BIN-CT optimizes waste collection routes to reduce costs and emissions.
problem Optimal waste collection in urban areas to manage increasing waste volumes.
method Predictive algorithms based on historical and future data for route planning.
result Reduction in distance traveled and operational costs by 70%.
Study estimates risks of nuclear waste storage projects.
problem Cost and schedule risks in nuclear waste storage projects.
method Reference class of 216 past projects for cost risk, 200 for schedule risk.
result Cost and schedule risks are substantial for nuclear waste storage projects.
Deep learning model reduces food waste by stabilizing online food delivery supply chains.
problem Wastage and bullwhip effect in online food delivery services.
method Two-phase LSTM network for demand forecasting, newsvendor model for inventory management.
result Significant reduction in bullwhip effect and food waste, improved forecasting accuracy.
LMM predicts healthcare costs and risks with improved accuracy.
problem Wasteful healthcare spending and inefficiencies in risk prediction.
method Generative pre-trained transformer trained on patient event sequences.
result Improves cost prediction by 14.1% and chronic conditions prediction by 1.9%.
Novel Bayesian optimization framework improves portfolio management stability and efficiency.
problem Stable and sample-efficient optimization for black-box portfolio models under limited observation budgets.
method TPE-AS framework with adaptive scheduling and importance sampling.
result Demonstrated effectiveness across four backtest settings with three distinct models.
New approach improves stock policies for paper companies, reducing waste and costs.
problem Improving stock policies for integrated paper companies.
method Developed a new approach to determine near-optimal stock policies.
result Reduction in total waste by 9% and logistics costs.
Implementing large-scale information and communication technology (IT) projects carries large risks and easily might disrupt operations, waste taxpayers' money, and create negative publicity. Because of the high risks it is important that government leaders manage the attendant risks. We analysed a sample of 1,355 publ…
LeanML reduces machine learning project waste by estimating best performance without training models.
problem Avoidable wastes in machine learning projects.
method Lean design pattern based on mutual information and performance metrics.
result Estimating best performance without training models is faster and cheaper.
Study shows ethanol blends and incentives can significantly reduce transportation carbon emissions.
problem Rapid growth in electric vehicles requires complementary strategies to decarbonize transportation.
method Analysis of ethanol blending, regulatory incentives, and economic assessments.
result Ethanol blending, especially E15 and E85, can substantially reduce carbon emissions and provide economic benefits.
The green area of economy is the key of healthy living. It is necessary to convene economic and ecologic framework to establish a market attentive to drastic reduction of emissions damaging our climate and landscapes in rural areas, to the protection of biological diversity of the planet, to stop producing nuclear wast…
To train good supervised and semi-supervised object classifiers, it is critical that we not waste the time of the human experts who are providing the training labels. Existing active learning strategies can have uneven performance, being efficient on some datasets but wasteful on others, or inconsistent just between ru…
We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities, e.g., for applications involving bandits or active learning. One simple approach to this is to use onlin…
New principle reduces load imbalance in LLM serving systems, saving up to 52% energy.
problem Wasted computational power due to load imbalance in LLM serving systems.
method Developed a universal load-balancing principle for barrier-synchronized systems with non-migratable state.
result Proves worst-case theoretical guarantees for imbalance reduction and energy savings.
Group discussions are essential for organizing every aspect of modern life, from faculty meetings to senate debates, from grant review panels to papal conclaves. While costly in terms of time and organization effort, group discussions are commonly seen as a way of reaching better decisions compared to solutions that do…
This paper evaluates various bus arrival time prediction models.
problem Improving prediction accuracy of bus arrival times.
method General evaluation framework for various models, including raw data pre-processing.
result Preliminary results show strengths and weaknesses of common models.
Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the ot…
SDN mitigates overthinking in deep networks, reducing inference cost and preserving accuracy.
problem Overthinking in deep neural networks, leading to unnecessary computations and misclassifications.
method Introducing internal classifiers (Shallow-Deep Network) to SDNs, enabling early confidence-based exits.
result SDNs reduce inference cost by more than 50% and preserve accuracy, mitigating overthinking.
