Semi-Supervised End-To-End Contrastive Learning For Time Series Classification

Time series classification is a critical task in various domains, such as finance, healthcare, and sensor data analysis. Unsupervised contrastive learning has garnered significant interest in learning effective representations from time series data with limited labels. The prevalent approach in existing contrastive learning methods consists of two separate stages: pre-training the encoder on unlabeled datasets and fine-tuning the well-trained model on a small-scale labeled dataset. However, such two-stage approaches suffer from several shortcomings, such as the inability of unsupervised pre-training contrastive loss to directly affect downstream fine-tuning classifiers, and the lack of exploiting the classification loss which is guided by valuable ground truth. In this paper, we propose an end-to-end model called SLOTS (Semi-supervised Learning fOr Time clasSification). SLOTS receives semi-labeled datasets, comprising a large number of unlabeled samples and a small proportion of labeled samples, and maps them to an embedding space through an encoder. We calculate not only the unsupervised contrastive loss but also measure the supervised contrastive loss on the samples with ground truth. The learned embeddings are fed into a classifier, and the classification loss is calculated using the available true labels. The unsupervised, supervised contrastive losses and classification loss are jointly used to optimize the encoder and classifier. We evaluate SLOTS by comparing it with ten state-of-the-art methods across five datasets. The results demonstrate that SLOTS is a simple yet effective framework. When compared to the two-stage framework, our end-to-end SLOTS utilizes the same input data, consumes a similar computational cost, but delivers significantly improved performance. We release code and datasets at https://anonymous.4open.science/r/SLOTS-242E.Comment: Submitted to NeurIPS 202

Research on the Impact of Game Users’ Perceived Value on Satisfaction and Loyalty - Based on the Perspectives of Hedonic Value and Utilitarian Value

As Chinese game market growing mature, cultivating loyal game users has become the new goals for game companies. Based on the theory of game users experience, this paper constructs the structural model of customer with the variables of perceived value, customer satisfaction and customer loyalty and studies the relationship between the game users’ hedonic/utilitarian value and customer satisfaction/customer loyalty from the perspective of the game user utilitarian value and hedonic value. The study finds that the game users’ perceived value has a positive effect on customer satisfaction and customer loyalty; while hedonic value has a more significant effect on customer satisfaction than utilitarian value, the latter one has a greater significant effect on customer loyalty than the former one; customer satisfaction has a positive effect on customer loyalty; hedonic value and utilitarian value interact and influence with each other. Implication and recommendation of this research is that enhancing the hedonic and utilitarian value of game users by game companies which is one of the effective ways to improve game users’ satisfaction and loyalty

Exact distributions of finite random matrices and their applications to spectrum sensing

The exact and simple distributions of finite random matrix theory (FRMT) are critically important for cognitive radio networks (CRNs). In this paper, we unify some existing distributions of the FRMT with the proposed coefficient matrices (vectors) and represent the distributions with the coefficient-based formulations. A coefficient reuse mechanism is studied, i.e., the same coefficient matrices (vectors) can be exploited to formulate different distributions. For instance, the same coefficient matrices can be used by the largest eigenvalue (LE) and the scaled largest eigenvalue (SLE); the same coefficient vectors can be used by the smallest eigenvalue (SE) and the Demmel condition number (DCN). A new and simple cumulative distribution function (CDF) of the DCN is also deduced. In particular, the dimension boundary between the infinite random matrix theory (IRMT) and the FRMT is initially defined. The dimension boundary provides a theoretical way to divide random matrices into infinite random matrices and finite random matrices. The FRMT-based spectrum sensing (SS) schemes are studied for CRNs. The SLE-based scheme can be considered as an asymptotically-optimal SS scheme when the dimension K is larger than two. Moreover, the standard condition number (SCN)-based scheme achieves the same sensing performance as the SLE-based scheme for dual covariance matrix [Formula: see text]. The simulation results verify that the coefficient-based distributions can fit the empirical results very well, and the FRMT-based schemes outperform the IRMT-based schemes and the conventional SS schemes

