60 research outputs found
Power of Continuous Triangular Norms with Application to Intuitionistic Fuzzy Information Aggregation
The paper aims to investigate the power operation of continuous triangular
norms (t-norms) and develop some intuitionistic fuzzy information aggregation
methods. It is proved that a continuous t-norm is power stable if and only if
every point is a power stable point, and if and only if it is the minimum
t-norm, or it is strict, or it is an ordinal sum of strict t-norms. Moreover,
the representation theorem of continuous t-norms is used to obtain the
computational formula for the power of continuous t-norms. Based on the power
operation of t-norms, four fundamental operations induced by a continuous
t-norm for the intuitionistic fuzzy (IF) sets are introduced. Furthermore,
various aggregation operators, namely the IF weighted average (IFWA), IF
weighted geometric (IFWG), and IF mean weighted average and geometric (IFMWAG)
operators, are defined, and their properties are analyzed. Finally, a new
decision-making algorithm is designed based on the IFMWAG operator, which can
remove the hindrance of indiscernibility on the boundaries of some classical
aggregation operators. The practical applicability, comparative analysis, and
advantages of the study with other decision-making methods are furnished to
ascertain the efficacy of the designed method
Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability
A trustworthy reinforcement learning algorithm should be competent in solving
challenging real-world problems, including {robustly} handling uncertainties,
satisfying {safety} constraints to avoid catastrophic failures, and
{generalizing} to unseen scenarios during deployments. This study aims to
overview these main perspectives of trustworthy reinforcement learning
considering its intrinsic vulnerabilities on robustness, safety, and
generalizability. In particular, we give rigorous formulations, categorize
corresponding methodologies, and discuss benchmarks for each perspective.
Moreover, we provide an outlook section to spur promising future directions
with a brief discussion on extrinsic vulnerabilities considering human
feedback. We hope this survey could bring together separate threads of studies
together in a unified framework and promote the trustworthiness of
reinforcement learning.Comment: 36 pages, 5 figure
Accelerated Policy Evaluation: Learning Adversarial Environments with Adaptive Importance Sampling
The evaluation of rare but high-stakes events remains one of the main
difficulties in obtaining reliable policies from intelligent agents, especially
in large or continuous state/action spaces where limited scalability enforces
the use of a prohibitively large number of testing iterations. On the other
hand, a biased or inaccurate policy evaluation in a safety-critical system
could potentially cause unexpected catastrophic failures during deployment. In
this paper, we propose the Accelerated Policy Evaluation (APE) method, which
simultaneously uncovers rare events and estimates the rare event probability in
Markov decision processes. The APE method treats the environment nature as an
adversarial agent and learns towards, through adaptive importance sampling, the
zero-variance sampling distribution for the policy evaluation. Moreover, APE is
scalable to large discrete or continuous spaces by incorporating function
approximators. We investigate the convergence properties of proposed algorithms
under suitable regularity conditions. Our empirical studies show that APE
estimates rare event probability with a smaller variance while only using
orders of magnitude fewer samples compared to baseline methods in both
multi-agent and single-agent environments.Comment: 10 pages, 5 figure
What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery
Training control policies in simulation is more appealing than on real robots
directly, as it allows for exploring diverse states in a safe and efficient
manner. Yet, robot simulators inevitably exhibit disparities from the real
world, yielding inaccuracies that manifest as the simulation-to-real gap.
