Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System

With the continuous increase of users and items, conventional recommender systems trained on static datasets can hardly adapt to changing environments. The high-throughput data requires the model to be updated in a timely manner for capturing the user interest dynamics, which leads to the emergence of streaming recommender systems. Due to the prevalence of deep learning-based recommender systems, the embedding layer is widely adopted to represent the characteristics of users, items, and other features in low-dimensional vectors. However, it has been proved that setting an identical and static embedding size is sub-optimal in terms of recommendation performance and memory cost, especially for streaming recommendations. To tackle this problem, we first rethink the streaming model update process and model the dynamic embedding size search as a bandit problem. Then, we analyze and quantify the factors that influence the optimal embedding sizes from the statistics perspective. Based on this, we propose the \textbf{D}ynamic \textbf{E}mbedding \textbf{S}ize \textbf{S}earch (\textbf{DESS}) method to minimize the embedding size selection regret on both user and item sides in a non-stationary manner. Theoretically, we obtain a sublinear regret upper bound superior to previous methods. Empirical results across two recommendation tasks on four public datasets also demonstrate that our approach can achieve better streaming recommendation performance with lower memory cost and higher time efficiency.Comment: Accepted for publication on CIKM202

Transformer as Linear Expansion of Learngene

We propose expanding the shared Transformer module to produce and initialize Transformers of varying depths, enabling adaptation to diverse resource constraints. Drawing an analogy to genetic expansibility, we term such module as learngene. To identify the expansion mechanism, we delve into the relationship between the layer's position and its corresponding weight value, and find that linear function appropriately approximates this relationship. Building on this insight, we present Transformer as Linear Expansion of learnGene (TLEG), a novel approach for flexibly producing and initializing Transformers of diverse depths. Specifically, to learn learngene, we firstly construct an auxiliary Transformer linearly expanded from learngene, after which we train it through employing soft distillation. Subsequently, we can produce and initialize Transformers of varying depths via linearly expanding the well-trained learngene, thereby supporting diverse downstream scenarios. Extensive experiments on ImageNet-1K demonstrate that TLEG achieves comparable or better performance in contrast to many individual models trained from scratch, while reducing around 2x training cost. When transferring to several downstream classification datasets, TLEG surpasses existing initialization methods by a large margin (e.g., +6.87% on iNat 2019 and +7.66% on CIFAR-100). Under the situation where we need to produce models of varying depths adapting for different resource constraints, TLEG achieves comparable results while reducing around 19x parameters stored to initialize these models and around 5x pre-training costs, in contrast to the pre-training and fine-tuning approach. When transferring a fixed set of parameters to initialize different models, TLEG presents better flexibility and competitive performance while reducing around 2.9x parameters stored to initialize, compared to the pre-training approach

Synthesis of Well-Defined, Brush-Type, Amphiphilic [Poly(styrene-co-2-hydroxyethyl methacrylate)-graft- Poly(e-caprolactone)]-b-Poly(ethylene oxide)-b- [Poly(styrene-co-2-hydroxyethyl methacrylate)-graft- Poly(e-caprolactone)] and Its Aggregation Behavior

ABSTRACT: Brush-type, amphiphilic [poly(styrene-co-2-hydroxyethyl methacrylate)-graft-poly(e-caprolactone)]-b-poly(ethylene oxide)-b-[poly(styrene-co-2-hydroxyethyl methacrylate)-graft-poly(e-caprolactone)] was successfully synthesized via consecutive ringopening anionic polymerization, reversible addition-fragmentation chain transfer (RAFT) polymerization, and coordination-insertion ring-opening polymerization (ROP). Two poly (ethylene oxide) macro-RAFT agents with two 3-benzylsulfanylthiocarbonylsufanyl propionic acid end groups were prepared by the reaction of Poly(ethylene oxide) with hydroxyl group at two ends [HO-PEO-OH] with 3-benzylsulfanylthiocarbonylsufanyl propionic acid chloride in the presence of pyridine; their molecular weights were 4840 and 8570 g/mol, and their molecular weight distributions were 1.07 and 1.09, respectively. The obtained macro-RAFT agents were used to mediate the copolymerization of styrene and 2-hydroxyethyl methacrylate with 2,2-azobisisobutyronitrile as the initiator and dimethylformamide as the solvent. The hydroxyl groups of the 2-hydroxyethyl methacrylate units of the resulting triblock copolymers then initiated the ROP of e-caprolactone in the presence of Sn(Oct) 2 at 100 8C in toluene. It was determined that the RAFT process was controllable. The self-assembled morphologies of the copolymers varied from rods to pearl necklaces and vesicles with an increase in the water concentration in tetrahydrofuran from 22.0 to 25.7, 29.6, and 39.0%, and the morphologies were also dependent on the molecular weight and chain structure of the copolymers

Understanding the planning of LLM agents: A survey

As Large Language Models (LLMs) have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention. This survey provides the first systematic view of LLM-based agents planning, covering recent works aiming to improve planning ability. We provide a taxonomy of existing works on LLM-Agent planning, which can be categorized into Task Decomposition, Plan Selection, External Module, Reflection and Memory. Comprehensive analyses are conducted for each direction, and further challenges for the field of research are discussed.Comment: 9 pages, 2 tables, 2 figure