Catching butterflies in the sky: Extended catalog of winged or X-shaped radio sources from the latest FIRST data release

We present a catalog of 290 "winged" or X-shaped radio galaxies (XRGs) extracted from the latest (2014 December 17) data release of the "Very Large Array Faint Images of the Radio Sky at Twenty centimeter." We have combined these radio images with their counterparts in the TIFR GMRT sky survey at 150 MHz, in an attempt to identify any low surface brightness radio emission present in these sources. This has enabled us to assemble a sample of 106 "strong" XRG candidates and 184 "probable" XRG candidates whose XRG designation needs to be verified by further observations. The present sample of 290 XRG candidates is almost twice as large as the number of XRGs currently known. Twenty-five of our 290 XRG candidates (9 "strong" and 16 "probable") are identified as quasars. Double-peaked narrow emission lines are seen in the optical spectra of three of the XRG candidates (two "strong" and one "probable"). Nearly 90% of the sample is located in the FR II domain of the Owen-Ledlow diagram. A few of the strong XRG candidates have a rather flat radio spectrum (spectral index alpha flatter than -0.3) between 150 MHz and 1.4 GHz, or between 1.4 and 5 GHz. Since this is not expected for lobe-dominated extragalactic radio sources (like nearly all known XRGs), these sources are particularly suited for follow-up radio imaging and near-simultaneous measurement of the radio spectrum.Comment: 22 pages, 9 figures, 3 tables, accepted for publication in ApJ

The Dusty and Extremely Red Progenitor of the Type II Supernova 2023ixf in Messier 101

Stars with initial masses in the range of 8-25 solar masses are thought to end their lives as hydrogen-rich supernova (SNe II). Based on the pre-explosion images of Hubble Space Telescope (\textit{HST}) and \textit{Spitzer} Space Telescope, we place tight constraints on the progenitor candidate of type IIP SN 2023ixf in Messier 101. Fitting of the spectral energy distribution (SED) of its progenitor with dusty stellar spectral models results in an estimation of the effective temperature as 3090 K, making it the coolest SN progenitor ever discovered. The luminosity is estimated as log($L/$L$_{\odot}$)$\sim4.8$, consistent with a red supergiant (RSG) star with an initial mass of 12$^{+2}_{-1}$ M$_{\odot}$. The derived mass loss rate (6-9$\times10^{-6}$ M$_{\odot}$ yr$^{-1}$) is much lower than that inferred from the flash spectroscopy of the SN, suggesting that the progenitor experienced a sudden increase in mass loss when approaching the final explosion. In the mid-infrared color diagram, the progenitor star is found to show a significant deviation from the range of regular RSGs, but is close to some extreme RSGs and super asymptotic giant branch (sAGB) stars. Thus, SN 2023ixf may belong to a rare subclass of electron-captured supernova for an origin of sAGB progenitor.Comment: 6 figures; under review by Science Bulleti

NH3-Sensing Mechanism Using Surface Acoustic Wave Sensor with AlO(OH) Film

In this study, AlO(OH) (boehmite) film was deposited onto a surface acoustic wave (SAW) resonator using a combined sol-gel and spin-coating technology, and prepared and used as a sensitive layer for a high-performance ammonia sensor. The prepared AlO(OH) film has a mesoporous structure and a good affinity to NH3 (ammonia gas) molecules, and thus can selectively adsorb and react with NH3. When exposed to ammonia gases, the SAW sensor shows an initial positive response of the frequency shift, and then a slight decrease of the frequency responses. The sensing mechanism of the NH3 sensor is based on the competition between mass-loading and elastic-loading effects. The sensor operated at room temperature shows a positive response of 1540 Hz to 10 ppm NH3, with excellent sensitivity, selectivity and stability

