Robust MMSE Precoding Strategy for Multiuser MIMO Relay Systems with Switched Relaying and Side Information

In this work, we propose a minimum mean squared error (MMSE) robust base station (BS) precoding strategy based on switched relaying (SR) processing and limited transmission of side information for interference suppression in the downlink of multiuser multiple-input multiple-output (MIMO) relay systems. The BS and the MIMO relay station (RS) are both equipped with a codebook of interleaving matrices. For a given channel state information (CSI) the selection function at the BS chooses the optimum interleaving matrix from the codebook based on two optimization criteria to design the robust precoder. Prior to the payload transmission the BS sends the index corresponding to the selected interleaving matrix to the RS, where the best interleaving matrix is selected to build the optimum relay processing matrix. The entries of the codebook are randomly generated unitary matrices. Simulation results show that the performance of the proposed techniques is significantly better than prior art in the case of imperfect CSI.

Episodic Reinforcement Learning with Expanded State-reward Space

Empowered by deep neural networks, deep reinforcement learning (DRL) has demonstrated tremendous empirical successes in various domains, including games, health care, and autonomous driving. Despite these advancements, DRL is still identified as data-inefficient as effective policies demand vast numbers of environmental samples. Recently, episodic control (EC)-based model-free DRL methods enable sample efficiency by recalling past experiences from episodic memory. However, existing EC-based methods suffer from the limitation of potential misalignment between the state and reward spaces for neglecting the utilization of (past) retrieval states with extensive information, which probably causes inaccurate value estimation and degraded policy performance. To tackle this issue, we introduce an efficient EC-based DRL framework with expanded state-reward space, where the expanded states used as the input and the expanded rewards used in the training both contain historical and current information. To be specific, we reuse the historical states retrieved by EC as part of the input states and integrate the retrieved MC-returns into the immediate reward in each interactive transition. As a result, our method is able to simultaneously achieve the full utilization of retrieval information and the better evaluation of state values by a Temporal Difference (TD) loss. Empirical results on challenging Box2d and Mujoco tasks demonstrate the superiority of our method over a recent sibling method and common baselines. Further, we also verify our method's effectiveness in alleviating Q-value overestimation by additional experiments of Q-value comparison.Comment: Accepted at AAMAS'2

Sequential Action-Induced Invariant Representation for Reinforcement Learning

How to accurately learn task-relevant state representations from high-dimensional observations with visual distractions is a realistic and challenging problem in visual reinforcement learning. Recently, unsupervised representation learning methods based on bisimulation metrics, contrast, prediction, and reconstruction have shown the ability for task-relevant information extraction. However, due to the lack of appropriate mechanisms for the extraction of task information in the prediction, contrast, and reconstruction-related approaches and the limitations of bisimulation-related methods in domains with sparse rewards, it is still difficult for these methods to be effectively extended to environments with distractions. To alleviate these problems, in the paper, the action sequences, which contain task-intensive signals, are incorporated into representation learning. Specifically, we propose a Sequential Action--induced invariant Representation (SAR) method, in which the encoder is optimized by an auxiliary learner to only preserve the components that follow the control signals of sequential actions, so the agent can be induced to learn the robust representation against distractions. We conduct extensive experiments on the DeepMind Control suite tasks with distractions while achieving the best performance over strong baselines. We also demonstrate the effectiveness of our method at disregarding task-irrelevant information by deploying SAR to real-world CARLA-based autonomous driving with natural distractions. Finally, we provide the analysis results of generalization drawn from the generalization decay and t-SNE visualization. Code and demo videos are available at https://github.com/DMU-XMU/SAR.git

Association between chronic pain and attrition: a prospective analysis of a national sample of midlife adults in the USA, 2004–2014