Contrary to the situation with stochastic gradient descent, we argue that when using stochastic methods with variance reduction, such as SDCA, SAG or SVRG, as well as their variants, it could be beneficial to reuse previously used samples instead of fresh samples, even when fresh samples are available. We demonstrate t…
sGPO optimizes RLVR training by balancing inference FLOPs and training efficiency.
problem RLVR training allocates rollout budget without considering query difficulty.
method sGPO trades inference FLOPs for reduced training FLOPs.
result sGPO matches or exceeds baseline performance while reducing training compute by a factor of three.
We present a method for performing Hamiltonian Monte Carlo that largely eliminates sample rejection for typical hyperparameters. In situations that would normally lead to rejection, instead a longer trajectory is computed until a new state is reached that can be accepted. This is achieved using Markov chain transitions…
New method reduces unnecessary data communication in distributed optimization.
problem Inefficient data communication in distributed optimization methods.
method Independent sparsification of local gradient estimates before communication.
result Communication of 99% of data is redundant and unnecessary.
We give a complete algorithm and source code for constructing general multifactor risk models (for equities) via any combination of style factors, principal components (betas) and/or industry factors. For short horizons we employ the Russian-doll risk model construction to obtain a nonsingular factor covariance matrix.…
Improves BO efficiency by allowing asynchronous parallel computing.
problem Wasteful use of resources in batch Bayesian optimisation.
method Developed PLAyBOOK for asynchronous local penalisation.
result Asynchronous BO often outperforms synchronous BO.
This paper explores portfolio management strategies to maximize alpha and minimize beta.
problem Maximizing returns while minimizing risk in investment portfolios.
method Examines asset allocation, diversification, active management, and risk management strategies.
result Combining these strategies optimizes portfolio performance.
We present and analyze several strategies for improving the performance of stochastic variance-reduced gradient (SVRG) methods. We first show that the convergence rate of these methods can be preserved under a decreasing sequence of errors in the control variate, and use this to derive variants of SVRG that use growing…
The paper explores efficient ways to represent categorical data.
problem Wasteful one-hot encoding of categorical variables.
method Investigates alternative, lower-dimensional real-valued representations.
result Proposed methods retain all predictive information without one-hot encoding.
A new model detects objects more efficiently with less computation.
problem Handling waste of regional information and compromise between efficiency and accuracy in CNN object detection.
method Proposes a probabilistic model with a search framework to map images into probabilistic object distributions.
result The proposed model gives more informative outputs with less computation.
New approach for active learning in overparameterized models.
problem Efficiently labeling datasets in machine learning.
method MaxiMin Active Learning for nonparametric or overparameterized models.
result Automatically identifies decision boundaries and data clusters.
Paper introduces a framework for managing cyber risk with insurance and cybersecurity models.
problem Pervasive challenges in managing cyber risk, especially for capital allocation.
method Combines insurance frequency-severity models with cybersecurity cascade models for comprehensive cyber risk assessment. Facilitates informed capital allocation through a two-pillar framework.
result Demonstrates the necessity of comprehensive cost-benefit analysis for budget-constrained companies.
The basic financial purpose of a firm is to maximize its value. An inventory management system should also contribute to realization of this basic aim. Many current asset management models currently found in financial management literature were constructed with the assumption of book profit maximization as basic aim. H…
Averaging recent model checkpoints speeds up training time.
problem Training large vision or language models is time-consuming.
method Average the weights of the k latest checkpoints.
result Speeds up training by dozens of epochs, saving up to 68 GPU hours.
Study finds Indian mutual funds adjust cash holdings based on inflows, impacting stock purchases.
problem Active liquidity management by mutual funds in India.
method Examined cash holdings and stock purchases of Indian equity mutual funds.
result Funds with active liquidity choices outperform, highlighting the importance of this strategy.
