Discriminative Sentence Modeling for Story Ending Prediction

Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense knowledge. To tackle this task, we propose a new neural network called Diff-Net for better modeling the differences of each ending in this task. The proposed model could discriminate two endings in three semantic levels: contextual representation, story-aware representation, and discriminative representation. Experimental results on the Story Cloze Test dataset show that the proposed model siginificantly outperforms various systems by a large margin, and detailed ablation studies are given for better understanding our model. We also carefully examine the traditional and BERT-based models on both SCT v1.0 and v1.5 with interesting findings that may potentially help future studies.Comment: 8 pages, accepted as a conference paper at AAAI 202

Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning

Chain-of-thought prompting~(CoT) and tool augmentation have been validated in recent work as effective practices for improving large language models~(LLMs) to perform step-by-step reasoning on complex math-related tasks. However, most existing math reasoning datasets may be not able to fully evaluate and analyze the ability of LLMs in manipulating tools and performing reasoning, as they may only require very few invocations of tools or miss annotations for evaluating intermediate reasoning steps. To address the issue, we construct \textbf{CARP}, a new Chinese dataset consisting of 4,886 computation-intensive algebra problems with formulated annotations on intermediate steps. In CARP, we test four LLMs with CoT prompting, and find that they are all prone to make mistakes at the early steps of the solution, leading to wrong answers. Based on this finding, we propose a new approach that can deliberate the reasoning steps with tool interfaces, namely \textbf{DELI}. In DELI, we first initialize a step-by-step solution based on retrieved exemplars, then iterate two deliberation procedures that check and refine the intermediate steps of the generated solution, from the perspectives of tool manipulation and natural language reasoning, until obtaining converged solutions or reaching the maximum turn. Experimental results on CARP and six other datasets show that the proposed DELI mostly outperforms competitive baselines, and can further boost the performance of existing CoT methods. Our data and code are available in \url{https://github.com/RUCAIBox/CARP}.Comment: 17 pages, working in progres

Regulating Higher-Order Organization through the Synergy of Two Self-Sorted Assemblies

The extracellular matrix (ECM) is the natural fibrous scaffold that regulates cell behavior in a hierarchical manner. By mimicking the dynamic and reciprocal interactions between ECM and cells, higher-order molecular self-assembly (SA), mediated through the dynamic growth of scaffold-like nanostructures assembled by different molecular components, was developed. Designed and synthesized were two self-sorted coumarin-based gelators, a peptide molecule and a benzoate molecule, which self-assemble into nanofibers and nanobelts, respectively, with different dynamic profiles. Upon the dynamic growth of the fibrous scaffold assembled from peptide gelators, nanobelts assembled from benzoate gelators transform into a layer-by-layer nanosheet, reaching ninefold increase in height. By using light and an enzyme, the spatial-temporal growth of the scaffold can be modified, leading to in situ height regulation of the higher-order architecture

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce

Rigorous monitoring is necessary to guide food system transformation in the countdown to the 2030 global goals

Food systems that support healthy diets in sustainable, resilient, just, and equitable ways can engender progress in eradicating poverty and malnutrition; protecting human rights; and restoring natural resources. Food system activities have contributed to great gains for humanity but have also led to significant challenges, including hunger, poor diet quality, inequity, and threats to nature. While it is recognized that food systems are central to multiple global commitments and goals, including the Sustainable Development Goals, current trajectories are not aligned to meet these objectives. As mounting crises further stress food systems, the consequences of inaction are clear. The goal of food system transformation is to generate a future where all people have access to healthy diets, which are produced in sustainable and resilient ways that restore nature and deliver just, equitable livelihoods. A rigorous, science-based monitoring framework can support evidence-based policymaking and the work of those who hold key actors accountable in this transformation process. Monitoring can illustrate current performance, facilitate comparisons across geographies and over time, and track progress. We propose a framework centered around five thematic areas related to (1) diets, nutrition, and health; (2) environment and climate; and (3) livelihoods, poverty, and equity; (4) governance; and (5) resilience and sustainability. We hope to call attention to the need to monitor food systems globally to inform decisions and support accountability for better governance of food systems as part of the transformation process. Transformation is possible in the next decade, but rigorous evidence is needed in the countdown to the 2030 SDG global goals

