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.
Paper proposes active learning for mining conflict dynamics from textual data.
Hybrid model improves geopolitical conflict forecasting.
The paper uses conformal prediction for forecasting conflict sequences in Markov processes.
Researchers develop a neural network that learns like humans, overcoming forgetting and structure issues.
Study analyzes crude oil futures markets using visibility graphs to understand their structure and dynamics.
Novel approach uses Gaussian processes to estimate conflict trends.
Time inconsistency leads to intra-personal conflict and reconciliation strategies.
Study examines grain futures connectedness during Russia-Ukraine conflict.
The deterrent effect of military alliances is well documented and widely accepted. However, such work has typically assumed that alliances are exogenous. This is problematic as alliances may simultaneously influence the probability of conflict and be influenced by the probability of conflict. Failing to account for suc…
The paper shows conflict graphs of Petersen family graphs are mostly unbalanced.
Unified approach detects traffic conflicts across various interactions.
The study extends Tutte's conflict graph concept to nonplanar graphs.
A distinction has been drawn in fair machine learning research between `group' and `individual' fairness measures. Many technical research papers assume that both are important, but conflicting, and propose ways to minimise the trade-offs between these measures. This paper argues that this apparent conflict is based on…
Signed Evidence Flow (SEF) combines fitted prediction with signed feature attributions to measure evidence conflict and stability.
Russia-Ukraine conflict impacts global agricultural futures and spot markets' extreme risks.
Automatic conflict detection has grown in relevance with the advent of body-worn technology, but existing metrics such as turn-taking and overlap are poor indicators of conflict in police-public interactions. Moreover, standard techniques to compute them fall short when applied to such diversified and noisy contexts. W…
While theories postulating a dual cognitive system take hold, quantitative confirmations are still needed to understand and identify interactions between the two systems or conflict events. Eye movements are among the most direct markers of the individual attentive load and may serve as an important proxy of informatio…
Subjective Logic (SL) is one of well-known belief models that can explicitly deal with uncertain opinions and infer unknown opinions based on a rich set of operators of fusing multiple opinions. Due to high simplicity and applicability, SL has been substantially applied in a variety of decision making in the area of cy…
In this paper, we propose a test, called Flagged-1-Bit (F1B) test, to study the intrinsic capability of recurrent neural networks in sequence learning. Four different recurrent network models are studied both analytically and experimentally using this test. Our results suggest that in general there exists a conflict be…
This paper tackles multilingual speech processing by optimizing conflicting objectives hierarchically.
New method optimizes multiple objectives using particle dynamics and gradient flow.
We study velocity correlations induced by diffusion and dissipation in a simple dissipative dynamical system. We observe that diffusion, as a result of time reversible microscopic processes, leads to correlations with different spatial parity from those caused by dissipation, consisting of time irreversible microscopic…
Few-shot learning is a challenging problem where the goal is to achieve generalization from only few examples. Model-agnostic meta-learning (MAML) tackles the problem by formulating prior knowledge as a common initialization across tasks, which is then used to quickly adapt to unseen tasks. However, forcibly sharing an…
We first pursue the study of how hierarchy provides a well-adapted tool for the analysis of change. Then, using a time sequence-constrained hierarchical clustering, we develop the practical aspects of a new approach to wavelet regression. This provides a new way to link hierarchical relationships in a multivariate time…
We investigate the structure of global inter-firm linkages using a dataset that contains information on business partners for about 400,000 firms worldwide, including all the firms listed on the major stock exchanges. Among the firms, we examine three networks, which are based on customer-supplier, licensee-licensor, a…
Enhances conformal prediction for better uncertainty estimates in armed conflict fatalities.
Prediction problems often admit competing models that perform almost equally well. This effect challenges key assumptions in machine learning when competing models assign conflicting predictions. In this paper, we define predictive multiplicity as the ability of a prediction problem to admit competing models with confl…
Air traffic control is a real-time safety-critical decision making process in highly dynamic and stochastic environments. In today's aviation practice, a human air traffic controller monitors and directs many aircraft flying through its designated airspace sector. With the fast growing air traffic complexity in traditi…
We present CYCLADES, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting. CYCLADES is asynchronous during shared model updates, and requires no memory locking mechanisms, similar to HOGWILD!-type algorithms. Unlike HOGWILD!, CYCLADES introduces no conflicts during the par…
WISCA generates consensus explanations from conflicting model-agnostic interpretability methods.
Many real-world decision problems are characterized by multiple conflicting objectives which must be balanced based on their relative importance. In the dynamic weights setting the relative importance changes over time and specialized algorithms that deal with such change, such as a tabular Reinforcement Learning (RL) …
Conflict sets are loci of intersecting wavefronts emanating from different surfaces. We show that generically conflict sets are Legendrian: locally they admit the structure of wavefronts. Simple stable singularities for this problem in occur when . Other related sets, such as kite curves a…
Territorial control is a key aspect shaping the dynamics of civil war. Despite its importance, we lack data on territorial control that are fine-grained enough to account for subnational spatio-temporal variation and that cover a large set of conflicts. To resolve this issue, we propose a theoretical model of the relat…
Study dynamic matching in heterogeneous networks using ODE model.
The study analyzes implicit biases in neural networks using backward error analysis.
The paper analyzes dynamics of momentum in high dimensions with sparse updates.
A 2-complex requires at least 12 colours to avoid edge conflicts.
The paper explores how complex models can improve system identification beyond traditional limits.
Cascading chains of events are a salient feature of many real-world social, biological, and financial networks. In social networks, social reciprocity accounts for retaliations in gang interactions, proxy wars in nation-state conflicts, or Internet memes shared via social media. Neuron spikes stimulate or inhibit spike…
Study reveals trade dynamics in dry bulk shipping networks, highlighting their randomness and periodic changes.
Generative diffusion models forecast implied vol surfaces without arbitrage issues.
The gauge theory of arbitrage was introduced by Ilinski in [arXiv:hep-th/9710148] and applied to fast money flows in [arXiv:cond-mat/9902044]. The theory of fast money flow dynamics attempts to model the evolution of currency exchange rates and stock prices on short, e.g.\ intra-day, time scales. It has been used to ex…
The study analyzes the conflict between group fairness and individual fairness in machine learning.
Introduces Rashomon Capacity to measure predictive multiplicity in probabilistic classifiers.
MAMBA learns policies competitive with multiple conflicting oracles.
Entity resolution seeks to merge databases as to remove duplicate entries where unique identifiers are typically unknown. We review modern blocking approaches for entity resolution, focusing on those based upon locality sensitive hashing (LSH). First, we introduce -means locality sensitive hashing (KLSH), which is b…
LLMs show biases in investment analysis, leading to unreliable recommendations.
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…