Blog
MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search
Deep learning (DL) has achieved remarkable progress in the field of medical imaging. However, adapting DL models to medical tasks remains a significant challenge, primarily due to two key factors: (1) architecture selection, as different tasks necessitate specialized model designs, and (2)...
An Extended Horizon Tactical Decision-Making for Automated Driving Based on Monte Carlo Tree Search
This paper introduces COR-MCTS (Conservation of Resources – Monte Carlo Tree Search), a novel tactical decision-making approach for automated driving focusing on maneuver planning over extended horizons. Traditional decision-making algorithms are often constrained by fixed planning horizons, typically up to 6 seconds...
Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications
An integration of satellites and terrestrial networks is crucial for enhancing performance of next generation communication systems. However, the networks are hindered by the long-distance path loss and security risks in dense urban environments. In this work, we propose a satellite-terrestrial covert...
Machine-learned RG-improved gauge actions and classically perfect gradient flows
Extracting continuum properties of quantum field theories from discretized spacetime is challenging due to lattice artifacts. Renormalization-group (RG)-improved lattice actions can preserve continuum properties, but are in general difficult to parameterize. Machine learning (ML) with gauge-equivariant convolutional neural networks provides a way...
Multiscale detection of practically significant changes in a gradually varying time series
In many change point problems it is reasonable to assume that compared to a benchmark at a given time point $t_0$ the properties of the observed stochastic process change gradually over time for $t >t_0$. Often, these gradual changes are not of...
Bidirectional Task-Motion Planning Based on Hierarchical Reinforcement Learning for Strategic Confrontation
In swarm robotics, confrontation scenarios, including strategic confrontations, require efficient decision-making that integrates discrete commands and continuous actions. Traditional task and motion planning methods separate decision-making into two layers, but their unidirectional structure fails to capture the interdependence between these layers, limiting...
Defects, Sound Damping, and the Boson Peak in Amorphous Solids
Two nearly universal and anomalous properties of glasses, the peak in the specific heat and plateau of the thermal conductivity, occur around the same temperature. This coincidence suggests that the two phenomena are related. Both effects can be rationalized by assuming Rayleigh...
Cryptoanalysis of a public key exchange based on circulant matrix over digital semiring
We present a cryptanalysis of a key exchange protocol based on the digital semiring. For this purpose, we find the maximal solution of a linear system over such semiring, and use the properties of circulant matrix to demonstrate that the protocol is...
Measurement of the time-integrated $CP$ asymmetry in $D^0 \to K^0_{\rm S} K^0_{\rm S}$ decays using opposite-side flavor tagging at Belle and Belle II
We measure the time-integrated $CP$ asymmetry in $D^0 \to K^0_{\rm S} K^0_{\rm S}$ decays reconstructed in $e^+e^-\to c{\overline c}$ events collected by the Belle and Belle II experiments. The corresponding data samples have integrated luminosities of 980 and 428 fb${}^{-1}$, respectively. To...
Branch-and-Bound Algorithms as Polynomial-time Approximation Schemes
Branch-and-bound algorithms (B&B) and polynomial-time approximation schemes (PTAS) are two seemingly distant areas of combinatorial optimization. We intend to (partially) bridge the gap between them while expanding the boundary of theoretical knowledge on the B&B framework. Branch-and-bound algorithms typically guarantee that an...