Technical trading represents a class of investment strategies for Financial Markets based on the analysis of trends and recurrent patterns of price time series. According standard economical theories these strategies should not be used because they cannot be profitable. On the contrary it is well-known that technical t…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,695 papers · 148 categories
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48 results for “resistive memory”
One-step learning in crosspoint memory reduces computation time.
problem Real-time AI at the edge requires fast, low-energy computing.
method Crosspoint resistive memory with feedback configures linear and logistic regression.
result Linear and logistic regression can be computed in one step.
High-speed model accurately simulates neuromorphic devices.
problem Accurately modeling stochastic synapses in large-scale neuromorphic systems.
method Generative vector autoregressive model based on resistive memory cell data.
result Fast, high-throughput model reproduces synaptic parameters and correlations.
RESIST improves decentralized learning resilience against MITM attacks.
problem Decentralized learning's vulnerability to MITM attacks.
method Multistep consensus gradient descent framework with robust statistics-based screening methods.
result Achieves algorithmic and statistical convergence for various ERM problems.
Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications. By converting dense models into sparse ones, pruning appears to be a promising solution to reducing the computation/memory cost. This paper studies classificat…
Financial Brownian particle in the layered order book fluid and Fluctuation-Dissipation relationsq-fin.TR
We introduce a novel description of the dynamics of the order book of financial markets as that of an effective colloidal Brownian particle embedded in fluid particles. The analysis of a comprehensive market data enables us to identify all motions of the fluid particles. Correlations between the motions of the Brownian…
This paper approximates -resistance for multi-class graph clustering.
problem Efficiently clustering graphs with -Laplacian bias.
method Developed an approximation to -resistance for multi-class clustering.
result Approximated -resistance provides a computationally feasible alternative to -Laplacian eigenvectors.
TxSim models DNN training on resistive crossbars, improving accuracy and feasibility.
problem Non-idealities in resistive crossbar systems degrade DNN training accuracy.
method TxSim is a fast, customizable framework that models all training operations, reducing simulation time with minimal accuracy loss.
result TxSim reveals substantial (3%-10%) accuracy degradation due to non-idealities in large-scale DNNs.
FAL improves formation resistivity prediction from cased boreholes with noise resistance.
problem Noise and high-frequency disaster in predicting formation resistivity from cased boreholes.
method Frequency-aware framework and temporal anti-noise block for LSTM.
result FAL achieves a 24.3% improvement in R2 over LSTM, reaching R2=0.91.
Optimal trading strategy under market resistance and concave price impact model.
problem Optimal trading in a market with endogenous resistance and concave price impact.
method Modeling market resistance, deriving a stochastic Fredholm equation, proving existence and uniqueness, proposing an iterative scheme.
result Existence and uniqueness of optimal control under certain conditions, exponential convergence of iterative scheme.
Extends Newton's minimal resistance problem to Riemannian surfaces.
problem Minimal resistance on Riemannian surfaces.
method Derive resistance functional, analyze constrained minimization.
result Smooth extremals are loxodromes, global minimizers characterized.
New curvature defined via graph resistances leads to Ricci flow.
problem Defining curvature on graph edges for analysis.
method Introducing Ricci--Foster curvature based on effective resistances and studying Ricci flow.
result Existence of solutions to Ricci flow on short time intervals, preservation of nonnegative curvature.
Improved accuracy in neural networks using CTF memory with RPU.
problem Challenges in achieving high accuracy in neural networks using in-memory computing.
method Exploring the trade-off between conductance change range and linearity in CTF memory for RPU-based matrix operations.
result Achieved accuracy of 97.9% on MNIST dataset, 89.1% and 70.5% on CIFAR-10 and CIFAR-100 datasets.
New method calculates discrete curvature using effective resistances.
problem Calculating discrete curvature on graphs.
method Effective resistances to calculate curvature on graph nodes and links.
result Relation to established discrete curvatures and convergence to continuous curvature.
Genie clusters faster and resists outliers.
problem Hierarchical clustering's sensitivity to outliers and slow computation.
method Genie uses an economic inequity measure to link clusters, balancing speed and quality.
result Genie outperforms other linkage methods in clustering quality and speed.
Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing. Despite significant advancements in satellite technology, high resolution imagery lacks global coverage and can be prohibitively expensive to procure for extended time periods. Accurately classi…
Extends Newton's minimal resistance problem to Lorentz-Minkowski space.
problem Minimal resistance in Lorentz-Minkowski space.
method Derived functional energy, determined Euler-Lagrange equation, analyzed maximum principle, found separable and radial solutions.
result Obtained solutions with conical singularities at the origin and analyzed the Single Shock Condition.
Proposes a new method to improve deep model security against adversarial deformations.
problem Deep neural networks' resistance to adversarial attacks, especially location perturbations.
method Regularizes flow gradients to provide a tighter bound and improve model resistance.
result Models trained with flow gradient regularization show better resistance to adversarial deformations compared to input gradient regularization and adversarial training.
The article deals with intrinsic metrics, Dirac operators and spectral triples induced by regular Dirichlet and resistance forms. We show, in particular, that if a local resistance form is given and the space is compact in resistance metric, then the intrinsic metric yields a geodesic space. Given a regular Dirichlet f…
Study predicts antimicrobial resistance in ICU patients quickly.
problem Delayed AMR testing in ICU leads to suboptimal treatment.
method Developed predictive models using clinical and microbiological data.
result Machine learning models predict AMR with higher accuracy than naive model.
Graph curvature measured by inverse resistance distance.
problem Defining and analyzing curvature in graphs.
method Defining curvature via inverse resistance distance and proving properties.
result Graphs with positive curvature have controlled diameter and spectral properties.
Paper explores robust regression methods and their bias-variance trade-off.
problem Understanding the trade-off between robust estimation and optimization methods.
method Examines traditional outlier-resistant robust estimation and robust optimization.
result Both methods follow converse strategies due to a bias-variance trade-off.
A trend towards energy-efficiency, security and privacy has led to a recent focus on deploying DNNs on microcontrollers. However, limits on compute and memory resources restrict the size and the complexity of the ML models deployable in these systems. Computation-In-Memory architectures based on resistive nonvolatile m…
Post-quantum cryptography needed for blockchain security.
problem Quantum computers threaten traditional blockchain cryptography.
method Review of theoretical cryptography and quantum information theory.
result Post-quantum cryptography is essential for blockchain security.
Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide. Accurately predicting resistance phenotypes from genome sequences shows great promise in promoting better use of antimicrobial agents, by determining which antibiotics are likely to be effective i…
The radial Newton problem: nonlinear dynamics of minimal resistance in central fieldsphysics.flu-dyn
The paper studies minimal resistance dynamics in radial fields, finding unique solutions for incompressible flows.
problem Nonlinear dynamics of minimal resistance in radial fields.
method Analysis of two non-equilibrium scenarios: scale-invariant free expansion and incompressible source flow.
result Incompressible flow acts as a structural regularizer, admitting unique, smooth, and strictly concave solutions.
Study optimal times to buy and sell stocks using support/resistance lines.
problem Optimal times to buy and sell stocks based on support and resistance lines.
method Mathematical model with probabilistic methods to solve optimal stopping problems.
result Best times to buy and sell stocks are determined by solving free boundary problems.
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamicscs.LG
New method designs antimicrobial peptides with high potency and low toxicity.
problem Designing potent antimicrobial drugs with low toxicity.
method CLaSS method using deep generative autoencoder and atomistic simulations.
result Design and synthesis of two novel AMPs with high potency and low toxicity.
Paper proves minimal resistance for a body in a fluid with decreasing density.
problem Minimal resistance for a body moving through a fluid with non-constant density.
method Local existence and regularity of radial solutions using a fixed-point theorem.
result Maximal domain of the solution is finite, terminating at a critical slope.
Motivation: HIV is difficult to treat because its virus mutates at a high rate and mutated viruses easily develop resistance to existing drugs. If the relationships between mutations and drug resistances can be determined from historical data, patients can be provided personalized treatment according to their own mutat…
Optimal Prediction of Resistance and Support Levels under Constant Elasticity of Variance Processesq-fin.MF
This paper optimizes predicting support and resistance levels in financial markets.
problem Optimizing prediction of resistance and support levels in financial markets.
method Assuming a constant elasticity of variance process, the paper derives optimal trading boundaries using the aspiration level hypothesis.
result Optimal trading boundaries serve as predictors of resistance and support levels, located relative to the median interval of the hidden aspiration level.