BackgroundHealth conditions of participants can significantly affect longitudinal drop-out in population-based epidemiological surveys, yet few studies have examined the association between chronic pain (CP) and follow-up attrition.MethodsThe Midlife in the United States study (MIDUS) was used to explore the longitudinal association between CP and survey attrition. CP was assessed by three measures: the presence of CP, CP interference and the number of pain sites at MIDUS 2. The types of sample attrition at MIDUS 3 encompassed several categories: complete, refusal to participate, inability to participate due to physical or mental constraints, deceased, non-working numbers, participants consistently unavailable for interviews, global refusal or withdrew from the study and not fielded. Multinomial logistic regression was employed to examine these relationships and to explore the moderation effects of sociodemographic variables and multiple chronic conditions on these associations.ResultsHigh-interference pain was associated with a 162% increased risk (RR 2.62, 95% CI 1.12 to 6.16, p=0.026) of being physically and mentally unable to participate in MIDUS 3. Individuals reporting the presence of CP (RR 0.65, 95% CI 0.45 to 0.95, p=0.028) and those with three or more CP sites (RR 0.48, 95% CI 0.27 to 0.87, p=0.016) were less likely to refuse participation in MIDUS 3. However, no further significant associations or moderating effects were identified.ConclusionPopulation-based epidemiological surveys may be susceptible to attrition bias from participants with CP, necessitating the adoption of adaptive survey methodologies.</jats:sec

The mediating effect of allostatic load on the association between life course socioeconomic disadvantage and chronic pain: a prospective finding from the National Survey of Midlife Development in the United States

BackgroundSocioeconomic disadvantages (SEDs) are associated with chronic pain (CP) and allostatic load (AL). Few prospective population-based studies have examined the relationship between life course SED, CP interference, and CP widespreadness, and there is no prospective population-based study on whether AL mediates the association between SED and CP.ObjectiveIn this study, we investigated whether the prospective effect of SED on CP at Midlife in the United States (MIDUS) 3 is consistent with the accumulation of risk model and social mobility model, using the National Survey of MIDUS (n = 593). To prepare for the mediation analysis, we tested (1) whether SED would be prospectively associated with AL in the MIDUS 2 biomarker project, (2) whether AL would be prospectively associated with CP, and (3) whether childhood, as a critical period, moderated the association between AL and CP. In addition, the mediating effect of AL on the association between SED and CP was examined.MethodSED was measured using cumulative scores and disadvantage trajectories derived from latent class trajectory modeling (LCTM). After multiple imputations, analyses were conducted using multinomial logistic regression for CP and negative binomial regression for AL, respectively. Finally, mediation analyses and moderated mediation analyses were performed.ResultsLCTM identified three SED trajectories, namely, constant low, high to low, and medium to high. The results showed that proximal cumulative SED was associated with high-interference CP. Furthermore, compared with the group with constant low SED, the group with medium-to-high SED was significantly associated with high-interference pain and experienced pain in at least three different sites. Cumulative SED and deteriorating SED trajectories were associated with higher AL, consistent with previous studies. Furthermore, childhood SED moderated the effect of AL on CP widespreadness and unexpectedly demonstrated a protective effect, while other associations between AL and CP were not significant. Subsequent mediation analysis did not yield statistically significant evidence.ConclusionsPeople who experienced more recent SED or increasing disadvantage throughout their lives were more likely to suffer from CP, and this association was not mediated by physiological system dysregulation caused by chronic stress. Therefore, measures to alleviate AL may not be effective in protecting socioeconomically disadvantaged populations from CP

Life course socioeconomic status, chronic pain, and the mediating role of allostatic load: findings from the midlife in the United States

IntroductionLow socioeconomic status (SES) has been linked to chronic pain (CP); however, the mechanisms by which SES over the life course influences downstream CP outcomes remain unclear.MethodsThis study utilizes data from the Midlife in the United States (MIDUS) survey, a prospective sample of community-dwelling individuals (N=781), to investigate the chain of risk additive model of SES in relation to CP. Additionally, the study examines the mediating role of allostatic load (AL) in the relationship between life course SES and CP. Confirmatory factor analysis was employed to capture the multidimensionality of life course SES and path analysis was used to examine the direct and indirect effects on CP. AL was computed by quartile-based summation and by latent class analysis.ResultsResults indicated lower SES in MIDUS 2 was associated with greater high-interference CP odds in MIDUS 3 (OR=1.069, 95% CI=1.006-1.136, P &amp;lt; 0.05) and no association was found between distal SES and levels of CP interference. Similarly, no significant relationship was observed between SES and the number of CP locations. Additionally, no additive effects of SES were found, and AL did not present mediation effects on the association between life course SES and CP.DiscussionThe present study emphasizes the importance of directly proximal effects of SES on CP, underscoring the need for equitable distribution of health resources and the implementation of policies focused on diminishing socioeconomic inequalities. Further research is needed to examine alternative pathways by which proximal SES impact CP.</jats:sec

