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.
AI threatens financial stability through misuse and stealth adoption.
Insiders camouflage trading to balance wealth and stealth, avoiding legal penalties.
StealthRank subtly boosts LLM rankings without detectable anomalies.
Federated learning distributes model training among a multitude of agents, who, guided by privacy concerns, perform training using their local data but share only model parameter updates, for iterative aggregation at the server. In this work, we explore the threat of model poisoning attacks on federated learning initia…
The introduction of automated flight control and management systems have made possible aircraft designs that sacrifice arodynamic stability in order to incorporate stealth technology intro their shape, operate more efficiently, and are highly maneuverable. Therefore, modern flight management systems are reliant on mult…
In recent years, neural networks have been extensively deployed for computer vision tasks, particularly visual classification problems, where new algorithms reported to achieve or even surpass the human performance. Recent studies have shown that they are all vulnerable to the attack of adversarial examples. Small and …
This paper considers the real-time detection of anomalies in high-dimensional systems. The goal is to detect anomalies quickly and accurately so that the appropriate countermeasures could be taken in time, before the system possibly gets harmed. We propose a sequential and multivariate anomaly detection method that sca…
Robust CLIP improves vision models' resistance to attacks.
Paper analyzes tech adoption in financial networks, finding key leadership and diffusion dynamics.
Matched filters reveal optimal normalization methods for different market participants.
We introduce techniques for exploring the functionality of a neural network and extracting simple, human-readable approximations to its performance. By performing gradient ascent on the input space of the network, we are able to produce large populations of artificial events which strongly excite a given classifier. By…
As online systems based on machine learning are offered to public or paid subscribers via application programming interfaces (APIs), they become vulnerable to frequent exploits and attacks. This paper studies adversarial machine learning in the practical case when there are rate limitations on API calls. The adversary …
Study analyzes smart contract adoption under bounded risk, showing stable adoption but fragile financial outcomes.
In this work, we analyze the problem of adoption of mobile money in Pakistan by using the call detail records of a major telecom company as our input. Our results highlight the fact that different sections of the society have different patterns of adoption of digital financial services but user mobility related feature…
The well-known Ising model used in statistical physics was adapted to a social dynamics context to simulate the adoption of a technological innovation. The model explicitly combines (a) an individual's perception of the advantages of an innovation and (b) social influence from members of the decision-maker's social net…
Study optimizes smart contract adoption under high demand variability using Negative Binomial models.
When the full stock of a new product is quickly sold in a few days or weeks, one has the impression that new technologies develop and conquer the market in a very easy way. This may be true for some new technologies, for example the cell phone, but not for others, like the blue-ray. Novelty, usefulness, advertising, pr…
Experts predict significant adoption of decentralized finance by 2034, with traditional finance adapting.
We propose a modelling framework for the optimal selection of crypto assets. Crypto assets differ by two essential features: security (technological) and stability (governance). Investors make choices over crypto assets similarly to how they make choices by using a recommender app: the app presents each investor with a…
Paper proposes a new method for predicting DER adoption with hierarchical guarantees.
Study develops smart contract framework for procurement under demand variability.
Model shows AI adoption amplifies financial market risk through prediction, herding, and cognitive dependency.
ADOPT optimizes Adam to converge with any β2 without bounded noise.
GenAI adoption paradoxically lowers ROE for U.S. banks, with spillovers but systemic risk concerns.
Paper develops robust econometric methods for staggered adoption studies.
Study uses exchangeable GPs for staggered-adoption policy evaluation in panel data.
AI-driven investment strategies self-defeat at scale due to signal crowding and erosion.
This paper analyzes the dynamic incentives for technology adoption under a transferable permits system, which allows for strategic trading on the permit market. Initially, firms can invest both in low-emitting production technologies and trade permits. In the model, technology adoption and allowance price are generated…
Deep learning faces adoption challenges in business analytics.
New method selects critical DER scenarios for distribution grid investment planning.
Survey of stablecoins to reduce cryptocurrency volatility.
In this study, the authors develop a structural model that combines a macro diffusion model with a micro choice model to control for the effect of social influence on the mobile app choices of customers over app stores. Social influence refers to the density of adopters within the proximity of other customers. Using a …
Blockchain-based exchanges adopt based on token pair volatility and personal use.
A new estimator reduces bias and improves efficiency for staggered adoption studies.
Recently, mobile operators in many developing economies have launched "Mobile Money" platforms that deliver basic financial services over the mobile phone network. While many believe that these services can improve the lives of the poor, a consistent difficulty has been identifying individuals most likely to benefit fr…
Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of …
We propose and discuss some toy models of stock markets using the same operatorial approach adopted in quantum mechanics. Our models are suggested by the discrete nature of the number of shares and of the cash which are exchanged in a real market, and by the existence of conserved quantities, like the total number of s…
Hashing techniques, also known as binary code learning, have recently gained increasing attention in large-scale data analysis and storage. Generally, most existing hash clustering methods are single-view ones, which lack complete structure or complementary information from multiple views. For cluster tasks, abundant p…
Dark blockchain venues increase miners' profits but raise users' execution risk.
Bitcoin's integration with major financial indices intensifies, suggesting a shift from alternative to integrated asset.
Studies report that firms do not invest in cost-effective green technologies. While economic barriers can explain parts of the gap, behavioural aspects cause further under-valuation. This could be partly due to systematic deviations of decision-making agents' perceptions from normative benchmarks, and partly due to the…
Synthetic experiments are crucial for assessing causal machine learning methods.
Model shows partial compliance can lead to less fair outcomes than expected.
Study uses artificial counterfactuals to show lockdowns reduced US case and death counts.
This paper explores IT governance for CBDC adoption in financial markets.
The significant computational requirements of deep learning present a major bottleneck for its large-scale adoption on hardware-constrained IoT-devices. Here, we envision a new paradigm called EdgeAI to address major impediments associated with deploying deep networks at the edge. Specifically, we discuss the existing …
Modeling tech transfer to explain convergence in Central and Eastern Europe.
To analyse a very large data set containing lengthy variables, we adopt a sequential estimation idea and propose a parallel divide-and-conquer method. We conduct several conventional sequential estimation procedures separately, and properly integrate their results while maintaining the desired statistical properties. A…