State Advantage Weighting for Offline RL

We present state advantage weighting for offline reinforcement learning (RL). In contrast to action advantage $A(s,a)$ that we commonly adopt in QSA learning, we leverage state advantage $A(s,s^\prime)$ and QSS learning for offline RL, hence decoupling the action from values. We expect the agent can get to the high-reward state and the action is determined by how the agent can get to that corresponding state. Experiments on D4RL datasets show that our proposed method can achieve remarkable performance against the common baselines. Furthermore, our method shows good generalization capability when transferring from offline to online.Comment: 3rd Offline RL workshop at NeurIPS 2022. arXiv admin note: text overlap with arXiv:2206.0798

Structural, stereochemical, and bioactive studies of cembranoids from Chinese soft coral Sarcophyton trocheliophorum

LBK

Simultaneous Source Localization and Polarization Estimation via Non-Orthogonal Joint Diagonalization with Vector-Sensors

Joint estimation of direction-of-arrival (DOA) and polarization with electromagnetic vector-sensors (EMVS) is considered in the framework of complex-valued non-orthogonal joint diagonalization (CNJD). Two new CNJD algorithms are presented, which propose to tackle the high dimensional optimization problem in CNJD via a sequence of simple sub-optimization problems, by using LU or LQ decompositions of the target matrices as well as the Jacobi-type scheme. Furthermore, based on the above CNJD algorithms we present a novel strategy to exploit the multi-dimensional structure present in the second-order statistics of EMVS outputs for simultaneous DOA and polarization estimation. Simulations are provided to compare the proposed strategy with existing tensorial or joint diagonalization based methods

Linguistic Expressions on "Tastiness" and Co-occurring Nonverbal Behaviors: Focusing on Expressions seen on a TV program

In this research, we analyzed the relations between linguistic expressions expressing "Tastiness" and co-occurring nonverbal expressions for the TV program. The linguistic expressions expressing "Tastiness" has been revealed in the previous researches. However, in this research, it became clear that "General evaluation expressions" are most used among linguistic expressions. In addition, when "Tastiness" is expressed, it was found that the nonverbal behaviors "Gaze" co-occurs most frequently. Regarding co-occurrence of linguistic expressions expressing "Tastiness" and nonverbal behaviors, it has been revealed that there are some linguistic expressions which tend to co-occur with nonverbal behaviors such as "Exclamation expressions". It was also seen that verbal expressions such as "Taste expressions" and "Olfactory expression" usually co-occur with certain nonverbal behaviors. While "General evaluation expressions" co-occur with various nonverbal behaviors

Investigating the diagnostic efficiency of a computer-aided diagnosis system for thyroid nodules in the context of Hashimoto’s thyroiditis

ObjectivesThis study aims to investigate the efficacy of a computer-aided diagnosis (CAD) system in distinguishing between benign and malignant thyroid nodules in the context of Hashimoto’s thyroiditis (HT) and to evaluate the role of the CAD system in reducing unnecessary biopsies of benign lesions.MethodsWe included a total of 137 nodules from 137 consecutive patients (mean age, 43.5 ± 11.8 years) who were histopathologically diagnosed with HT. The two-dimensional ultrasound images and videos of all thyroid nodules were analyzed by the CAD system and two radiologists with different experiences according to ACR TI-RADS. The diagnostic cutoff values of ACR TI-RADS were divided into two categories (TR4 and TR5), and then the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the CAD system and the junior and senior radiologists were compared in both cases. Moreover, ACR TI-RADS classification was revised according to the results of the CAD system, and the efficacy of recommended fine-needle aspiration (FNA) was evaluated by comparing the unnecessary biopsy rate and the malignant rate of punctured nodules.ResultsThe accuracy, sensitivity, specificity, PPV, and NPV of the CAD system were 0.876, 0.905, 0.830, 0.894, and 0.846, respectively. With TR4 as the cutoff value, the AUCs of the CAD system and the junior and senior radiologists were 0.867, 0.628, and 0.722, respectively, and the CAD system had the highest AUC (P < 0.0001). With TR5 as the cutoff value, the AUCs of the CAD system and the junior and senior radiologists were 0.867, 0.654, and 0.812, respectively, and the CAD system had a higher AUC than the junior radiologist (P < 0.0001) but comparable to the senior radiologist (P = 0.0709). With the assistance of the CAD system, the number of TR4 nodules was decreased by both junior and senior radiologists, the malignant rate of punctured nodules increased by 30% and 22%, and the unnecessary biopsies of benign lesions were both reduced by nearly half.ConclusionsThe CAD system based on deep learning can improve the diagnostic performance of radiologists in identifying benign and malignant thyroid nodules in the context of Hashimoto’s thyroiditis and can play a role in FNA recommendations to reduce unnecessary biopsy rates

