Dynamic prize linked savings aims to increase account sizes and manage risks.
problem Limited appeal and low account sizes in prize linked savings.
method Proposes a dynamic prize linked savings model and three risk management methods.
result Effective risk management methods were tested and shown to mitigate severe risks.
SAM improves deep learning tasks by promoting balancedness, reducing outlier impact.
problem Improving generalization in deep learning tasks, especially with scale-invariant problems.
method Introduces balancedness as a new concept to depict global behaviors of SAM, focusing on the difference between squared norms of two variables.
result SAM promotes balancedness and is data-responsive, outperforming SGD in outlier scenarios.
A scalable algorithm for MAX-VAR GCCA with improved performance and structure-promoting regularization.
problem Scalability issues and lack of structural constraints in MAX-VAR GCCA.
method Proposed an alternating optimization (AO) algorithm to handle non-convex MAX-VAR GCCA.
result The algorithm globally converges to a critical point at a sublinear rate and approaches a global optimal solution at a linear rate.
Post-training quantization saves resources for neural networks.
problem Implementing neural networks in resource-constrained hardware.
method Generalized post-training quantization method (GPFQ) with modifications for sparsity and error analysis.
result Error decays linearly with over-parametrization, showing minor loss of accuracy.
Mixed models study optimal saving under risk.
problem Optimal saving in risky consumer environments.
method Two mixed models combining possibilistic and probabilistic approaches.
result Optimal saving strategies identified for risk types.
Argentum is a crypto coin for saving and investment in unstable countries.
problem Stable purchasing power for savings in unstable economies.
method Designing a crypto coin backed by investment instruments.
result Provides a stabilization instrument for savings in unstable economies.
Adaptive BO improves solder joint reliability by 3% with half the computational cost.
problem Improving solder joint reliability under thermomechanical loading.
method Adaptive Bayesian optimization with Gaussian process regression.
result Adaptive BO outperforms regular BO by 3% on average at any given computational budget.
PromID predicts human promoter positions with high accuracy.
problem Difficulties in identifying human promoter sequences.
method Deep learning approach predicting exact TSS positions.
result Significantly reduces false positive predictions.
Study on pooled annuity funds and how initial savings affect income stability.
problem Analyzing the stability of income payments in pooled annuity funds.
method Examining the influence of initial savings on income fluctuations and developing a criterion for pooling funds.
result Identification of a term, the 'implied number of homogeneous members', linking initial savings to income fluctuations.
Estimates cost savings from early cancer diagnosis.
problem Improving early cancer diagnosis to reduce treatment costs.
method Combining published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis, and extrapolating to other cancer sites.
result Estimates U.S. national annual treatment cost-savings from early cancer diagnosis in the trillions.
Improved cash flow forecasts lead to significant cost savings.
problem The impact of forecast accuracy on cash management costs.
method Analysis of two real data sets from the textile industry.
result Predictive accuracy is highly correlated with cost savings.
The effects of saving and spending patterns on holding time distribution of money are investigated based on the ideal gas-like models. We show the steady-state distribution obeys an exponential law when the saving factor is set uniformly, and a power law when the saving factor is set diversely. The power distribution c…
Data science reveals co-evolution of income inequality and savings across countries.
problem Understanding the co-evolution of income inequality and savings across countries.
method Time series data for Gini indices and Gross Domestic Savings (% of GDP) were used to construct correlation and similarity matrices, and a multi-dimensional scaling technique was applied. Linear regression was used to test the empirical linkage between income inequality and savings.
result The empirical model proposed by Chakraborti-Chakrabarti (2000) holds reasonably true for many economies of the world, showing a moderate relationship between income inequality and savings.
iPromoter-BnCNN identifies and classifies six types of sigma promoters.
problem Accurate classification of sigma promoters remains challenging.
method Convolutional Neural Network (CNN) combining local features.
result Supremacy on 5-fold cross-validation and independent test datasets.
We analyze the ideal gas like models of markets and review the different cases where a `savings' factor changes the nature and shape of the distribution of wealth. These models can produce similar distribution of wealth as observed across varied economies. We present a more realistic model where the saving factor can v…
Smart home system saves energy based on consumer behavior.
problem Energy wastage in smart homes.
method Machine learning algorithm using consumer behavior data.
result The system successfully saves energy without compromising comfort.