TRAF3 Negatively Regulates Platelet Activation and Thrombosis

CD40 ligand (CD40L), a member of the tumor necrosis factor (TNF) superfamily, binds to CD40, leading to many effects depending on target cell type. Platelets express CD40L and are a major source of soluble CD40L. CD40L has been shown to potentiate platelet activation and thrombus formation, involving both CD40-dependent and -independent mechanisms. A family of proteins called TNF receptor associated factors (TRAFs) plays key roles in mediating CD40L-CD40 signaling. Platelets express several TRAFs. It has been shown that TRAF2 plays a role in CD40L-mediated platelet activation. Here we show that platelet also express TRAF3, which plays a negative role in regulating platelet activation. Thrombin- or collagen-induced platelet aggregation and secretion are increased in TRAF3 knockout mice. The expression levels of collagen receptor GPVI and integrin αIIbβ3 in platelets were not affected by deletion of TRAF3, suggesting that increased platelet activation in the TRAF3 knockout mice was not due to increased expression platelet receptors. Time to formation of thrombi in a FeCl3-induced thrombosis model was significantly shortened in the TRAF3 knockout mice. However, mouse tail-bleeding times were not affected by deletion of TRAF3. Thus, TRAF3 plays a negative role in platelet activation and in thrombus formation in vivo

A Trust Evaluation Algorithm for Wireless Sensor Networks Based on Node Behaviors and D-S Evidence Theory

For wireless sensor networks (WSNs), many factors, such as mutual interference of wireless links, battlefield applications and nodes exposed to the environment without good physical protection, result in the sensor nodes being more vulnerable to be attacked and compromised. In order to address this network security problem, a novel trust evaluation algorithm defined as NBBTE (Node Behavioral Strategies Banding Belief Theory of the Trust Evaluation Algorithm) is proposed, which integrates the approach of nodes behavioral strategies and modified evidence theory. According to the behaviors of sensor nodes, a variety of trust factors and coefficients related to the network application are established to obtain direct and indirect trust values through calculating weighted average of trust factors. Meanwhile, the fuzzy set method is applied to form the basic input vector of evidence. On this basis, the evidence difference is calculated between the indirect and direct trust values, which link the revised D-S evidence combination rule to finally synthesize integrated trust value of nodes. The simulation results show that NBBTE can effectively identify malicious nodes and reflects the characteristic of trust value that ‘hard to acquire and easy to lose’. Furthermore, it is obvious that the proposed scheme has an outstanding advantage in terms of illustrating the real contribution of different nodes to trust evaluation

Circumstellar Material Ejected Violently by A Massive Star Immediately before its Death

Type II supernovae represent the most common stellar explosions in the Universe, for which the final stage evolution of their hydrogen-rich massive progenitors towards core-collapse explosion are elusive. The recent explosion of SN 2023ixf in a very nearby galaxy, Messier 101, provides a rare opportunity to explore this longstanding issue. With the timely high-cadence flash spectra taken within 1-5 days after the explosion, we can put stringent constraints on the properties of the surrounding circumstellar material around this supernova. Based on the rapid fading of the narrow emission lines and luminosity/profile of $\rm H\alpha$ emission at very early times, we estimate that the progenitor of SN 2023ixf lost material at a mass-loss rate $\dot{\rm M} \approx 6 \times 10^{-4}\, \rm M_{\odot}\,yr^{-1}$ over the last 2-3 years before explosion. This close-by material, moving at a velocity $v_{\rm w} \approx 55\rm \, km\,s^{-1}$, accumulates a compact CSM shell at the radius smaller than $7 \times 10^{14}$ cm from the progenitor. Given the high mass-loss rate and relatively large wind velocity presented here, together with the pre-explosion observations made about two decades ago, the progenitor of SN 2023ixf could be a short-lived yellow hypergiant that evolved from a red supergiant shortly before the explosion.Comment: 10 pages, 6 figures in main body, accepted for publication in Science Bulleti