Russia-Ukraine conflict impacts global agricultural futures and spot markets' extreme risks.
problem Impact of Russia-Ukraine conflict on global agricultural futures and spot markets' extreme risks.
method Analytical framework for tail dependence, Copula-CoVaR method, ARMA-GARCH-skewed Student-t model.
result The outbreak of the conflict intensified risks in the wheat market the most and showed significant asymmetries in extreme risk spillovers.
Study examines grain futures connectedness during Russia-Ukraine conflict.
problem Quantile return connectedness of grain futures markets during geopolitical instability.
method Dynamic quantile VAR combined with frequency-domain decomposition.
result Heterogeneous spillovers across quantiles, with strong transmitters and persistent receivers.
Study analyzes crude oil futures markets using visibility graphs to understand their structure and dynamics.
problem Understanding the structure and dynamics of crude oil futures markets during global challenges.
method Visibility graph analysis of daily and high-frequency data.
result Crude oil futures markets exhibit small-world properties and assortative mixing, with unique sensitivities to global disruptions.
Geopolitical and geoeconomic shocks affect sovereign risk differently, with distinct transmission channels.
problem Understanding how geopolitical and geoeconomic shocks impact sovereign credit risk.
method Daily panel data of 42 economies over 2018-2025; semistructural framework; Shapley-Taylor decomposition; machine learning predictions; placebo and sign-restricted SVAR evidence.
result Geopolitical shocks primarily increase sovereign credit spreads through direct repricing, while geoeconomic shocks mainly affect spreads through financial conditions and policy uncertainty.
Study examines spillovers between BRICS and U.S. staple grain futures markets.
problem Contemporaneous and lagged spillover effects in BRICS staple grain futures markets and their linkages with U.S. markets.
method Examines contemporaneous and lagged spillover effects using econometric models.
result Contemporaneous spillovers dominate, and net spillovers are driven by lagged connectedness. Systemic risk is lower in intra-BRICS markets compared to those including the U.S.
Study finds ESG investments more resilient than traditional equity indices during market turmoil.
problem Resilience of ESG investments during financial instability.
method Daily returns analysis using MGND and EGARCH-in-mean models.
result ESG investments show higher resilience compared to traditional equity indices during crises.
Novel approach uses Gaussian processes to estimate conflict trends.
problem Estimating temporal and spatial patterns of violent conflict.
method Highly disaggregated conflict event data with Gaussian processes.
result Powerful conflict forecasts and insights into conflict dynamics.
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.
problem Understanding the balance of conflict graphs in Petersen family graphs.
method Analyzing maximally planar subgraphs and their conflict graphs.
result All but three strong conflict graphs from Petersen Family Graphs are unbalanced.
Unified approach detects traffic conflicts across various interactions.
problem Inconsistent detection of traffic conflicts across different interactions.
method Unified probabilistic approach decomposes conflicts into statistical learning tasks.
result Effective collision warnings across diverse datasets and environments.
Paper resolves apparent conflict between group and individual fairness.
problem Apparent conflict between group and individual fairness in machine learning.
method Theoretical discussions from fair machine learning, political, and legal philosophy.
result Individual and group fairness are not fundamentally in conflict.
The study extends Tutte's conflict graph concept to nonplanar graphs.
problem Understanding the structure of nonplanar graphs through conflict graphs.
method Defining a signed conflict graph for maximally planar subgraphs and analyzing their balance.
result For graphs with a flat embedding, every maximal planar subgraph has unbalanced conflict graphs if and only if the graph is intrinsically linked.
Signed Evidence Flow (SEF) combines fitted prediction with signed feature attributions to measure evidence conflict and stability.
problem Modern data analysis lacks mechanisms to show the clarity, conflict, or stability of evidence behind predictions.
method Signed Evidence Flow (SEF) combines fitted prediction with signed feature attributions.
result SEF measures conflict and stability, and shows that conflict can improve loss prediction beyond confidence.
The paper uses conformal prediction for forecasting conflict sequences in Markov processes.
problem Forecasting future conflict states for national policy decisions.
method Conformal prediction on Markov processes for temporally-dependent data.
result Conformal prediction provides valid uncertainty quantification and robustness to model misspecification.
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…
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.
problem Training models for multilingual, multi-task speech processing is hampered by conflicting objectives.
method Investigates three multi-objective MSP formulations and introduces a lightweight layer-selection mechanism.
result A bi-level recipe outperforms standard flat optimization in state-of-the-art MSP models.
Paper proposes active learning for mining conflict dynamics from textual data.
problem Lack of detailed information on conflict dynamics in existing datasets.
method Active learning with a large, encoder-only language model.
result Performance similar to human coding with reduced human annotation.
This study proposes an equal-weight portfolio strategy to reduce risk compared to traditional ETFs.
problem Risk of passive ETFs not matching optimal portfolio weights.
method Introduced an equal-weight portfolio strategy to reduce idiosyncratic risk.
result Equal-weight portfolio has lower risk than traditional ETFs, especially during idiosyncratic events.
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.
problem Lack of individual-level uncertainty estimates in existing forecasting models.
method Introduces bin-conditional conformal prediction (BCCP) to improve coverage rates across subsets of the outcome variable.
result Demonstrates improved local coverage and well-calibrated uncertainty estimates across various ranges of fatalities.
Hybrid model improves geopolitical conflict forecasting.
problem Forecasting geopolitical events from sparse, bursty data.
method Sparse Temporal Fusion Transformer (TFT) + Variational Nearest Neighbor Gaussian Process (VNNGP).
result Consistently outperforms standalone TFT in long-range horizons.
