Market impact is reduced when orders are filled with concentrated counterparts.
problem Market impact increases with a large number of trading counterparts.
method Analyzed London Stock Exchange data to show concentrated trading impacts market price.
result Concentrated trading reduces market impact when matched with similarly concentrated counterparts.
Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management. One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning. Comprehensively, the majo…
TripDecoder recovers metro routes and travel times from smart card data.
problem Recover unknown routes and travel times in metro systems.
method Decouples two inference tasks: travel time and route preference.
result TripDecoder improves accuracy and efficiency compared to competitors.
A great deal of academic and theoretical work has been dedicated to optimal liquidation of large orders these last twenty years. The optimal split of an order through time (`optimal trade scheduling') and space (`smart order routing') is of high interest \rred{to} practitioners because of the increasing complexity of t…
CARROT optimizes LLM routing by choosing the cheapest and most accurate model.
problem Optimizing query routing to the most cost-effective LLM for a given task.
method CARROT uses cost and performance estimates to select the best LLM for each query.
result CARROT is minimax optimal, selecting the best LLM for any query.
New system assigns vehicles to routes for cost and energy efficiency.
problem Optimizing vehicle route assignment for cost and energy efficiency.
method Machine learning-based neural network algorithm to estimate energy consumption and provide real-time recommendations.
result Demonstrated efficient vehicle assignment for medium and heavy duty trucks.
Study shows marketable order routing to wholesalers benefits all traders, leading to lower market depth and price volatility.
problem Determining the preference of retail traders for marketable order routing.
method Two models: one for market makers competing for retail order flow (Bertrand model) and another for price-taking competitive liquidity providers (open exchange model).
result Routing marketable orders to wholesalers is preferred by all traders, leading to mean reverting inventories and lower market depth.
Bike usage in Smart Cities becomes paramount for sustainable urban development. Cycling provides tremendous opportunities for a more healthy lifestyle, lower energy consumption and carbon emissions as well as reduction of traffic jams. While the number of cyclists increase along with the expansion of bike sharing initi…
Optimal crypto asset routing with CFMMs, including fixed costs.
problem Optimizing order execution on a network of CFMMs with fixed costs.
method Convex optimization for no fixed costs, mixed-integer convex for fixed costs, heuristics for approximate solutions.
result Approximate solutions to optimal routing and arbitrage certification problems.
An efficient algorithm optimizes trades across CFMM networks.
problem Optimizing trades through a network of CFMMs for maximum utility.
method Decomposition method to solve the routing problem.
result Significant performance improvements over commercial solvers.
This study develops an unsupervised learning algorithm for products of expert capsules with dynamic routing. Analogous to binary-valued neurons in Restricted Boltzmann Machines, the magnitude of a squashed capsule firing takes values between zero and one, representing the probability of the capsule being on. This analo…
In the edge computing paradigm, mobile devices offload the computational tasks to an edge server by routing the required data over the wireless network. The full potential of edge computing becomes realized only if a smart device selects the most appropriate server in terms of the latency and energy consumption, among …
In this paper, we propose a game theoretical adversarial intervention detection mechanism for reliable smart road signs. A future trend in intelligent transportation systems is ``smart road signs" that incorporate smart codes (e.g., visible at infrared) on their surface to provide more detailed information to smart veh…
SMART combines decision trees and MARS for better regression modeling.
problem High variance in decision trees for continuous relationships, poor performance in MARS for discontinuities.
method SMART uses a decision tree to identify subsets with distinct continuous relationships, then applies MARS to fit these relationships independently.
result SMART improves regression performance over state-of-the-art methods in capturing discontinuities and continuous relationships.
Smart city surveillance benefits from sound event recognition.
problem Improving monitoring capabilities in smart cities.
method Exploration of several classifiers on the SESA dataset.
result SGD achieved 72.13% accuracy in sound event recognition.
Hierarchical clustering models capture electricity load patterns from smart meters.
problem Improving efficiency and sustainability of electricity systems.
method Quantile autocovariances and autocorrelations for summarizing time series.
result Clusters identify relevant consumption behaviors and capture geo-demographic segmentation.
