Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

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The chemisorption energy of reactants on a catalyst surface, Eads, is among the most informative characteristics of understanding and pinpointing the optimal catalyst. The intrinsic complexity of catalyst surfaces and chemisorption reactions presents signi ...

Entanglement forging based variational algorithms leverage the bipartition of quantum systems for addressing ground-state problems. The primary limitation of these approaches lies in the exponential summation required over the numerous potential basis stat ...

Materials that efficiently promote the thermodynamically uphill water-splitting reaction under solar illumination are essential for generating carbon-free ("green") hydrogen. Mapping out the combinatorial space of potential photocatalysts for this reaction ...

Dynamical perturbations modify the states of classical systems in surprising ways and give rise to important applications in science and technology. For example, Floquet engineering exploits the possibility of band formation in the frequency domain when a ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

Many applications, e.g. in content recommendation, sports, or recruitment, leverage the comparisons of alternatives to score those alternatives. The classical Bradley-Terry model and its variants have been widely used to do so. The historical model conside ...

Quantum computing not only holds the potential to solve long-standing problems in quantum physics, but also to offer speed-ups across a broad spectrum of other fields. Access to a computational space that incorporates quantum effects, such as superposition ...

In this thesis, we give new protocols that offer a quantum advantage for problems in ML, Physics, and Finance. Quantum mechanics gives predictions that are inconsistent with local realism. The experiment proving this fact (Bell, 1964) gives a quantum proto ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

Humans are chronically exposed to airborne microplastics (MPs) by inhalation. Various types of polymer particles have been detected in lung samples, which could pose a threat to human health. Inhalation toxicological studies are crucial for assessing the e ...

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

How to measure students' Computational Problem-Solving (CPS) competencies is an ongoing research topic. Prevalent approaches vary by measurement tools (e.g., interactive programming, multiple-choice tests, or programming-independent tests) and task types ( ...

LiCu3O3 is an antiferromagnetic mixed valence cuprate where trilayers of edge -sharing Cu(II)O (3d9) are sandwiched in between planes of Cu(I) (3d10) ions, with Li stochastically substituting Cu(II). Angle resolved photoemission spectroscopy (ARPES) and de ...

Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is construc ...

Smarty is a fully-reconfigurable on-chip feed-forward artificial neural network (ANN) with ten integrated time-to-digital converters (TDCs) designed in a 16 nm FinFET CMOS technology node. The integration of TDCs together with an ANN aims to reduce system ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Situational awareness strategies are essential for the reliable and secure operation of the electric power grid which represents critical infrastructure in modern society. With the rise of converter-interfaced renewable generation and the consequent shift ...

According to physicalism, everything is physical or metaphysically connected to the physical. If physicalism were true, it seems that we should - in principle - be able to reduce the descriptions and explanations of special sciences to physical ones, for e ...

Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

Chattez avec Graph Search

Interpreting chemisorption strength with AutoML-based feature deletion experiments

Hybrid ground-state quantum algorithms based on neural Schrödinger forging

Data-Intensive Exploration of the Photoelectrochemical Responses of Main-Group Metal Sulfides

Kapitza Stabilization of Quantum Critical Order

Graph generative deep learning models with an application to circuit topologies

Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

Scalable Quantum Algorithms for Noisy Quantum Computers

Quantum Protocols for ML, Physics, and Finance

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Understanding generalization and robustness in modern deep learning

Inhalation of Microplastics—A Toxicological Complexity

Efficient Continual Finite-Sum Minimization

A Comparative Analysis of Tools & Task Types for Measuring Computational Problem-Solving

Electron Glass Phase with Resilient Zhang-Rice Singlets in LiCu3O3

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

On-Chip Fully Reconfigurable Artificial Neural Network in 16 nm FinFET for Positron Emission Tomography

Reliable data-driven decision-making through optimal transport

Random matrix methods for high-dimensional machine learning models

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

Why computational complexity may set impenetrable barriers for epistemic reductionism