arXiv research

Novel approach uses Gaussian processes to estimate conflict trends.

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.

The paper uses conformal prediction for forecasting conflict sequences in Markov processes.

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.

Paper proposes active learning for mining conflict dynamics from textual data.

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…

Hybrid model improves geopolitical conflict forecasting.

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…

Study examines grain futures connectedness during Russia-Ukraine conflict.

WISCA generates consensus explanations from conflicting model-agnostic interpretability methods.

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 $\R^n$ occur when $0 \leq n-l \leq 4$. Other related sets, such as kite curves a…

Researchers develop a neural network that learns like humans, overcoming forgetting and structure issues.

The study analyzes implicit biases in neural networks using backward error analysis.

A 2-complex requires at least 12 colours to avoid edge conflicts.

The study analyzes the conflict between group fairness and individual fairness in machine learning.

Study analyzes crude oil futures markets using visibility graphs to understand their structure and dynamics.

Time inconsistency leads to intra-personal conflict and reconciliation strategies.

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 $k$-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…

Proposes a new framework for uncertainty-aware LLM post-training.

Study compares MAPF and MARL algorithms for warehouse automation.

New method robustly discovers causal relationships from imperfect data.

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 $\mathcal{M}$ 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 …

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…

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…

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…

EXAGREE selects a stakeholder-aligned model to reduce conflicting explanations in machine learning.

New methods resolve conflicting treatment effect estimates in health tech assessments.

Combining complementary information from multiple modalities is intuitively appealing for improving the performance of learning-based approaches. However, it is challenging to fully leverage different modalities due to practical challenges such as varying levels of noise and conflicts between modalities. Existing metho…

This paper analyzes the conflict between Hamming loss and subset accuracy in multi-label classification.

The paper reconciles two conflicting fairness criteria in algorithmic risk scores.