A Numerical Approach to Solving an Inverse Heat Conduction Problem Using the Levenberg-Marquardt Algorithm

This chapter is intended to provide a numerical algorithm involving the combined use of the Levenberg-Marquardt algorithm and the Galerkin finite element method for estimating the diffusion coefficient in an inverse heat conduction problem (IHCP). In the present study, the functional form of the diffusion coefficient is an unknown priori. The unknown diffusion coefficient is approximated by the polynomial form and the present numerical algorithm is employed to find the solution. Numerical experiments are presented to show the efficiency of the proposed method

RecExplainer: Aligning Large Language Models for Recommendation Model Interpretability

Recommender systems are widely used in various online services, with embedding-based models being particularly popular due to their expressiveness in representing complex signals. However, these models often lack interpretability, making them less reliable and transparent for both users and developers. With the emergence of large language models (LLMs), we find that their capabilities in language expression, knowledge-aware reasoning, and instruction following are exceptionally powerful. Based on this, we propose a new model interpretation approach for recommender systems, by using LLMs as surrogate models and learn to mimic and comprehend target recommender models. Specifically, we introduce three alignment methods: behavior alignment, intention alignment, and hybrid alignment. Behavior alignment operates in the language space, representing user preferences and item information as text to learn the recommendation model's behavior; intention alignment works in the latent space of the recommendation model, using user and item representations to understand the model's behavior; hybrid alignment combines both language and latent spaces for alignment training. To demonstrate the effectiveness of our methods, we conduct evaluation from two perspectives: alignment effect, and explanation generation ability on three public datasets. Experimental results indicate that our approach effectively enables LLMs to comprehend the patterns of recommendation models and generate highly credible recommendation explanations.Comment: 12 pages, 8 figures, 4 table

Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations

Recommender models excel at providing domain-specific item recommendations by leveraging extensive user behavior data. Despite their ability to act as lightweight domain experts, they struggle to perform versatile tasks such as providing explanations and engaging in conversations. On the other hand, large language models (LLMs) represent a significant step towards artificial general intelligence, showcasing remarkable capabilities in instruction comprehension, commonsense reasoning, and human interaction. However, LLMs lack the knowledge of domain-specific item catalogs and behavioral patterns, particularly in areas that diverge from general world knowledge, such as online e-commerce. Finetuning LLMs for each domain is neither economic nor efficient. In this paper, we bridge the gap between recommender models and LLMs, combining their respective strengths to create a versatile and interactive recommender system. We introduce an efficient framework called InteRecAgent, which employs LLMs as the brain and recommender models as tools. We first outline a minimal set of essential tools required to transform LLMs into InteRecAgent. We then propose an efficient workflow within InteRecAgent for task execution, incorporating key components such as a memory bus, dynamic demonstration-augmented task planning, and reflection. InteRecAgent enables traditional recommender systems, such as those ID-based matrix factorization models, to become interactive systems with a natural language interface through the integration of LLMs. Experimental results on several public datasets show that InteRecAgent achieves satisfying performance as a conversational recommender system, outperforming general-purpose LLMs.Comment: 16 pages, 15 figures, 4 table

RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems

This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs). RecAI provides a suite of tools, including Recommender AI Agent, Recommendation-oriented Language Models, Knowledge Plugin, RecExplainer, and Evaluator, to facilitate the integration of LLMs into recommender systems from multifaceted perspectives. The new generation of recommender systems, empowered by LLMs, are expected to be more versatile, explainable, conversational, and controllable, paving the way for more intelligent and user-centric recommendation experiences. We hope the open-source of RecAI can help accelerate evolution of new advanced recommender systems. The source code of RecAI is available at \url{https://github.com/microsoft/RecAI}.Comment: 4 pages. Webconf 2024 demo trac

Where does stress happen? Ecological momentary assessment of daily stressors using a mobile phone app.

Despite the importance of daily stress to individuals' health and wellbeing, few studies have explored where stress happens in real time. As such, stress interventions rarely account for the environment in which stress occurs. We used ecological momentary assessment (EMA) to collect daily stress data. Thirty-three participants utilized a mobile phone-based EMA app to record stressors as they went about their daily lives. GPS coordinates were automatically collected with each stress report. Data from thematic and geographic information system (GIS) analysis were used in a chi-square analysis of stressors by location (home, work, work from home, and other) to determine if certain stressors were more prevalent in certain environments. The study found that nine daily stressors significantly differed by location. Work-related stress was reported more often at work but was also commonly experienced at home. In contrast, pets, household chores, sleep and media related stressors were reported most at home, but not experienced as often in other locations. Physical illnesses, vehicles or driving, and law and order stressors occurred most often in the 'work from home' condition. Traffic-related stress was experienced more common in 'other' environments. Study findings: 1) expand the understanding of environments in which specific stressors occur; 2) extend the nomological network of cognitive appraisal theory to include stress experienced in free-living conditions; 3) provide baseline data for potential targeted 'just-in-time' stress interventions, tailored to specific stressors in certain environments; 4) provide findings related to the 'work from home' phenomenon, increasingly popular during and after the COVID-19 pandemic

