage in vivo. These results provide evidence that AgNPs have beneficial effects in preventing H3N2 influenza virus infection both in vitro and in vivo, and demonstrate that AgNPs can be used as potential therapeutics for inhibiting outbreaks of influenza.<br /