This paper introduces STAP to measure DEX efficiency and shows better routing algorithms increase DEX performance and stakeholder benefits.
problem Measuring and improving the efficiency of decentralized exchanges (DEXs).
method Introduces STAP as a measure of DEX efficiency and compares two routing algorithms.
result Better routing algorithms improve DEX efficiency and stakeholder benefits.
Deep learning solves EV routing with time windows for EV fleets.
problem Electric vehicle routing with time windows for logistics.
method End-to-end deep reinforcement learning with attention model and graph embedding.
result Proposed model efficiently solves large EVRPTW instances.
Survey of RL in finance, tackling complex decision-making.
problem Complex financial decision-making problems with limited model assumptions.
method Value and policy-based RL algorithms, neural networks, deep RL.
result Improved financial decision-making with less model assumptions.
Study optimizes smart contract adoption under high demand variability using Negative Binomial models.
problem Effective supply chain management under high demand variability.
method Combines dynamic Negative Binomial demand modeling with endogenous smart contract adoption optimization.
result The NB model outperforms other benchmarks in forecasting and optimizing smart contract adoption and order quantity.
Paper uses DRL for smart MG energy dispatch, improving stability and performance.
problem Improving energy dispatch in IoT-driven smart MGs with DGs, PVs, and batteries.
method Formulated POMDP model, proposed FH-DDPG and FH-RDPG algorithms, compared with baseline algorithms.
result Proposed algorithms enhance MG performance and stability under uncertainty.
New methods learn correlated equilibria in large games without structural assumptions.
problem Learning correlated equilibria in large, anonymous games with exponential player count.
method Developed Mean-Field correlated and coarse-correlated equilibria, and used classical algorithms to learn them efficiently.
result Efficiently learned correlated equilibria in all games without structural assumptions.
A new approach for instance-optimal learning that bypasses impossibility results.
problem Impossibility of achieving marginal-by-marginal guarantees for all marginals.
method Introduces relatively smart learning, which requires competition only with certifiable semi-supervised guarantees.
result One-Inclusion Graph learner is relatively smart up to squaring the sample complexity.
AutoPQ automates quantile forecasting for smart grids, reducing workload and environmental impact.
problem Accurate and unbiased uncertainty quantification in probabilistic forecasting for smart grid operations.
method AutoPQ uses a conditional Invertible Neural Network (cINN) to generate quantile forecasts from point forecasts, automating model selection and hyperparameter optimization.
result AutoPQ outperforms state-of-the-art methods while reducing computational effort and environmental impact.
Proposes a probabilistic framework for smart contract risk quantification.
problem Quantifying financial risk of smart contract cyber attacks and failures.
method Probabilistic graph-theoretical framework using bond percolation models.
result Analytical results and numerical examples for aggregate loss distribution.
Paper proposes auction method for smart derivatives to avoid disputes.
problem Disputes over derivative liquidation processes in smart contracts.
method Defines an auction type resolution for smart derivatives.
result Proposes a beneficial method for smart derivatives participants.
Recently, a wide range of smart devices are deployed in a variety of environments to improve the quality of human life. One of the important IoT-based applications is smart homes for healthcare, especially for elders. IoT-based smart homes enable elders' health to be properly monitored and taken care of. However, elder…
Dynamic-weight AMMs outperform traditional CEX rebalancing in tokenized funds, especially on L2s.
problem Improving asset allocation efficiency in decentralized finance (DeFi) protocols.
method Block-level arbitrage analysis and long-term performance benchmarks on two live pools.
result Dynamic-weight AMMs can achieve performance comparable to or better than traditional CEX rebalancing, especially on Layer 2 (L2) networks.
ETHGamDet detects crypto gambling contracts and addresses.
problem Identifying crypto gambling contracts and addresses.
method Multi-modal retrieval methods, LightGBM model with memory components.
result ETHGamDet achieves high F1-scores in address and contract classification.
Hybrid QC system for Bengali questions using smart data balancing.
problem Classifying factoid questions in Bengali.
method Two-stage approach with 1D CNN for coarse classification and SGD for fine classification.
result Effectiveness of smart data balancing technique in improving classification accuracy.
Capsule networks improve performance on image classification tasks with fewer parameters.
problem Improving performance of capsule networks with fewer parameters.
method Inverted dot-product attention routing, Layer Normalization, concurrent iterative routing.
result Improves performance on benchmark datasets CIFAR-10 and CIFAR-100, and performs at-par with ResNet-18.