Towards Faster k-Nearest-Neighbor Machine Translation

Recent works have proven the effectiveness of k-nearest-neighbor machine translation(a.k.a kNN-MT) approaches to produce remarkable improvement in cross-domain translations. However, these models suffer from heavy retrieve overhead on the entire datastore when decoding each token. We observe that during the decoding phase, about 67% to 84% of tokens are unvaried after searching over the corpus datastore, which means most of the tokens cause futile retrievals and introduce unnecessary computational costs by initiating k-nearest-neighbor searches. We consider this phenomenon is explainable in linguistics and propose a simple yet effective multi-layer perceptron (MLP) network to predict whether a token should be translated jointly by the neural machine translation model and probabilities produced by the kNN or just by the neural model. The results show that our method succeeds in reducing redundant retrieval operations and significantly reduces the overhead of kNN retrievals by up to 53% at the expense of a slight decline in translation quality. Moreover, our method could work together with all existing kNN-MT systems.Comment: 7 page

Allostatic load and chronic pain: a prospective finding from the national survey of midlife development in the United States, 2004-2014.

Background Previous research has demonstrated a correlation between chronic stress and chronic pain (CP). However, there have been few studies examining the prospective association of allostatic load (AL)-the biological processes related to stress-with CP. Methods We firstly conducted latent class analysis to identify phenotypes of AL using a community-dwelling sample, the Midlife in the United States. Multinomial logistic regression models were used to examine the prospective association between phenotypes of AL at MIDUS 2 biomarker project and the presence of CP, CP interference and the number of CP sites at MIDUS 3. Results Three phenotypes of AL, low biological dysregulation, parasympathetic dysregulation and metabolic dysregulation, were identified. Compared to low biological dysregulation group, participants experiencing metabolic dysregulation phenotype of AL at MIDUS 2 had higher risks of having high-interference CP (RRR = 2.00, 95% CI: 1.06, 3.79, P < 0.05) and 3 or more CP sites (RRR = 2.03, 95% CI: 1.08, 3.83, P < 0.05) at MIDUS 3. Conclusion The findings indicate that focusing on mitigating the metabolic dysfunction phenotype of AL has the potential to be an efficacious strategy for alleviating future CP bodily widespreadness and high CP interference

BJTU-WeChat's Systems for the WMT22 Chat Translation Task

This paper introduces the joint submission of the Beijing Jiaotong University and WeChat AI to the WMT'22 chat translation task for English-German. Based on the Transformer, we apply several effective variants. In our experiments, we utilize the pre-training-then-fine-tuning paradigm. In the first pre-training stage, we employ data filtering and synthetic data generation (i.e., back-translation, forward-translation, and knowledge distillation). In the second fine-tuning stage, we investigate speaker-aware in-domain data generation, speaker adaptation, prompt-based context modeling, target denoising fine-tuning, and boosted self-COMET-based model ensemble. Our systems achieve 0.810 and 0.946 COMET scores. The COMET scores of English-German and German-English are the highest among all submissions.Comment: Accepted by WMT 2022 as a system pape

Cross-Lingual Knowledge Editing in Large Language Models

Knowledge editing aims to change language models' performance on several special cases (i.e., editing scope) by infusing the corresponding expected knowledge into them. With the recent advancements in large language models (LLMs), knowledge editing has been shown as a promising technique to adapt LLMs to new knowledge without retraining from scratch. However, most of the previous studies neglect the multi-lingual nature of some main-stream LLMs (e.g., LLaMA, ChatGPT and GPT-4), and typically focus on monolingual scenarios, where LLMs are edited and evaluated in the same language. As a result, it is still unknown the effect of source language editing on a different target language. In this paper, we aim to figure out this cross-lingual effect in knowledge editing. Specifically, we first collect a large-scale cross-lingual synthetic dataset by translating ZsRE from English to Chinese. Then, we conduct English editing on various knowledge editing methods covering different paradigms, and evaluate their performance in Chinese, and vice versa. To give deeper analyses of the cross-lingual effect, the evaluation includes four aspects, i.e., reliability, generality, locality and portability. Furthermore, we analyze the inconsistent behaviors of the edited models and discuss their specific challenges