India introduces NPS to manage pension liabilities and promote savings.
problem Managing pension liabilities and promoting savings among employees.
method Comparative analysis of NPS and OPS, addressing stakeholder claims.
result NPS reduces government pension liabilities and promotes disciplined saving.
We consider a simple model of a closed economic system where the total money is conserved and the number of economic agents is fixed. In analogy to statistical systems in equilibrium, money and the average money per economic agent are equivalent to energy and temperature, respectively. We investigate the effect of the …
We review a simple model of closed economy, where the economic agents make money transactions and a saving criterion is present. We observe the Gibbs distribution for zero saving propensity, and non-Gibbs distributions otherwise. While the exact solution in the case of zero saving propensity is already known to be give…
SAVED safely learns robot tasks with sparse rewards.
problem Challenges in reinforcement learning for robotics, especially sparse rewards and complex constraints.
method SAVED uses supervision to constrain exploration and learn efficiently, handling complex constraints.
result SAVED outperforms state-of-the-art methods in success rate, constraint satisfaction, and sample efficiency.
Gradient boosting predicts promotion efficiency using multiple performance indicators.
problem Forecasting promotion efficiency in FMCG retail.
method Gradient boosting applied to six performance indicators for different product groups.
result Models accurately forecast promotion efficiency, optimizing marketing strategies.
Study explores factors influencing saving behavior among Dhaka employees.
problem Factors influencing saving behavior among Dhaka employees.
method Quantitative approach with cross-sectional survey design, structured questionnaire, descriptive statistics, reliability analysis, regression analysis.
result Only financial management practices had a significant positive relationship with saving behavior.
We discuss the ideal gas like models of a trading market. The effect of savings on the distribution have been thoroughly reviewed. The market with fixed saving factors leads to a Gamma-like distribution. In a market with quenched random saving factors for its agents we show that the steady state income (m) distributi…
SAVE combines Q-learning and MCTS with amortized value estimates for improved performance.
problem Combining model-free Q-learning and model-based MCTS for efficient learning and planning.
method SAVE uses a learned prior to guide MCTS, which estimates improved state-action values. These estimates are used to update the prior, creating a cooperative relationship between learning and search.
result SAVE achieves higher rewards with fewer training steps and strong performance with small search budgets.
We mathematically analyze a simple market model where trading at each point in time involves only two agents with the sum of their money being conserved and with neither parties resulting with negative money after the interaction process. The exchange involves random re-distribution among the two players of a fixed fra…
Study on optimal portfolio selection with varying borrowing and saving rates in continuous-time markets.
problem Optimal portfolio selection in markets with different borrowing and saving rates.
method Hamilton-Jacobi-Bellman equation, partial differential equation, verification argument.
result Existence and smoothness of the value function, identification of trading regions and strategies.
New algorithms secure IoT edge computing from jamming attacks.
problem Secure mobile edge computing in IoT under jamming attacks.
method Online learning tools for developing SAVE-S and SAVE-A algorithms.
result Achieves sublinear regret without extra resources.
New Bitcoin coin selection method improves cost savings.
problem Improving cost savings in Bitcoin coin selection.
method Coin selection with leverage, allowing user-tunable parameters.
result Natural replacement for standard knapsack method.
We have studied numerically the statistical mechanics of the dynamic phenomena, including money circulation and economic mobility, in some transfer models. The models on which our investigations were performed are the basic model proposed by A. Dragulescu and V. Yakovenko [1], the model with uniform saving rate develop…
We address the problem of retrieving relevant experiments given a query experiment, motivated by the public databases of datasets in molecular biology and other experimental sciences, and the need of scientists to relate to earlier work on the level of actual measurement data. Since experiments are inherently noisy and…
Active learning helps save experiments in drug-target prediction by stopping when predictions are accurate enough.
problem Determining when to stop active learning to save experiments and costs.
method Computed active learning traces on simulated data to learn a regression model for accuracy. Designed stopping criteria based on model performance.
result Up to 40% savings in experiments for highly accurate predictions.
