Optimal Retailing mode for Content Platforms\u27 E-commerce

In recent years, content platforms have expanded their operations into e-commerce by leveraging short videos or live-streaming and collecting commissions. This paper examines the optimal operational modes that content platforms should adopt when launching e-commerce businesses. Specifically, we analyze the content platforms’ choices between building a joint channel with e-commerce platforms (joint mode), building a self-operated channel independently (self-operated mode), or operating both channels simultaneously (dual-channel mode). Using a game-theoretic model, we find that the content platform tends to choose the self-operated mode (or joint mode) when all sellers have high (or low) conversion ability, and the dual-channel mode when sellers have differentiated conversion abilities. Additionally, our analysis shows that the content platform\u27s mode selection switches from joint mode to dual-channel mode and to self-operated mode with a decrease in e-commerce platform\u27s commission rate or a weaker spillover effect from the content platform to the e-commerce platform

Unveiling Single-Bit-Flip Attacks on DNN Executables

Recent research has shown that bit-flip attacks (BFAs) can manipulate deep neural networks (DNNs) via DRAM Rowhammer exploitations. Existing attacks are primarily launched over high-level DNN frameworks like PyTorch and flip bits in model weight files. Nevertheless, DNNs are frequently compiled into low-level executables by deep learning (DL) compilers to fully leverage low-level hardware primitives. The compiled code is usually high-speed and manifests dramatically distinct execution paradigms from high-level DNN frameworks. In this paper, we launch the first systematic study on the attack surface of BFA specifically for DNN executables compiled by DL compilers. We design an automated search tool to identify vulnerable bits in DNN executables and identify practical attack vectors that exploit the model structure in DNN executables with BFAs (whereas prior works make likely strong assumptions to attack model weights). DNN executables appear more "opaque" than models in high-level DNN frameworks. Nevertheless, we find that DNN executables contain extensive, severe (e.g., single-bit flip), and transferrable attack surfaces that are not present in high-level DNN models and can be exploited to deplete full model intelligence and control output labels. Our finding calls for incorporating security mechanisms in future DNN compilation toolchains.Comment: Fix typ

Large Language Models are Zero Shot Hypothesis Proposers

Significant scientific discoveries have driven the progress of human civilisation. The explosion of scientific literature and data has created information barriers across disciplines that have slowed the pace of scientific discovery. Large Language Models (LLMs) hold a wealth of global and interdisciplinary knowledge that promises to break down these information barriers and foster a new wave of scientific discovery. However, the potential of LLMs for scientific discovery has not been formally explored. In this paper, we start from investigating whether LLMs can propose scientific hypotheses. To this end, we construct a dataset consist of background knowledge and hypothesis pairs from biomedical literature. The dataset is divided into training, seen, and unseen test sets based on the publication date to control visibility. We subsequently evaluate the hypothesis generation capabilities of various top-tier instructed models in zero-shot, few-shot, and fine-tuning settings, including both closed and open-source LLMs. Additionally, we introduce an LLM-based multi-agent cooperative framework with different role designs and external tools to enhance the capabilities related to generating hypotheses. We also design four metrics through a comprehensive review to evaluate the generated hypotheses for both ChatGPT-based and human evaluations. Through experiments and analyses, we arrive at the following findings: 1) LLMs surprisingly generate untrained yet validated hypotheses from testing literature. 2) Increasing uncertainty facilitates candidate generation, potentially enhancing zero-shot hypothesis generation capabilities. These findings strongly support the potential of LLMs as catalysts for new scientific discoveries and guide further exploration.Comment: Instruction Workshop @ NeurIPS 202

Hard Sample Aware Network for Contrastive Deep Graph Clustering

Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample mining-based algorithms have achieved great attention for their promising performance. However, we find that the existing hard sample mining methods have two problems as follows. 1) In the hardness measurement, the important structural information is overlooked for similarity calculation, degrading the representativeness of the selected hard negative samples. 2) Previous works merely focus on the hard negative sample pairs while neglecting the hard positive sample pairs. Nevertheless, samples within the same cluster but with low similarity should also be carefully learned. To solve the problems, we propose a novel contrastive deep graph clustering method dubbed Hard Sample Aware Network (HSAN) by introducing a comprehensive similarity measure criterion and a general dynamic sample weighing strategy. Concretely, in our algorithm, the similarities between samples are calculated by considering both the attribute embeddings and the structure embeddings, better revealing sample relationships and assisting hardness measurement. Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones. In this way, our method can mine not only the hard negative samples but also the hard positive sample, thus improving the discriminative capability of the samples further. Extensive experiments and analyses demonstrate the superiority and effectiveness of our proposed method.Comment: 9 pages, 6 figure

Microbial functional trait of rRNA operon copy numbers increases with organic levels in anaerobic digesters.