FinTech uses data science and AI to transform finance.
problem Transforming finance with data science and AI.
method DSAI techniques including complex system methods, quantitative methods, etc.
result DSAI enables smart FinTech for various financial sectors.
Route Choice Models predict the route choices of travelers traversing an urban area. Most of the route choice models link route characteristics of alternative routes to those chosen by the drivers. The models play an important role in prediction of traffic levels on different routes and thus assist in development of ef…
Accurate and reliable travel time predictions in public transport networks are essential for delivering an attractive service that is able to compete with other modes of transport in urban areas. The traditional application of this information, where arrival and departure predictions are displayed on digital boards, is…
Smart bin monitors predict medication adherence with high accuracy.
problem Predicting chronic medication adherence to improve healthcare efficiency.
method Machine learning models trained on IoT device data.
result High predictive performance (ROC AUC 0.86).
Text classification is a challenging problem which aims to identify the category of texts. In the process of training, word embeddings occupy a large part of parameters. Under the limitation of limited computing resources, it indirectly limits the ability of subsequent network designs. In order to reduce the number of …
The paper categorizes four types of scale-up: smart, dumb, forced, and fumbled.
problem Growing ventures in size and maintaining efficiency.
method Identifying modularity and speed as key factors, categorizing four types of scale-up.
result Modularity and speed are crucial for successful scale-up.
JAMPR learns to solve complex VRP with time windows.
problem Vehicle routing problems with time windows and vehicle capacities.
method Joint attention to construct multiple routes concurrently.
result JAMPR outperforms existing models on different problem sizes.
CROCS clusters consumer behaviour from smart meters, capturing variability and robustness.
problem Insufficient consumer segmentation in existing clustering methods.
method Two-stage clustering framework: first stage clusters daily load profiles, second stage uses WSMD for set-to-set comparison.
result CROCS captures intra-consumer variability and robustness to anomalies and missing data.
A large GPS dataset reveals that a single route often covers 60% of travel observations.
problem Limited insights from small GPS datasets on route choice behavior.
method Evaluation of path generation algorithms including link penalty, link elimination, simulation, and via-node methods.
result Modified link penalty method achieves 97% coverage, significantly higher than previous studies.
Global routing has been a historically challenging problem in electronic circuit design, where the challenge is to connect a large and arbitrary number of circuit components with wires without violating the design rules for the printed circuit boards or integrated circuits. Similar routing problems also exist in the de…
The most common method for DNN pruning is hard thresholding of network weights, followed by retraining to recover any lost accuracy. Recently developed smart pruning algorithms use the DNN response over the training set for a variety of cost functions to determine redundant network weights, leading to less accuracy deg…
We propose and systematically evaluate three strategies for training dynamically-routed artificial neural networks: graphs of learned transformations through which different input signals may take different paths. Though some approaches have advantages over others, the resulting networks are often qualitatively similar…
A new router uses attention-based reinforcement learning to solve detailed routing problems efficiently.
problem Solving detailed routing in integrated circuits while adhering to complex design rules.
method Attention-based reinforcement learning applied to track-assignment detailed routing.
result The attention router achieves over 100x acceleration compared to a genetic router without sacrificing solution quality.
Improved security of smart contracts by classifying them into four categories.
problem Detecting and classifying vulnerabilities in smart contracts efficiently.
method Used AWD-LSTM for multi-class classification, addressing class imbalance.
result Achieved a weighted average Fbeta score of 90.0%.
Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart build…
Study develops smart contract framework for procurement under demand variability.
problem Operational and economic implications of smart contract adoption under moderate uncertainty.
method Multi-supplier model with endogenized adoption costs, supplier readiness, and inventory penalties; analytical and numerical results.
result Partial adoption strategies support moderate demand variability, while excessive digital investment reduces profitability.
Proposes a multi-objective variational autoencoder for smart infrastructure damage detection.
problem Detecting and diagnosing damage in smart infrastructure using multi-way data.
method Multi-objective variational autoencoder (MVA) method for smart infrastructure damage detection and diagnosis.
result The method accurately detects structural damage, estimates severity, and captures damage locations.