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…
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.
problem Conflicting explanations from diverse interpretability algorithms.
method WISCA integrates class probability and normalized attributions to generate consistent explanations.
result WISCA consistently aligns with the most reliable individual method, improving explanation reliability.
Conflict sets are loci of intersecting wavefronts emanating from l different surfaces. We show that generically conflict sets are Legendrian: locally they admit the structure of wavefronts. Simple stable singularities for this problem in Rn occur when 0≤n−l≤4. Other related sets, such as kite curves a…
Researchers develop a neural network that learns like humans, overcoming forgetting and structure issues.
problem Catastrophic forgetting and structure limitations in neural networks.
method Memory playback strategy and dynamic structure extension using conditional variational autoencoder (CVAE).
result The method effectively prevents forgetting and allows for dynamic network growth.
The study analyzes implicit biases in neural networks using backward error analysis.
problem Analyzing implicit biases in multitask and continual learning settings.
method Backward error analysis to compute implicit training biases, deriving modified losses with three terms.
result The conflict term, measuring gradient alignment, is a new quantity in continual learning.
A 2-complex requires at least 12 colours to avoid edge conflicts.
problem Generalizing the Four Colour Theorem to three dimensions.
method Examined 2-complexes embedded in 3-manifolds, proving necessity and sufficiency of 12 colours.
result 12 colours are both necessary and sufficient for 2-complexes in 3-manifolds.
The study analyzes the conflict between group fairness and individual fairness in machine learning.
problem The conflict between group fairness (optimal statistical parity) and individual fairness in machine learning.
method Established sufficient conditions for the compatibility between optimal statistical parity and individual fairness requirements.
result Identified regions along the Pareto frontier that satisfy individual fairness requirements.
Time inconsistency leads to intra-personal conflict and reconciliation strategies.
problem Time inconsistency in dynamic choice problems.
method Rigorous treatment of intra-personal equilibrium in continuous-time settings.
result A new approach to understanding and reconciling intra-personal conflicts.
Introduces Rashomon Capacity to measure predictive multiplicity in probabilistic classifiers.
problem Predictive multiplicity in classification models leading to unjustified decisions.
method Introduces Rashomon Capacity, a metric for probabilistic classifiers, and provides a rigorous derivation.
result Rashomon Capacity captures nuanced score variations and provides strategies for disclosing conflicting models.
MAMBA learns policies competitive with multiple conflicting oracles.
problem Learning policies from multiple conflicting oracles in reinforcement learning.
method MAMBA uses a gradient estimator in the style of GAE to optimize policies, leveraging demonstrations from multiple weak oracles.
result MAMBA outperforms the state-of-the-art in learning 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 k-means locality sensitive hashing (KLSH), which is b…
LLMs show biases in investment analysis, leading to unreliable recommendations.
problem LLMs face conflicts between pre-trained knowledge and real-time market data, leading to biases in investment analysis.
method Experimental framework to investigate emergent behaviors in LLMs, analyzing sector, size, and momentum biases.
result Distinct, model-specific biases observed, including a tendency to prefer technology stocks, large-cap stocks, and contrarian strategies.
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…
Proposes a new framework for uncertainty-aware LLM post-training.
problem Heterogeneous, conflicting data in large language models.
method α-Rényi variational framework for learning distributions over post-training parameters.
result Enables training examples to be softly routed across ensemble members, promoting model specialisation and providing uncertainty estimates.
Study compares MAPF and MARL algorithms for warehouse automation.
problem Optimizing multi-agent pickup and delivery in warehouse settings.
method Compared conflict-based search (MAPF) and shared experience actor-critic (MARL).
result Comprehensive benchmarking of MAPF and MARL in a simulated warehouse environment.
New method robustly discovers causal relationships from imperfect data.
problem Challenges in causal discovery from imperfect structural constraints.
method Prior alignment and conflict resolution through surrogate model and multi-task learning.
result Proposes a robust method for causal discovery under imperfect constraints.
The use of machine learning (ML) is on the rise in many sectors of software development, and automotive software development is no different. In particular, Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are two areas where ML plays a significant role. In automotive development, safety is…
This paper consists of two parts. In the first one we study the behaviour of medial axes (skeletons) of closed sets in a connected complete Riemannian manifold M under deformations. The second one is devoted to a similar study of conflict sets. We apply a new approach to the deformation process. Instead of …
AGI modifies utility function to cooperate, conflicting with orthogonality thesis.
problem AGI modifying utility function to improve cooperation, conflicting with orthogonality thesis.
method Observing and influencing interactions, converging to similar utility functions.
result AGIs in competitive environments may optimize for favorable influence.
Study shows negative war news correlates with increased stock market volatility.
problem Understanding the impact of geopolitical events on financial markets.
method Used BERT model for sentiment analysis and GARCH model for volatility forecasting.
result Negative news sentiment during geopolitical crises is associated with increased stock market volatility.
Bidders in day-ahead electricity markets want to sell/buy electricity when their bids generate positive surplus and not to take an action when the reverse holds. However, non-convexities in these markets cause conflicts between the actions that the bidders want to take and the actual market results. In this work, we in…
This describes a statistical technique called "tonsuring" for exploratory data analysis in finance. Instead of rejecting "outlier" data that conflicts with the model, this strips out "inlier" data to get a clearer picture of how the market changes for larger moves.
In industrial environments, an increasing amount of wireless devices are used, which utilize license-free bands. As a consequence of these mutual interferences of wireless systems might decrease the state of coexistence. Therefore, a central coexistence management system is needed, which allocates conflict-free resourc…
Armed conflict has led to an unprecedented number of internally displaced persons (IDPs) - individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when large fluxes of IDPs will cross into an area remains a major challen…