Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning

Deep reinforcement learning (DRL) breaks through the bottlenecks of traditional reinforcement learning (RL) with the help of the perception capability of deep learning and has been widely applied in real-world problems.While model-free RL, as a class of efficient DRL methods, performs the learning of state representations simultaneously with policy learning in an end-to-end manner when facing large-scale continuous state and action spaces. However, training such a large policy model requires a large number of trajectory samples and training time. On the other hand, the learned policy often fails to generalize to large-scale action spaces, especially for the continuous action spaces. To address this issue, in this paper we propose an efficient policy learning method in latent state and action spaces. More specifically, we extend the idea of state representations to action representations for better policy generalization capability. Meanwhile, we divide the whole learning task into learning with the large-scale representation models in an unsupervised manner and learning with the small-scale policy model in the RL manner.The small policy model facilitates policy learning, while not sacrificing generalization and expressiveness via the large representation model. Finally,the effectiveness of the proposed method is demonstrated by MountainCar,CarRacing and Cheetah experiments

Face Recognition with Facial Occlusion Based on Local Cycle Graph Structure Operator

Facial occlusion is a difficulty in the field of face recognition. The lack of features caused by occlusion may reduce the face recognition rate greatly. How to extract the identified features from the occluded faces has a profound effect on face recognition. This chapter presents a Local Cycle Graph Structure (LCGS) operator, which makes full use of the information of the pixels around the target pixel with its neighborhood of 3 × 3. Thus, the recognition with the extracted features is more efficient. We apply the extreme learning machine (ELM) classifier to train and test the features extracted by LCGS algorithm. In the experiment, we use the olivetti research laboratory (ORL) database to simulate occlusion randomly and use the AR database for physical occlusion. Physical coverings include scarves and sunglasses. Experimental results demonstrate that our algorithm yields a state-of-the-art performance

Impaired large-scale cortico–hippocampal network connectivity, including the anterior temporal and posterior medial systems, and its associations with cognition in patients with first-episode schizophrenia

Background and objectiveThe cortico–hippocampal network is an emerging neural framework with striking evidence that it supports cognition in humans, especially memory; this network includes the anterior temporal (AT) system, the posterior medial (PM) system, the anterior hippocampus (aHIPPO), and the posterior hippocampus (pHIPPO). This study aimed to detect aberrant patterns of functional connectivity within and between large-scale cortico–hippocampal networks in first-episode schizophrenia patients compared with a healthy control group via resting-state functional magnetic resonance imaging (rs-fMRI) and to explore the correlations of these aberrant patterns with cognition.MethodsA total of 86 first-episode, drug-naïve schizophrenia patients and 102 healthy controls (HC) were recruited to undergo rs-fMRI examinations and clinical evaluations. We conducted large-scale edge-based network analysis to characterize the functional architecture of the cortico–hippocampus network and investigate between-group differences in within/between-network functional connectivity. Additionally, we explored the associations of functional connectivity (FC) abnormalities with clinical characteristics, including scores on the Positive and Negative Syndrome Scale (PANSS) and cognitive scores.ResultsCompared with the HC group, schizophrenia patients exhibited widespread alterations to within-network FC of the cortico–hippocampal network, with decreases in FC involving the precuneus (PREC), amygdala (AMYG), parahippocampal cortex (PHC), orbitofrontal cortex (OFC), perirhinal cortex (PRC), retrosplenial cortex (RSC), posterior cingulate cortex (PCC), angular gyrus (ANG), aHIPPO, and pHIPPO. Schizophrenia patients also showed abnormalities in large-scale between-network FC of the cortico–hippocampal network, in the form of significantly decreased FC between the AT and the PM, the AT and the aHIPPO, the PM and the aHIPPO, and the aHIPPO and the pHIPPO. A number of these signatures of aberrant FC were correlated with PANSS score (positive, negative, and total score) and with scores on cognitive test battery items, including attention/vigilance (AV), working memory (WM), verbal learning and memory (Verb_Lrng), visual learning and memory (Vis_Lrng), reasoning and problem-solving (RPS), and social cognition (SC).ConclusionSchizophrenia patients show distinct patterns of functional integration and separation both within and between large-scale cortico–hippocampal networks, reflecting a network imbalance of the hippocampal long axis with the AT and PM systems, which regulate cognitive domains (mainly Vis_Lrng, Verb_Lrng, WM, and RPS), and particularly involving alterations to FC of the AT system and the aHIPPO. These findings provide new insights into the neurofunctional markers of schizophrenia