The ecological concept of the r-K life history strategy is widely applied in macro-ecology to characterize functional traits of taxa. However, its adoption in microbial communities is limited, owing to the lack of a measureable, convenient functional trait for classification. In this study, we performed an experiment of stepwise organic amendments in triplicate anaerobic digesters. We found that high resource availability significantly favored microbial r-strategists such as Bacillus spp. Incremental resource availability heightened average rRNA operon copy number of microbial community, resulting in a strong, positive correlation (r>0.74, P<0.008). This study quantifies how resource availability manipulations influence microbial community composition and supports the idea that rRNA operon copy number is an ecologically meaningful trait which reflects resource availability

The combined therapeutic effects of \u3csup\u3e131\u3c/sup\u3eiodine-labeled multifunctional copper sulfide-loaded microspheres in treating breast cancer

Compared to conventional cancer treatment, combination therapy based on well-designed nanoscale platforms may offer an opportunity to eliminate tumors and reduce recurrence and metastasis. In this study, we prepared multifunctional microspheres loading 131I-labeled hollow copper sulfide nanoparticles and paclitaxel (131I-HCuSNPs-MS-PTX) for imaging and therapeutics of W256/B breast tumors in rats. 18F-fluordeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging detected that the expansion of the tumor volume was delayed (P\u3c0.05) following intra-tumoral (i.t.) injection with 131I-HCuSNPs-MS-PTX plus near-infrared (NIR) irradiation. The immunohistochemical analysis further confirmed the anti-tumor effect. The single photon emission computed tomography (SPECT)/photoacoustic imaging mediated by 131I-HCuSNPs-MS-PTX demonstrated that microspheres were mainly distributed in the tumors with a relatively low distribution in other organs. Our results revealed that 131I-HCuSNPs-MS-PTX offered combined photothermal, chemo- and radio-therapies, eliminating tumors at a relatively low dose, as well as allowing SPECT/CT and photoacoustic imaging monitoring of distribution of the injected agents non-invasively. The copper sulfide-loaded microspheres, 131I-HCuSNPs-MS-PTX, can serve as a versatile theranostic agent in an orthotopic breast cancer model

Impact Mechanism and Improvement Strategy on Urban Ventilation, Urban Heat Island and Urban Pollution Island: A Case Study in Xiangyang, China

There has been a growing interest in finding mitigation measures for urban heat islands and urban pollution islands that focus mainly on urban landscape mechanisms. However, relatively little research has considered spatial non-stationarity and temporal non-stationarity, which are both intrinsic properties of the environmental system, simultaneously. At the same time, the relevance of and differences between the thermal environment and air pollution has also been rarely discussed, and both issues are of great importance to urban planning. In this study, which is aimed at improving urban ventilation to reduce the urban heat island and urban pollution island effects, an urban ventilation potential evaluation, land surface temperature time-series clustering and air pollution source identification are comprehensively applied to identify the operational areas, compensation areas and ventilation corridors in Xiangyang, China, thus bridging the gap between academic research and urban planning. The specific research areas include: (1) defining the operational areas for urban ventilation corridor planning through an urban ventilation potential evaluation featuring urban morphology indicators, land surface temperature time-series clustering with k-means and an urban air pollution source diffusion analysis via the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and geographically weighted regression (GWR) methods; (2) identifying urban cold islands through land surface temperatures and delimiting the compensation areas in urban ventilation corridor planning; (3) designating urban ventilation corridors through an urban ventilation potential evaluation and computational fluid dynamics (CFD); and (4) improving urban ventilation corridor planning through defining operational areas, compensation areas and ventilation corridors as well as proposing corresponding control measures

Landau level splitting in Cd3As2 under high magnetic fields

Three-dimensional topological Dirac semimetals (TDSs) are a new kind of Dirac materials that exhibit linear energy dispersion in the bulk and can be viewed as three-dimensional graphene. It has been proposed that TDSs can be driven to other exotic phases like Weyl semimetals, topological insulators and topological superconductors by breaking certain symmetries. Here we report the first transport experiment on Landau level splitting in TDS Cd3As2 single crystals under high magnetic fields, suggesting the removal of spin degeneracy by breaking time reversal symmetry. The detected Berry phase develops an evident angular dependence and possesses a crossover from nontrivial to trivial state under high magnetic fields, a strong hint for a fierce competition between the orbit-coupled field strength and the field-generated mass term. Our results unveil the important role of symmetry breaking in TDSs and further demonstrate a feasible path to generate a Weyl semimetal phase by breaking time reversal symmetry.Comment: 31 page

How Do the Multi-Temporal Centroid Trajectories of Urban Heat Island Correspond to Impervious Surface Changes: A Case Study in Wuhan, China

Conspicuous expansion and intensification of impervious surfaces accompanied by rapid urbanization are widely recognized to have exerted evident impacts on the urban thermal environment. Investigating the spatially and temporally varying relationships between Land Surface Temperature (LST) and impervious surfaces (IS) at multiple scales is of great significance for steering IS expansion and intensification. This study proposes an analytical framework to investigate the spatiotemporal variations of LST and its responses to IS in Wuhan, China at both city scale and sub-region scale. The summer LST patterns in 2002–2017 are extracted by Multi-Task Gaussian Process (MTGP) model from raw 8-day synthesized MODerate-resolution Imaging Spectroradiometer (MODIS) LST data. At the city scale, the weighted center of LST (LSTWC) and impervious surface fraction (ISFWC), multi-temporal trajectories and coupling indicators are utilized to comprehensively examine the spatial and temporal dynamics of LST and IS within Wuhan. At the sub-region scale, urban heat island ratio index (URI), impervious surfaces contribution index (ISCI) and sprawl rate are introduced for further quantifying the relationships of LST and IS. The results reveal that IS and hot thermal landscapes expanded by 407.43 km2 and 255.82 km2 in Wuhan in 2002–2017 at city scale. The trajectories of LSTWCs and ISFWCs are visually coherent and both heading to southeast direction in general. At the sub-region scale, the specific cardinal directions with the highest ISCI variations are examined to be the exact directions of ISFWC trajectories in 2002–2017. The results reveal that the spatiotemporal variations of LST and IS are highly correlated at both city and sub-region scales within Wuhan, thus testifying the significance of steering IS expansion and renewal for controlling urban thermal environment deterioration