The Radon Signed Cumulative Distribution Transform and its applications in classification of Signed Images

Here we describe a new image representation technique based on the mathematics of transport and optimal transport. The method relies on the combination of the well-known Radon transform for images and a recent signal representation method called the Signed Cumulative Distribution Transform. The newly proposed method generalizes previous transport-related image representation methods to arbitrary functions (images), and thus can be used in more applications. We describe the new transform, and some of its mathematical properties and demonstrate its ability to partition image classes with real and simulated data. In comparison to existing transport transform methods, as well as deep learning-based classification methods, the new transform more accurately represents the information content of signed images, and thus can be used to obtain higher classification accuracies. The implementation of the proposed method in Python language is integrated as a part of the software package PyTransKit, available on Github

Susceptibility trends of zoliflodacin against multidrug-resistant Neisseria gonorrhoeae clinical isolates in Nanjing, China (2014-2018)

Previously, we reported potent activity of a novel spiropyrimidinetrione, zoliflodacin, against N. gonorrhoeae isolates from symptomatic men in Nanjing, China, collected in 2013. Here, we investigated trends of susceptibilities of zoliflodacin in 986 isolates collected from men between 2014 and 2018. N. gonorrhoeae isolates were tested for susceptibility to zoliflodacin and seven other antibiotics. Mutations in gyrA, gyrB, parC, parE and mtrR genes were determined by PCR and sequencing. The MICs of zoliflodacin ranged from \u3c /=0.002 to 0.25 mg/L; the overall MIC50s and MIC90s were 0.06 mg/L and 0.125mg/L in 2018, increasing two-fold from 2014. However, the percent of isolates with lower zoliflodacin MICs declined in each year sequentially while the percent with higher MICs increased yearly (P \u3c /=0.00001). All isolates were susceptible to spectinomycin but resistant to ciprofloxacin (MIC \u3e /=1 mg/L); 21.2% (209/986) were resistant to azithromycin ( \u3e /=1 mg/L), 43.4% (428/986) were penicillinase-producing (PPNG), 26.9% (265/986) tetracycline-resistant (TRNG) and 19.4% (191/986) were multi-drug resistant (MDR) isolates. Among 202 isolates tested, all were quinolone resistant with double or triple mutations in gyrA; One hundred ninety three (193/202; 95.5%) also had mutations in parC There were no D429N/A and/or K450T mutations in GyrB identified in the 143 isolates with higher zoliflodacin MICs; a S467N mutation in GyrB was identified in one isolate. We report that zoliflodacin continues to have excellent in vitro activity against clinical gonococcal isolates, including those with high-level resistance to ciprofloxacin, azithromycin and extended spectrum cephalosporins

Anomalous Dome-like Superconductivity in RE2(Cu1-xNix)5As3O2(RE=La, Pr, Nd)

Significant manifestation of interplay of superconductivity and charge density wave, spin density wave or magnetism is dome-like variation in superconducting critical temperature (Tc) for cuprate, iron-based and heavy Fermion superconductors. Overall behavior is that the ordered temperature is gradually suppressed and the Tc is enhanced under external control parameters. Many phenomena like pesudogap, quantum critical point and strange metal emerge in the different doping range. Exploring dome-shaped Tc in new superconductors is of importance to detect emergent effects. Here, we report that the observation of superconductivity in new layered Cu-based compound RE2Cu5As3O2 (RE=La, Pr, Nd), in which the Tc exhibits dome-like variation with maximum Tc of 2.5 K, 1.2 K and 1.0 K as substituting Cu by large amount of Ni ions. The transitions of T* in former two compounds can be suppressed by either Ni doping or rare earth replacement. Simultaneously, the structural parameters like As-As bond length and c/a ratio exhibit unusual variations as Ni-doping level goes through the optimal value. The robustness of superconductivity, up to 60% of Ni doping, reveals the unexpected impurity effect on inducing and enhancing superconductivity in this novel layered materialsComment: 16 pages, 5 figures. Comments are welcom