Efficacy and safety of guselkumab for the treatment of patients with moderate-to-severe plaque psoriasis: A metaanalysis of randomized clinical trials

Purpose: To conduct a systematic analysis on data from randomized controlled trials (RCTs) on different doses of guselkumab, and provide high-quality evidence for its use in the treatment of patients with moderate-to-severe plaque psoriasis (PsO). Methods: Related studies were searched using online search engines including MEDLINE, PubMed, and central registry of Cochrane controlled trials from January 2001 to October 2017. Only randomized, placebo-controlled, double-blind clinical trials involving guselkumab- and placebo-treated PsO subjects were included. Results: Five eligible double-blind, randomized, and placebo-controlled trials involving patients with moderate-to-severe PsO subjects treated with guselkumab were included. Compared with the placebo groups, the proportion of patients with improvements in Psoriasis Area and Severity Index (PASI) 75 (RR= 12.14; 95% CI= 9.11-16.16; p < 0.001); PASI 90 (RR= 23.26; 95% CI =14.57-37.13; p < 0.001), and PASI 100 (RR = 37.66; 95% CI = 15.81-89.69; p < 0.001) were significantly higher than those in guselkumab-treated groups. Furthermore, the guselkumab-treated groups showed significant decreases in Physician’s Global Assessment (PGA) score (RR = 10.46; 95% CI = 7.96-13.83; p < 0.001) and the Dermatology Life Quality Index (DLQI) score (SMD = -1.3; 95% CL = -1.4 to -1.19; p < 0.001), when compared with the placebo groups. However, there were no significant differences in adverse events (AEs) (RR = 1.01; 95% CL = 0.93-1.11; p > 0.05); severe adverse events (SAEs) (RR = 1.32; 95% CI =0.69-2.54; p > 0.05) and study discontinuations (RR = 0.79; 95% CI = 0.42-1.48; p > 0.05) between the two groups. Conclusion: This meta-analysis summarizes available evidence for the use of guselkumab in psoriasis. The results suggest that guselkumab is superior to placebo in moderate-to-severe psoriasis, and is welltolerated, effective, and safe in improving the severity of disease and quality of life. Keywords: Guselkumab, Effectiveness, Safety, Plaque psoriasis, Meta-analysis, Quality of lif

Novel Clinical and Genomic Signatures of the 2022 Monkeypox Virus

The monkeypox outbreaks started in 2022 and became an unexpected public health emergency of international concern (PHEIC). The factors that drove this neglected zoonosis in Africa into a global focus is largely unknown. Combined clinical, epidemiologic, and phylogenomic analyses indicate that substantial genome mutations, deletions, and rearrangement contributed to the sudden outbreak and unusual features in transmission and outcomes. Because no vaccine or antiviral drug is available in China, we call for immediate action and collaboration in response to the new monkeypox crisis

Orecchio: Extending Body-Language through Actuated Static and Dynamic Auricular Postures

In this paper, we propose using the auricle – the visible part of the ear – as a means of expressive output to extend body language to convey emotional states. With an initial exploratory study, we provide an initial set of dynamic and static auricular postures. Using these results, we examined the relationship between emotions and auricular postures, noting that dynamic postures involving stretching the top helix in fast (e.g., 2Hz) and slow speeds (1Hz) conveyed intense and mild pleasantness while static postures involving bending the side or top helix towards the center of the ear were associated with intense and mild unpleasantness. Based on the results, we developed a prototype (called Orrechio) with miniature motors, custommade robotic arms and other electronic components. A preliminary user evaluation showed that participants feel more comfortable using expressive auricular postures with people they are familiar with, and that it is a welcome addition to the vocabulary of human body language

Disorder induced multifractal superconductivity in monolayer niobium dichalcogenides

The interplay between disorder and superconductivity is a subtle and fascinating phenomenon in quantum many body physics. The conventional superconductors are insensitive to dilute nonmagnetic impurities, known as the Anderson's theorem. Destruction of superconductivity and even superconductor-insulator transitions occur in the regime of strong disorder. Hence disorder-enhanced superconductivity is rare and has only been observed in some alloys or granular states. Because of the entanglement of various effects, the mechanism of enhancement is still under debate. Here we report well-controlled disorder effect in the recently discovered monolayer NbSe$_2$ superconductor. The superconducting transition temperatures of NbSe$_2$ monolayers are substantially increased by disorder. Realistic theoretical modeling shows that the unusual enhancement possibly arises from the multifractality of electron wave functions. This work provides the first experimental evidence of the multifractal superconducting state