Second-line therapy for patients with steroid-refractory aGVHD: systematic review and meta-analysis of randomized controlled trials

ObjectiveSteroids-refractory (SR) acute graft-versus-host disease (aGVHD) is a life-threatening condition in patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT), but the optimal second-line therapy still has not been established. We aimed to perform a systematic review and meta-analysis of randomized controlled trials (RCTs) to compare the efficacy and safety of different second-line therapy regimens.MethodsLiterature search in MEDLINE, Embase, Cochrane Library and China Biology Medicine databases were performed to retrieve RCTs comparing the efficacy and safety of different therapy regimens for patients with SR aGVHD. Meta-analysis was conducted with Review Manager version 5.3. The primary outcome is the overall response rate (ORR) at day 28. Pooled relative risk (RR) and 95% confidence interval (CI) were calculated with the Mantel-Haenszel method.ResultsEight eligible RCTs were included, involving 1127 patients with SR aGVHD and a broad range of second-line therapy regimens. Meta-analysis of 3 trials investigating the effects of adding mesenchymal stroma cells (MSCs) to other second-line therapy regimens suggested that the addition of MSCs is associated with significantly improvement in ORR at day 28 (RR = 1.15, 95% CI = 1.01–1.32, P = 0.04), especially in patients with severe (grade III–IV or grade C–D) aGVHD (RR = 1.26, 95% CI = 1.04–1.52, P = 0.02) and patients with multiorgan involved (RR = 1.27, 95% CI = 1.05–1.55, P = 0.01). No significant difference was observed betwwen the MSCs group and control group in consideration of overall survival and serious adverse events. Treatment outcomes of the other trials were comprehensively reviewed, ruxolitinib showed significantly higher ORR and complete response rate at day 28, higher durable overall response at day 56 and longer failure-free survival in comparison with other regimens; inolimomab shows similar 1-year therapy success rate but superior long-term overall survial in comparison with anti-thymocyte globulin, other comparisons did not show significant differences in efficacy.ConclusionsAdding MSCs to other second-line therapy regimens is associated with significantly improved ORR, ruxolitinib showed significantly better efficacy outcomes in comparison with other regimens in patients with SR aGVHD. Further well-designed RCTs and integrated studies are required to determine the optimal treatment.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022342487

Design Challenges of Intra- and Inter- Chiplet Interconnection

In a chiplet-based many-core system, intra- and inter- chiplet interconnection is key to system performance and power consumption. There are a few challenges in intra- and inter- chiplet interconnection network: 1) Fast and accurate simulation is necessary to analyze the performance metrics. 2) Efficient network architecture for inter- and intra- chiplet is necessary, including topology, PHY design and deadlock free routing algorithms, etc. 3) Deep learning based AI systems are demanding more computation power, which calls for the need of efficient and low power chiplet-based systems. This paper proposes network designs to address these challenges and provides future research directions

CRISPR-Cas9-Based Functional Interrogation of Unconventional Translatome Reveals Human Cancer Dependency on Cryptic Non-Canonical Open Reading Frames

Emerging evidence suggests that cryptic translation beyond the annotated translatome produces proteins with developmental or physiological functions. However, functions of cryptic non-canonical open reading frames (ORFs) in cancer remain largely unknown. To fill this gap and systematically identify colorectal cancer (CRC) dependency on non-canonical ORFs, we apply an integrative multiomic strategy, combining ribosome profiling and a CRISPR-Cas9 knockout screen with large-scale analysis of molecular and clinical data. Many such ORFs are upregulated in CRC compared to normal tissues and are associated with clinically relevant molecular subtypes. We confirm the in vivo tumor-promoting function of the microprotein SMIMP, encoded by a primate-specific, long noncoding RNA, the expression of which is associated with poor prognosis in CRC, is low in normal tissues and is specifically elevated in CRC and several other cancer types. Mechanistically, SMIMP interacts with the ATPase-forming domains of SMC1A, the core subunit of the cohesin complex, and facilitates SMC1A binding to cis-regulatory elements to promote epigenetic repression of the tumor-suppressive cell cycle regulators encoded by CDKN1A and CDKN2B. Thus, our study reveals a cryptic microprotein as an important component of cohesin-mediated gene regulation and suggests that the \u27dark\u27 proteome, encoded by cryptic non-canonical ORFs, may contain potential therapeutic or diagnostic targets