Hypoxia in Aging and Aging-Related Diseases: Mechanism and Therapeutic Strategies

As the global aging process continues to lengthen, aging-related diseases (e.g., chronic obstructive pulmonary disease (COPD), heart failure) continue to plague the elderly population. Aging is a complex biological process involving multiple tissues and organs and is involved in the development and progression of multiple aging-related diseases. At the same time, some of these aging-related diseases are often accompanied by hypoxia, chronic inflammation, oxidative stress, and the increased secretion of the senescence-associated secretory phenotype (SASP). Hypoxia seems to play an important role in the process of inflammation and aging, but is often neglected in advanced clinical research studies. Therefore, we have attempted to elucidate the role played by different degrees and types of hypoxia in aging and aging-related diseases and their possible pathways, and propose rational treatment options based on such mechanisms for reference

Multi Perspective Reflection and Integration Construction Strategy of Social Psychological Service System

Social psychological service system is complex, and the construction of social psychological service system is a complex systematic project. From the perspective of history, management, philosophy, society and psychology, social psychological service is not only the inevitable result of the further development of mental health education, but also the practical needs of social governance. In the final analysis, it is a practical activity based on people, for people and serving people. It should not only take into account the complex society and people’s social attributes, but also follow the general principles of psychology. Social psychological services are not equal to mental health services, social risk prevention and control or psychological counseling services. They are more than eliminating psychological problems, and the service cannot rely too much on psychological professionals. The scientific construction of social psychological service system needs an all-round integration from the aspects of concept, mechanism, resources, content and ways, so as to better serve people and society

The coupled evolution of mid- to late Holocene temperature and moisture in the southeast Qaidam Basin

The southeast Qaidam Basin (SQB) lies near the boundary between the modern-day Asian summer monsoon (ASM) and mid-latitude westerly jet, and the paleoclimate variation in this region can be affected by these two atmospheric circulation systems. Reconstructions of paleotemperature and palaeohydrology are therefore critical to constraining the driving forces of climate in this region, where the ecological environment is fragile. Here, we analyzed glycerol dialkyl glycerol tetraethers (GDGTs), microbial membrane lipids occurring ubiquitously in aquatic and terrestrial environments across the globe, in an aeolian sediment profile over the last 7000 years from Xiangride Town (XRD) of southeast Qaidam Basin (SQB), China. The temperature record was generated using the global calibration of MAT(mr) based on branched GDGTs (brGDGTs), whereas the palaeohydrological condition was reconstructed from the relative abundance of isoprenoid GDGTs vs. brGDGTs (R-i/b), the cyclisation index of brGDGTs (CBT), and the brGDGT-based pH indices. The results show that the paleoclimate during the mid-Holocene was relatively warmer and wetter in the SQB. Afterwards it was a trend to a cold and dry climate. Temperature variation was highly coupled with the moisture change during the mid- to late Holocene, as opposed to some previous studies showing a warm-dry and cold-wet climate pattern in the northern Qaidam basin. The palaeohydrological evolution agrees with the precipitation pattern of the Chinese loess plateau but opposes to that in Northeastern China, the middle reaches of Yangtze river and Arid Central Asia (ACA), implying that the ASM strength is the driving force of precipitation evolution during the mid- to late Holocene in the SQB and the spatial heterogeneity of the mid- to late Holocene precipitation pattern across China is remarkable. A rapid cold and drought event at around 4 ka before present (BP) was identified, which might be a key factor that caused the decline in the agricultural civilization during the late Neolithic Age in northwestern China