Existing literature has proposed to close this gap by actively modifying
specific simulator parameters to align the simulated data with real-world
observations. However, the set of tunable parameters is usually manually
selected to reduce the search space in a case-by-case manner, which is hard to
scale up for complex systems and requires extensive domain knowledge. To
address the scalability issue and automate the parameter-tuning process, we
introduce an approach that aligns the simulator with the real world by
discovering the causal relationship between the environment parameters and the
sim-to-real gap. Concretely, our method learns a differentiable mapping from
the environment parameters to the differences between simulated and real-world
robot-object trajectories. This mapping is governed by a simultaneously-learned
causal graph to help prune the search space of parameters, provide better
interpretability, and improve generalization. We perform experiments to achieve
both sim-to-sim and sim-to-real transfer, and show that our method has
significant improvements in trajectory alignment and task success rate over
strong baselines in a challenging manipulation task
lncRNA profile study reveals the mRNAs and lncRNAs associated with docetaxel resistance in breast cancer cells
Abstract Resistance to adjuvant systemic treatment, including taxanes (docetaxel and paclitaxel) is a major clinical problem for breast cancer patients. lncRNAs (long non-coding RNAs) are non-coding transcripts, which have recently emerged as important players in a variety of biological processes, including cancer development and chemotherapy resistance. However, the contribution of lncRNAs to docetaxel resistance in breast cancer and the relationship between lncRNAs and taxane-resistance genes are still unclear. Here, we performed comprehensive RNA sequencing and analyses on two docetaxel-resistant breast cancer cell lines (MCF7-RES and MDA-RES) and their docetaxel-sensitive parental cell lines. We identified protein coding genes and pathways that may contribute to docetaxel resistance. More importantly, we identified lncRNAs that were consistently up-regulated or down-regulated in both the MCF7-RES and MDA-RES cells. The co-expression network and location analyses pinpointed four overexpressed lncRNAs located within or near the ABCB1 (ATP-binding cassette subfamily B member 1) locus, which might up-regulate the expression of ABCB1. We also identified the lncRNA EPB41L4A-AS2 (EPB41L4A Antisense RNA 2) as a potential biomarker for docetaxel sensitivity. These findings have improved our understanding of the mechanisms underlying docetaxel resistance in breast cancer and have provided potential biomarkers to predict the response to docetaxel in breast cancer patients
Comprehensive genomic analysis of Oesophageal Squamous Cell Carcinoma reveals clinical relevance
Abstract Oesophageal carcinoma is the fourth leading cause of cancer-related death in China, and more than 90% of these tumours are oesophageal squamous cell carcinoma (ESCC). Although several ESCC genomic sequencing studies have identified mutated somatic genes, the number of samples in each study was relatively small, and the molecular basis of ESCC has not been fully elucidated. Here, we performed an integrated analysis of 490 tumours by combining the genomic data from 7 previous ESCC projects. We identified 18 significantly mutated genes (SMGs). PTEN, DCDC1 and CUL3 were first reported as SMGs in ESCC. Notably, the AJUBA mutations and mutational signature4 were significantly correlated with a poorer survival in patients with ESCC. Hierarchical clustering analysis of the copy number alteration (CNA) of cancer gene census (CGC) genes in ESCC patients revealed three subtypes, and subtype3 exhibited more CNAs and marked for worse prognosis compared with subtype2. Moreover, database annotation suggested that two significantly differential CNA genes (PIK3CA and FBXW7) between subtype3 and subtype2 may serve as therapeutic drug targets. This study has extended our knowledge of the genetic basis of ESCC and shed some light into the clinical relevance, which would help improve the therapy and prognosis of ESCC patients
Field-effect transistors made from solution-grown two-dimensional tellurene
The reliable production of two-dimensional crystals is essential for the
development of new technologies based on 2D materials. However, current
synthesis methods suffer from a variety of drawbacks, including limitations in
crystal size and stability. Here, we report the fabrication of large-area,
high-quality 2D tellurium (tellurene) using a substrate-free solution process.
Our approach can create crystals with a process-tunable thickness, from
monolayer to tens of nanometres, and with lateral sizes of up to 100 um. The
chiral-chain van der Waals structure of tellurene gives rise to strong in-plane
anisotropic properties and large thickness dependent shifts in Raman
vibrational modes, which is not observed in other 2D layered materials. We also
fabricate tellurene field-effect transistors, which exhibit air-stable
performance at room temperature for over two months, on off ratios on the order
of 106 and field-effect mobilities of around 700 cm2 per Vs. Furthermore, by
scaling down the channel length and integrating with high-k dielectrics,
transistors with a significant on-state current density of 1 A mm-1 are
demonstrated
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