Dynamic promotion optimization for e-commerce platforms within financial constraints.
problem Balancing promotional costs with incremental revenue for sustainable growth.
method Knapsack Problem formulation for dynamic optimization, Retrospective Estimation, online-dynamic calibration.
result Significant increase in target outcome while staying within financial constraints.
RL agent learns to save costs by managing household energy storage.
problem Maximizing cost savings in smart grids with household energy storage.
method Data-driven RL agent learns from tariff structures and storage capacity.
result RL agent explains its learning process and strategies.
Model trains agents to optimize saving and investment strategies for diverse retirement needs.
problem Optimal saving and investment strategies for individuals in varied employment and income profiles.
method Deep reinforcement learning to train intelligent agents with heterogeneous profiles.
result Flexible methodology estimates lifetime consumption and investment choices for different profiles.
SAVeD detects dataset versions without metadata, improving accuracy and separation.
problem Difficulty in identifying similar versions of structured datasets.
method Contrastive learning with modified SimCLR pipeline, generating and contrasting augmented table views.
result SAVeD achieves higher accuracy and separation scores on unseen tables.
Locally convex sparse learning reduces communication and processing time over networks.
problem Efficiently estimate sparse signals over networked nodes with minimal communication.
method Convex optimization with ℓ1-norm minimization, incorporating neighbors' estimates. result Local estimates converge to exact sparse signal under certain conditions.
Some general features of kinetic multi-agent models are reviewed, with particular attention to the relation between the agent saving propensities and the form of the equilibrium wealth distribution. The effect of a finite cutoff of the saving propensity distribution on the corresponding wealth distribution is studied. …
RNN-MAS predicts YouTube video popularity by integrating multiple sources of external influence.
problem Predicting popularity in asynchronous social media streams with multiple external influences.
method Recurrent Neural Network (RNN) for modeling asynchronous streams, integrating multiple sources of external influence.
result RNN-MAS outperforms state-of-the-art YouTube popularity prediction system by 17%.
Various multi-agent models of wealth distributions defined by microscopic laws regulating the trades, with or without a saving criterion, are reviewed. We discuss and clarify the equilibrium properties of the model with constant global saving propensity, resulting in Gamma distributions, and their equivalence to the Ma…
Kinetic exchange models have been successful in explaining the shape of the income/wealth distribution in the economies. However, such models usually make some ad-hoc assumptions when it comes to determining the savings factor. Here, we examine a few models in and out of the domain of standard neo-classical economics t…
The simulator simplifies simulation code for statisticians.
problem Rapid prototyping of simulations introduces bugs.
method Creates a common infrastructure for simulations, focusing on specific aspects of each paper.
result Succinct, readable, and reusable simulation code.
BayTiDe discovers time-delayed differential equations from noisy data.
problem Discovering time-delayed differential equations from data with large delays and noise.
method Bayesian inference with a sparsity-promoting prior.
result BayTiDe accurately identifies time-delayed differential equations with accuracy proportional to data resolution.
Paper develops a method to learn optimal sparsity-promoting regularizers for linear inverse problems.
problem Solving linear inverse problems with sparse solutions.
method Bilevel optimization framework to select an optimal synthesis operator B. result Established well-posedness and theoretical guarantees for the learning process.
We consider the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving (two-body) collision. Unlike in the ideal gas, we introduce saving propensity λ of agents, such that each agent saves a fraction λ of its money and trades with t…
TCA algorithm saves up to 40% energy in IoT devices.
problem Energy consumption and user comfort trade-off in IoT devices.
method Incremental learning Temporal Clustering Algorithm (TCA) for IoT devices.
result Achieved up to 40% energy savings with high success rate.
NPS is widely used but flawed, harming companies and customers.
problem NPS lacks desirable characteristics for market research metrics.
method No specific method mentioned; focuses on NPS's shortcomings.
result NPS has done significant harm to companies and customers.
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.
Energy savings for DNN inference on resource-constrained devices.
problem Energy efficiency in deep learning inference for constrained devices.
method Efficiently searches through equivalent DNN graphs to find the one with the least execution cost.
result Achieves 24% energy savings with minimal performance impact.