Beneficial effects of Apelin-13 on metabolic diseases and exercise

Apelin, a novel endogenous ligand of the G-protein-coupled receptor APJ, is encoded by the APLN gene and can be hydrolyzed into multiple subtypes, with Apelin-13 being one of the most active subtypes of the Apelin family. Recent studies have revealed that Apelin-13 functions as an adipokine that participates in the regulation of different biological processes, such as oxidative stress, inflammation, apoptosis, and energy metabolism, thereby playing an important role in the prevention and treatment of various metabolic diseases. However, the results of recent studies on the association between Apelin-13 and various metabolic states remain controversial. Furthermore, Apelin-13 is regulated or influenced by various forms of exercise and could therefore be categorized as a new type of exercise-sensitive factor that attenuates metabolic diseases. Thus, in this review, our purpose was to focus on the relationship between Apelin-13 and related metabolic diseases and the regulation of response movements, with particular reference to the establishment of a theoretical basis for improving and treating metabolic diseases

Migration and transformation of dissolved carbon during accumulated cyanobacteria decomposition in shallow eutrophic lakes: a simulated microcosm study

The decomposition processes of accumulated cyanobacteria can release large amounts of organic carbon and affect the carbon cycling in shallow eutrophic lakes. However, the migration and transformation mechanisms of dissolved carbon (DC) require further study and discussion. In this study, a 73-day laboratory microcosm experiment using suction samplers (Rhizon and syringe) was conducted to understand the migration and transformation of DC during the cyanobacteria decomposition. The decomposition of cyanobacteria biomass caused anoxic and reduction conditions, and changed the acid-base environment in the water column. During the early incubation (days 0–18), a large amount of cyanobacteria-derived particulate organic matter (POM) was decomposed into dissolved organic carbon (DOC) in the overlying water, reaching the highest peak value of 1.82 g L−1 in the treatment added the high cyanobacteria biomass (470 g). After 18 days of incubation, the mineralization of increased DOC to dissolved inorganic carbon (DIC) maintained a high DIC level of overlying water in treatments added cyanobacteria biomass. The treatment added the medium cyanobacteria biomass (235 g) presented the lower DOC/total dissolved carbon ratio than the high cyanobacteria biomass associated with the lower mineralization from DOC to DIC. Due to the concentration differences of DIC at water-sediment interface, the main migration of DIC from pore water to overlying water occurred in the treatment without added cyanobacteria biomass. However, the treatments added the cyanobacteria biomass presented the obvious diffusion of DOC and the low migration of DIC at the water-sediment interface. The diffusive fluxes of DOC at the water-sediment interface increased with the cyanobacteria biomass added, reaching the maximum value of 411.01 mg/(m2·d) in the treatment added the high cyanobacteria biomass. In the overlying water, the group added the sediment and medium cyanobacteria biomass presented a faster degradation of cyanobacteria-derived POM to DOC and a higher mineralization level of DOC to DIC than added the medium cyanobacteria biomass without sediment. Therefore, during accumulated cyanobacteria decomposition, the biomass of accumulated cyanobacteria and sediment property can influence the migration and transformation of DC, playing an important role in carbon cycling in shallow eutrophic lakes

Role of SIRT3 in bone homeostasis and its application in preventing and treating bone diseases

Bone homeostasis refers to the balance between osteoblast-mediated bone formation and osteoclast-mediated bone resorption and the maintenance of stable bone mass. SIRT3 is a class of mitochondrial protein deacetylase that influences various mitochondrial functions and is involved in the mechanisms underlying resistance to aging; regulation of bone marrow mesenchymal stem cells, osteoblasts, and osteoclasts; and development of osteoporosis, osteoarthritis, and other bone diseases. Moreover, exercise affects bones through SIRT3. Thus, studies on SIRT3 may provide insights for the treatment of bone diseases. Although SIRT3 can exert multiple effects on bone, the specific mechanism by which it regulates bone homeostasis remains unclear. By evaluating the relevant literature, this review discusses the structure and function of SIRT3, reveals the role and associated mechanisms of SIRT3 in regulating bone homeostasis and mediating bone health during exercise, and highlights the potential pharmacological value of SIRT3 in treating bone diseases

Ladder-like energy-relaying exciplex enables 100% internal quantum efficiency of white TADF-based diodes in a single emissive layer.

Development of white organic light-emitting diodes based on purely thermally activated delayed fluorescence with a single-emissive-layer configuration has been a formidable challenge. Here, we report the rational design of a donor-acceptor energy-relaying exciplex and its utility in fabricating single-emissive-layer, thermally activated delayed fluorescence-based white organic light-emitting diodes that exhibit 100% internal quantum efficiency, 108.2 lm W-1 power efficiency, and 32.7% external quantum efficiency. This strategy enables thin-film fabrication of an 8 cm × 8 cm thermally activated delayed fluorescence white organic light-emitting diodes (10 inch2) prototype with 82.7 lm W-1 power efficiency and 25.0% external quantum efficiency. Introduction of a phosphine oxide-based acceptor with a steric group to the exciplex limits donor-acceptor triplet coupling, providing dual levels of high-lying and low-lying triplet energy. Transient spectroscopic characterizations confirm that a ladder-like energy relaying occurs from the high-lying triplet level of the exciplex to a blue emitter, then to the low-lying triplet level of the phosphine oxide acceptor, and ultimately to the yellow emitter. Our results demonstrate the broad applicability of energy relaying in multicomponent systems for exciton harvesting, providing opportunities for the development of third-generation white organic light-emitting diode light sources