Online Joint Assortment-Inventory Optimization under MNL Choices

We study an online joint assortment-inventory optimization problem, in which we assume that the choice behavior of each customer follows the Multinomial Logit (MNL) choice model, and the attraction parameters are unknown a priori. The retailer makes periodic assortment and inventory decisions to dynamically learn from the realized demands about the attraction parameters while maximizing the expected total profit over time. In this paper, we propose a novel algorithm that can effectively balance the exploration and exploitation in the online decision-making of assortment and inventory. Our algorithm builds on a new estimator for the MNL attraction parameters, a novel approach to incentivize exploration by adaptively tuning certain known and unknown parameters, and an optimization oracle to static single-cycle assortment-inventory planning problems with given parameters. We establish a regret upper bound for our algorithm and a lower bound for the online joint assortment-inventory optimization problem, suggesting that our algorithm achieves nearly optimal regret rate, provided that the static optimization oracle is exact. Then we incorporate more practical approximate static optimization oracles into our algorithm, and bound from above the impact of static optimization errors on the regret of our algorithm. At last, we perform numerical studies to demonstrate the effectiveness of our proposed algorithm

FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition

This paper concentrates on the understanding of interlocutors' emotions evoked in conversational utterances. Previous studies in this literature mainly focus on more accurate emotional predictions, while ignoring model robustness when the local context is corrupted by adversarial attacks. To maintain robustness while ensuring accuracy, we propose an emotion recognizer augmented by a full-attention topic regularizer, which enables an emotion-related global view when modeling the local context in a conversation. A joint topic modeling strategy is introduced to implement regularization from both representation and loss perspectives. To avoid over-regularization, we drop the constraints on prior distributions that exist in traditional topic modeling and perform probabilistic approximations based entirely on attention alignment. Experiments show that our models obtain more favorable results than state-of-the-art models, and gain convincing robustness under three types of adversarial attacks

On the role of surrogates in the efficient estimation of treatment effects with limited outcome data

We study the problem of estimating treatment effects when the outcome of primary interest (e.g., long-term health status) is only seldom observed but abundant surrogate observations (e.g., short-term health outcomes) are available. To investigate the role of surrogates in this setting, we derive the semiparametric efficiency lower bounds of average treatment effect (ATE) both with and without presence of surrogates, as well as several intermediary settings. These bounds characterize the best-possible precision of ATE estimation in each case, and their difference quantifies the efficiency gains from optimally leveraging the surrogates in terms of key problem characteristics when only limited outcome data are available. We show these results apply in two important regimes: when the number of surrogate observations is comparable to primary-outcome observations and when the former dominates the latter. Importantly, we take a missing-data approach that circumvents strong surrogate conditions which are commonly assumed in previous literature but almost always fail in practice. To show how to leverage the efficiency gains of surrogate observations, we propose ATE estimators and inferential methods based on flexible machine learning methods to estimate nuisance parameters that appear in the influence functions. We show our estimators enjoy efficiency and robustness guarantees under weak conditions

Eriodictyol attenuates spinal cord injury by activating Nrf2/HO-1 pathway and inhibiting NF-κB pathway

Purpose: To investigate the effect of eriodictyol on spinal cord injury (SCI) and its underlying mechanism of action.Methods: Thirty Sprague-Dawley rats were assigned to sham, SCI, and eriodictyol-treated groups (SCI + Eri; 10, 20, and 50 mg/kg). Moderate spinal cord contusion injury was induced to model SCI. Locomotor recovery was assessed based on Basso, Beattie, and Bresnahan (BBB) score. Pain wasevaluated by paw withdrawal threshold (PWT) and latency (PWL), and spinal cord water content was measured. Tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), and interleukin-6 (IL-6) expression were determined by enzyme-linked immunosorbent assay (ELISA) and reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). Immunoassay was used to determine malondialdehyde (MDA), superoxide dismutase (SOD), glutathione (GSH), and glutathione peroxidase (GSH-PX) levels while Western blotting was employed to evaluate nuclear factor erythroid 2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1), nuclear factor-kappa B (NF-κB), and phosphorylated NF-κB (p-NF-κB) levels.Results: Eriodictyol elevated BBB score, PWT, and PWL in SCI rats but reduced spinal cord water content (p < 0.05). Eriodictyol treatment down-regulated TNF-α, IL-1β, IL-6, and MDA, whereas SOD, GSH, and GSH-PX levels were elevated (p < 0.05). Eriodictyol administration increased Nrf2 and HO-1 levels but reduced p-NF-κB/NF-κB.Conclusion: This study provides a potential therapy to promote long-term functional recovery following SCI. Keywords: Spinal cord injury, Eriodictyol, Nrf2/HO-1 pathway, NF-κB signaling pathway, Polymerase chain reaction, Basso, Beattie and Bresnahan scor