Framework for managing cyber risks in networks.
problem Managing systemic cyber risks in digital networks.
method Three components: acceptable configurations, risk mitigation interventions, and cost function.
result Effective decision-making for network resilience.
Research identifies risks in selecting project managers for civil engineering projects.
problem Lack of awareness of project manager selection criteria and associated risks.
method Combined ANP-FMEA approach for risk analysis.
result ANP-FMEA model identifies more significant risks than traditional FMEA.
Deep learning improves portfolio management by optimizing asset weights.
problem Traditional portfolio managers are outperformed by deep learning models in trading.
method Proposes a deep reinforcement learning portfolio manager that allocates weights to assets.
result The proposed portfolio manager outperforms conventional managers in risk-adjusted returns.
This research develops a dynamic risk management system for industrial companies.
problem Risk assessment and management in industrial enterprises.
method Qualitative and quantitative analysis, systematic risk classification, dynamic system development.
result Effective risk management strategies formed through dynamic risk management system and risk assessment methods.
The paper defines successful active management and introduces a framework.
problem The elusive criteria for successful active management in the literature.
method Introducing definitions of key concepts and a logically coherent evaluation framework.
result A strong defense of active management emerges through the defined concepts.
Study finds managers' tenure and education influence their choice between in-court and out-of-court restructuring.
problem Exploring managers' characteristics and their impact on restructuring decisions.
method Empirical investigation using upper echelons theory and data from 342 managers of French firms.
result Managers with longer tenure and higher education levels prefer private restructuring over court involvement.
The paper fits cash management models to data using stochastic and linear programming.
problem Cash flow probability distribution assumptions in cash management models are relaxed.
method Stochastic and linear programming to fit models to data.
result A small random sample of data is sufficient to fit bound-based models.
This paper provides an overview of CCA-based multi-view learning approaches.
problem Fusing data from different sources or subsets.
method Canonical correlation analysis (CCA) for mapping data onto a common space with maximum correlation.
result Overview of many representative CCA-based multi-view learning approaches.
Decision tool helps manage biofouling risks for ships in the Baltic Sea.
problem Biofouling of ships causes environmental and economic issues.
method Bayesian networks to identify biofouling management strategies.
result Optimal biofouling management includes biocidal-free coating and in-water cleaning.
AI predicts employee attrition to prevent turnover.
problem Predicting and preventing employee attrition.
method Ensemble classification and Linear Regression models.
result Predicts employee attrition with over 91% accuracy.
Model cash management under ambiguity using maxmin preferences and diffusion.
problem Optimizing cash reserves in the presence of ambiguity.
method Singular control model with maxmin preferences, verified using Dynkin games.
result Higher expected costs and narrower inaction region under increased ambiguity.
This review classifies electricity price models for risk management.
problem Choosing suitable models for risk management in electricity markets.
method Classification of models based on their ability to represent price behavior.
result Helps users select appropriate models for risk management.
This research reviews reinforcement learning for optimizing building energy management.
problem Optimizing energy utilization in building management systems.
method Comprehensive review of reinforcement learning applications in building energy management.
result Challenges and future directions in reinforcement learning for building energy management.
Study improves machine learning for long-term financial portfolio management.
problem Machine learning precision declines with long-term data.
method Data augmentation using multiple time scales and learning data.
result Generalization performance can be maintained for long-term tasks.
Paper proposes real-time risk metrics for stablecoin protocols.
problem Lack of risk management frameworks for stablecoins.
method Developed two risk metrics: capitalization and liquidity.
result Demonstrated practical benefits of real-time on-chain data.
Enhances portfolio management with RL, considering transaction costs and short selling.
problem Lack of practical aspects in RL for portfolio management.
method Proposes a general RL framework for asset management with continuous weights, short selling, and relevant features. Compares PGAC, PPO, and ES algorithms in a simulated environment with transaction costs.
result Demonstrates advantages of RL algorithms in real-life asset management scenarios.