Product recalls: The effects of industry, recall strategy and hazard, on shareholder wealth

The purpose of this paper is to provide insights into the effects of product recalls on shareholder wealth of manufacturing firms in different supply chains. Previous research examining this phenomenon is largely uni-sectorial and/or does not consider the interplay of hazard, recall strategy and sector. By utilizing the event study method, this study examines investors\u27 reactions to key product recall characteristics: industry, recall strategy and hazard level, on a cross-industry sample of 296 product recall announcements. The results show a significant negative reaction of share values to product recalls and significant differences between industry type and hazard levels. More regulated and stringent supply chains, such as the automotive and pharmaceutical, showed statistically significant losses in share price. The results show that industry sector and level of hazard associated with defective products are significant factors impacting the shareholder wealth of manufacturing firms. Contrary to some studies, the impact of recall strategy was not confirmed, although proactive recall strategies led, in some cases, to an increase in share price. Further research would benefit from more detailed investigation of recall strategies on the value of companies in specific sectors, particularly ones which are susceptible to frequent and costly product recalls

Data Valuation and Detections in Federated Learning

Federated Learning (FL) enables collaborative model training while preserving the privacy of raw data. A challenge in this framework is the fair and efficient valuation of data, which is crucial for incentivizing clients to contribute high-quality data in the FL task. In scenarios involving numerous data clients within FL, it is often the case that only a subset of clients and datasets are pertinent to a specific learning task, while others might have either a negative or negligible impact on the model training process. This paper introduces a novel privacy-preserving method for evaluating client contributions and selecting relevant datasets without a pre-specified training algorithm in an FL task. Our proposed approach FedBary, utilizes Wasserstein distance within the federated context, offering a new solution for data valuation in the FL framework. This method ensures transparent data valuation and efficient computation of the Wasserstein barycenter and reduces the dependence on validation datasets. Through extensive empirical experiments and theoretical analyses, we demonstrate the potential of this data valuation method as a promising avenue for FL research.Comment: Fixed some experimental errors and typo

Graphene-oxide modified polyvinyl-alcohol as microbial carrier to improve high salt wastewater treatment

This work discussed the preparation and characterization of graphene oxide (GO) modified polyvinyl alcohol (PVA) for bacteria immobilization to enhance the biodegrdation efficiency of saline organic wastewater. GO-PVA material has lamellar structure with higher surface area to support bacterial growth and high salinity tolerance. It significantly stimulated the bacterial population by 1.4 times from 2.07×103 CFU/mL to 5.04×103 CFU/mL, and the microbial structure was also improved for salinity tolerance. Acinetobacter, Pseudomonas and Thermophilic hydrogen bacilli were enriched inside GO-PVA materials for glucose biodegradation. Compared to the CODCr removal efficiency with only PVA as the carrier (52.8%), GO-PVA material had better degradation performance (62.8%). It is proved as a good candidate for bioaugmentation to improve biodegradation efficiency in hypersaline organic wastewater

KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding

With the advent of pre-trained language models (LMs), increasing research efforts have been focusing on infusing commonsense and domain-specific knowledge to prepare LMs for downstream tasks. These works attempt to leverage knowledge graphs, the de facto standard of symbolic knowledge representation, along with pre-trained LMs. While existing approaches leverage external knowledge, it remains an open question how to jointly incorporate knowledge graphs representing varying contexts, from local (e.g., sentence), to document-level, to global knowledge, to enable knowledge-rich and interpretable exchange across these contexts. Such rich contextualization can be especially beneficial for long document understanding tasks since standard pre-trained LMs are typically bounded by the input sequence length. In light of these challenges, we propose KALM, a Knowledge-Aware Language Model that jointly leverages knowledge in local, document-level, and global contexts for long document understanding. KALM first encodes long documents and knowledge graphs into the three knowledge-aware context representations. It then processes each context with context-specific layers, followed by a context fusion layer that facilitates interpretable knowledge exchange to derive an overarching document representation. Extensive experiments demonstrate that KALM achieves state-of-the-art performance on three long document understanding tasks across 6 datasets/settings. Further analyses reveal that the three knowledge-aware contexts are complementary and they all contribute to model performance, while the importance and information exchange patterns of different contexts vary with respect to different tasks and datasets

InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactions

We have recently seen tremendous progress in diffusion advances for generating realistic human motions. Yet, they largely disregard the multi-human interactions. In this paper, we present InterGen, an effective diffusion-based approach that incorporates human-to-human interactions into the motion diffusion process, which enables layman users to customize high-quality two-person interaction motions, with only text guidance. We first contribute a multimodal dataset, named InterHuman. It consists of about 107M frames for diverse two-person interactions, with accurate skeletal motions and 23,337 natural language descriptions. For the algorithm side, we carefully tailor the motion diffusion model to our two-person interaction setting. To handle the symmetry of human identities during interactions, we propose two cooperative transformer-based denoisers that explicitly share weights, with a mutual attention mechanism to further connect the two denoising processes. Then, we propose a novel representation for motion input in our interaction diffusion model, which explicitly formulates the global relations between the two performers in the world frame. We further introduce two novel regularization terms to encode spatial relations, equipped with a corresponding damping scheme during the training of our interaction diffusion model. Extensive experiments validate the effectiveness and generalizability of InterGen. Notably, it can generate more diverse and compelling two-person motions than previous methods and enables various downstream applications for human interactions.Comment: accepted by IJCV 202

Pharmacological Basis for Use of Armillaria mellea

Armillaria mellea, an edible fungus, exhibits various pharmacological activities, including antioxidant and antiapoptotic properties. However, the effects of A. mellea on Alzheimer’s disease (AD) have not been systemically reported. The present study aimed to explore the protective effects of mycelium polysaccharides (AMPS) obtained from A. mellea, especially AMPSc via 70% ethanol precipitation in a L-glutamic acid- (L-Glu-) induced HT22 cell apoptosis model and an AlCl3 plus D-galactose- (D-gal-) induced AD mouse model. AMPSc significantly enhanced cell viability, suppressed nuclear apoptosis, inhibited intracellular reactive oxygen species accumulation, prevented caspase-3 activation, and restored mitochondrial membrane potential (MMP). In AD mice, AMPSc enhanced horizontal movements in an autonomic activity test, improved endurance times in a rotarod test, and decreased escape latency time in a water maze test. Furthermore, AMPSc reduced the apoptosis rate, amyloid beta (Aβ) deposition, oxidative damage, and p-Tau aggregations in the AD mouse hippocampus. The central cholinergic system functions in AD mice improved after a 4-week course of AMPSc administration, as indicated by enhanced acetylcholine (Ach) and choline acetyltransferase (ChAT) concentrations, and reduced acetylcholine esterase (AchE) levels in serum and hypothalamus. Our findings provide experimental evidence suggesting A. mellea as a neuroprotective candidate for treating or preventing neurodegenerative diseases