Generating collision-free motion in dynamic, partially observable environments is a fundamental challenge for robotic manipulators. Classical motion planners can compute globally optimal trajectories but require full environment knowledge and are typically too slow for dynamic scenes. Neural motion policies offer a promising alternative by operating in closed-loop directly on raw sensory inputs but often struggle to generalize in complex or dynamic settings. We propose Deep Reactive Policy (DRP), a visuo-motor neural motion policy designed for reactive motion generation in diverse dynamic environments, operating directly on point cloud sensory input. At its core is IMPACT, a transformer-based neural motion policy pretrained on 10 million generated expert trajectories across diverse simulation scenarios. We further improve IMPACT's static obstacle avoidance through iterative student-teacher finetuning. We additionally enhance the policy's dynamic obstacle avoidance at inference time using DCP-RMP, a locally reactive goal-proposal module. We evaluate DRP on challenging tasks featuring cluttered scenes, dynamic moving obstacles, and goal obstructions. DRP achieves strong generalization, outperforming prior classical and neural methods in success rate across both simulated and real-world settings. Video results and code available at https://deep-reactive-policy.com

Recent years have seen growing interest in Question Difficulty Estimation (QDE) using natural language processing techniques. Question difficulty is often represented using discrete levels, framing the task as ordinal regression due to the inherent ordering from easiest to hardest. However, the literature has neglected the ordinal nature of the task, relying on classification or discretized regression models, with specialized ordinal regression methods remaining unexplored. Furthermore, evaluation metrics are tightly coupled to the modeling paradigm, hindering cross-study comparability. While some metrics fail to account for the ordinal structure of difficulty levels, none adequately address class imbalance, resulting in biased performance assessments. This study addresses these limitations by benchmarking three types of model outputs -- discretized regression, classification, and ordinal regression -- using the balanced Discrete Ranked Probability Score (DRPS), a novel metric that jointly captures ordinality and class imbalance. In addition to using popular ordinal regression methods, we propose OrderedLogitNN, extending the ordered logit model from econometrics to neural networks. We fine-tune BERT on the RACE++ and ARC datasets and find that OrderedLogitNN performs considerably better on complex tasks. The balanced DRPS offers a robust and fair evaluation metric for discrete-level QDE, providing a principled foundation for future research.

Abstract--In the commercial sphere, such as operations and maintenance, advertising, and marketing recommendations, intelligent decision-making utilizing data mining and neural network technologies is crucial, especially in resource allocation to optimize ROI. This study delves into the Cost-aware Binary Treatment Assignment Problem (C-BTAP) across different industries, with a focus on the state-of-the-art Direct ROI Prediction (DRP) method. A larger area under the curve indicates better performance. Three popular methods have been proposed for tackling C-BTAP: 1) Two-Phase I. First, TPM utilized uplift models, such as In a wide range of commercial activities, intelligent decisionmaking meta-learners [11], [12], causal forests [6], [13]-[15], or neural based on data mining and neural network technologies network based representation learning [16]-[18] approaches, is playing an increasingly important role. One crucial aspect of to predict the revenue lift and cost lift, respectively. Then, this intelligent decision-making is figuring out how to allocate a calculation is performed by dividing the revenue uplift limited resources in order to maximize returns, essentially prediction by the cost uplift prediction. For instance, of revenue uplift model and cost uplift model may cause an in the field of operations and maintenance, how to allocate enlargement of model errors due to the mathematical operations machine resources and computational power to maximize the during combination; 2) For the method of Direct Rank (DR), revenue of supported businesses [1]; in the advertising sector, a loss function aimed at ranking individuals' ROI is created, how to distribute an advertiser's total budget reasonably to as noted in [9]. However, [5] demonstrate that achieving maximize the revenue from their products [2]; and in the accurate ranking is not possible when the loss function fully realms of recommendation and marketing, how to allocate converges because the loss function is not convex, which is suitable coupons, discounts, and coins as incentives to users in also detailed in Appendix E of [5]; 3) based on our research order to maximize platform user retention, GMV, etc [3]-[8]. of the published literature, the Direct ROI Prediction (DRP) In causal inference, actions such as adjusting the computational method [5], presented at AAAI 2023, remains the state-ofthe-art power for a specific business operation, modulating (SOTA) for C-BTAP so far. DRP designs a convex the cost of a particular advertisement, and offering incentives loss function for neural networks to guarantee an unbiased of varying value, as mentioned in the above examples, are estimation of ROI of individuals when the loss converges.

Residential consumers can use the demand response program (DRP) if they can utilize the home energy management system (HEMS), which reduces consumer costs by automatically adjusting air conditioning (AC) setpoints and shifting some appliances to off-peak hours. If HEMS knows occupancy status, consumers can gain more economic benefits and thermal comfort. However, for the building occupancy status, direct sensing is costly, inaccurate, and intrusive for residents. So, forecasting algorithms could serve as an effective alternative. The goal of this study is to present a non-intrusive, accurate, and cost-effective approach, to develop a multi-objective simulation model for the application of DRPs in a smart residential house, where (a) electrical load demand reduction, (b) adjustment in thermal comfort (AC) temperature setpoints, and (c) , worst cases scenario approach is very conservative. Because that is unlikely all uncertain parameters take their worst values at all times. So, the flexible robust counterpart optimization along with uncertainty budgets is developed to consider uncertainty realistically. Simulated results indicate that considering uncertainty increases the costs by 36 percent and decreases the AC temperature setpoints. Besides, using DRPs reduces demand by shifting some appliance operations to off-peak hours and lowers costs by 13.2 percent.

Digital transformation is the future of your organization -- but reading about digital transformation strategies can feel like looking at a bowl of alphabet soup. Studies show that only about 7 percent of corporate leadership is digitally savvy, which means you might feel a bit out of your ken as your organization begins adopting new strategies and processes in the name of digital transformation. Remaining relevant in the modern business environment will require plenty of engagement with digital education. In the meantime, you can use the following glossary of acronyms to help you decipher the memos you receive about your business's ongoing digital transformation. Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans.

Great successes of deep neural networks have been witnessed in various real applications. Many algorithmic and implementation techniques have been developed, however, theoretical understanding of many aspects of deep neural networks is far from clear. A particular interesting issue is the usefulness of dropout, which was motivated from the intuition of preventing complex co-adaptation of feature detectors. In this paper, we study the Rademacher complexity of different types of dropout, and our theoretical results disclose that for shallow neural networks (with one or none hidden layer) dropout is able to reduce the Rademacher complexity in polynomial, whereas for deep neural networks it can amazingly lead to an exponential reduction of the Rademacher complexity.