Rapid identification of bacteria is essential to prevent the spread of infectious disease, help combat antimicrobial resistance, and improve patient outcomes. Raman optical spectroscopy promises to combine bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving cl…
The paper discovers and evaluates support and resistance levels in financial time series.
problem Understanding and predicting support and resistance levels in financial markets.
method Developed a heuristic discovery algorithm to identify SR levels in intraday price series.
result Discovered SR levels statistically significantly reverse price trends and have a decay aspect over time.
Non-volatile memory, such as resistive RAM (RRAM), is an emerging energy-efficient storage, especially for low-power machine learning models on the edge. It is reported, however, that the bit error rate of RRAMs can be up to 3.3% in the ultra low-power setting, which might be crucial for many use cases. Binary neural n…
New approach uses RS levels to improve stock trading RL model efficiency and stability.
problem Stability and adaptivity challenges in current financial RL applications.
method Leverages RS levels as regularization terms in MBRL for stock trading optimization.
result Enhanced performance and better profit gain with less riskiness compared to pure MBRL models.
Convolutional neural networks are state-of-the-art for various segmentation tasks. While for 2D images these networks are also computationally efficient, 3D convolutions have huge storage requirements and therefore, end-to-end training is limited by GPU memory and data size. To overcome this issue, we introduce a netwo…
Shells resist three out of six possible loads if simply connected.
problem Understanding the load resistance of shells.
method Formal mathematical analysis of shell strains and deflections.
result The space of strains is three-dimensional for simply-connected shells.
Gaining Outlier Resistance with Progressive Quantiles: Fast Algorithms and Theoretical Studiesstat.ME
New framework robustifies loss functions with quantiles for outlier resistance.
problem Widespread outliers in big data affect statistical estimation and inference.
method Introduces a framework connecting to trimming, scalable algorithms, and new techniques.
result Robust estimators achieve minimax rate optimality in regression, classification, and neural networks.
Lithography simulation is one of the key steps in physical verification, enabled by the substantial optical and resist models. A resist model bridges the aerial image simulation to printed patterns. While the effectiveness of learning-based solutions for resist modeling has been demonstrated, they are considerably data…
OGAN attacks autoencoders to prevent deepfake creation.
problem Preventing deepfake creation using adversarial attacks.
method Training-resistant adversarial attack on face-swapping autoencoders.
result OGAN attacks are training-resistant and transfer across different models and faces.
Bayesian neural networks are compressed using feature and weight pruning based on posterior inclusion probabilities.
problem Efficiently compressing Bayesian neural networks to reduce computation cost and improve generalizability.
method Bayesian model selection principles are applied to obtain posterior inclusion probabilities for pruning and feature selection.
result Pruned models show better generalizability on simulated and real-world data.
New invariant for special alternating links based on graph Laplacian.
problem Developing an invariant for special alternating links.
method Using the Laplacian matrix of the Tait graph, invariant is defined.
result A specific quadratic trace expression is invariant under flype moves.
Currently, approximately 30% of epileptic patients treated with antiepileptic drugs (AEDs) remain resistant to treatment (known as refractory patients). This project seeks to understand the underlying similarities in refractory patients vs. other epileptic patients, identify features contributing to drug resistance acr…
FastGAT reduces GNN computation time by 10x using graph sparsification.
problem High computational burden in attention-based GNNs.
method Spectral sparsification to generate optimal graph pruning.
result Per-epoch time is almost linear in graph nodes, reducing computational time by up to 10x.
Study uses AI and ML to predict and optimize corrosion resistance of aluminum alloys.
problem Corrosion resistance of aluminum alloys in marine environments.
method Investigated two ML approaches: direct and inverse, using Random Forest, neural network, and Gaussian Process Regression.
result Gaussian Process Regression with hybrid kernel functions provided superior predictive performance.
The AdaBoost algorithm has the superiority of resisting overfitting. Understanding the mysteries of this phenomena is a very fascinating fundamental theoretical problem. Many studies are devoted to explaining it from statistical view and margin theory. In this paper, we illustrate it from feature learning viewpoint, an…
RESPIRE calibrates low-cost air-quality sensors for CO levels, resistant to outliers.
problem Calibrating LCAQ sensors against regulatory-grade monitors is expensive and time-consuming.
method PROvably outlier-resistant semi-parametric regression technique.
result RESPIRE offers improved prediction in cross-site, cross-season, and cross-sensor settings.
We present several models to describe the stochastic evolution of stocks that show some strong resistance at some level and generalize to this situation the evolution based upon geometric Brownian motion. If volatility and drift are related in a certain way we show that our model can be integrated in an exact way. The …