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There is a mismatch between psychological and computational studies on emotions. Psychological research aims at explaining and documenting internal mechanisms of these phenomena, while computational work often simplifies them into labels. Many emotion fundamentals remain under-explored in natural language processing, particularly how emotions develop and how people cope with them. To help reduce this gap, we follow theories on coping, and treat emotions as strategies to cope with salient situations (i.e., how people deal with emotion-eliciting events). This approach allows us to investigate the link between emotions and behavior, which also emerges in language. We introduce the task of coping identification, together with a corpus to do so, constructed via role-playing. We find that coping strategies realize in text even though they are challenging to recognize, both for humans and automatic systems trained and prompted on the same task. We thus open up a promising research direction to enhance the capability of models to better capture emotion mechanisms from text.

The Continuous-Variable Firing Rate (CVFR) model, widely used in neuroscience to describe the intertangled dynamics of excitatory biological neurons, is here trained and tested as a veritable dynamically assisted classifier. To this end the model is supplied with a set of planted attractors which are self-consistently embedded in the inter-nodes coupling matrix, via its spectral decomposition. Learning to classify amounts to sculp the basin of attraction of the imposed equilibria, directing different items towards the corresponding destination target, which reflects the class of respective pertinence. A stochastic variant of the CVFR model is also studied and found to be robust to aversarial random attacks, which corrupt the items to be classified. This remarkable finding is one of the very many surprising effects which arise when noise and dynamical attributes are made to mutually resonate.

Fourier Neural Operators (FNOs) excel on tasks using functional data, such as those originating from partial differential equations. Such characteristics render them an effective approach for simulating the time evolution of quantum wavefunctions, which is a computationally challenging, yet coveted task for understanding quantum systems. In this manuscript, we use FNOs to model the evolution of random quantum spin systems, so chosen due to their representative quantum dynamics and minimal symmetry. We explore two distinct FNO architectures and examine their performance for learning and predicting time evolution using both random and low-energy input states. Additionally, we apply FNOs to a compact set of Hamiltonian observables ($\sim\text{poly}(n)$) instead of the entire $2^n$ quantum wavefunction, which greatly reduces the size of our inputs and outputs and, consequently, the requisite dimensions of the resulting FNOs. Moreover, this Hamiltonian observable-based method demonstrates that FNOs can effectively distill information from high-dimensional spaces into lower-dimensional spaces. The extrapolation of Hamiltonian observables to times later than those used in training is of particular interest, as this stands to fundamentally increase the simulatability of quantum systems past both the coherence times of contemporary quantum architectures and the circuit-depths of tractable tensor networks., Comment: 9 pages, 4 figures

Quantum computation shows promise for addressing numerous classically intractable problems, such as optimization tasks. Many optimization problems are NP-hard, meaning that they scale exponentially with problem size and thus cannot be addressed at scale by traditional computing paradigms. The recently proposed quantum algorithm arXiv:2206.14999 addresses this challenge for some NP-hard problems, and is based on classical semidefinite programming (SDP). In this manuscript, we generalize the SDP-inspired quantum algorithm to sum-of-squares programming, which targets a broader problem set. Our proposed algorithm addresses degree-$k$ polynomial optimization problems with $N \leq 2^n$ variables (which are representative of many NP-hard problems) using $O(nk)$ qubits, $O(k)$ quantum measurements, and $O(\textrm{poly}(n))$ classical calculations. We apply the proposed algorithm to the prototypical Max-$k$SAT problem and compare its performance against classical sum-of-squares, state-of-the-art heuristic solvers, and random guessing. Simulations show that the performance of our algorithm surpasses that of classical sum-of-squares after rounding. Our results further demonstrate that our algorithm is suitable for large problems and approximates the best known classical heuristics, while also providing a more generalizable approach compared to problem-specific heuristics., Comment: 15 pages, 4 figures

The Washington Office of Superintendent of Public Instruction (OSPI) annually submits a report outlining the method used in determining the coefficients for each site characteristic used to establish the transportation allocation for the following year. The Student Transportation Allocation Reporting System (STARS) will determine the 2024-25 school year allocation. The 2024-25 regression analysis will use data elements from spring 2024, fall 2024, and winter 2025 reporting periods.

The Wilson-Cowan model for metapopulation, a Neural Mass Network Model, treats different subcortical regions of the brain as connected nodes, with connections representing various types of structural, functional, or effective neuronal connectivity between these regions. Each region comprises interacting populations of excitatory and inhibitory cells, consistent with the standard Wilson-Cowan model. By incorporating stable attractors into such a metapopulation model's dynamics, we transform it into a learning algorithm capable of achieving high image and text classification accuracy. We test it on MNIST and Fashion MNIST, in combination with convolutional neural networks, on CIFAR-10 and TF-FLOWERS, and, in combination with a transformer architecture (BERT), on IMDB, always showing high classification accuracy. These numerical evaluations illustrate that minimal modifications to the Wilson-Cowan model for metapopulation can reveal unique and previously unobserved dynamics.

Modular, distributed and multi-core architectures are currently considered a promising approach for scalability of quantum computing systems. The integration of multiple Quantum Processing Units necessitates classical and quantum-coherent communication, introducing challenges related to noise and quantum decoherence in quantum state transfers between cores. Optimizing communication becomes imperative, and the compilation and mapping of quantum circuits onto physical qubits must minimize state transfers while adhering to architectural constraints. The compilation process, inherently an NP-hard problem, demands extensive search times even with a small number of qubits to be solved to optimality. To address this challenge efficiently, we advocate for the utilization of heuristic mappers that can rapidly generate solutions. In this work, we propose a novel approach employing Deep Reinforcement Learning (DRL) methods to learn these heuristics for a specific multi-core architecture. Our DRL agent incorporates a Transformer encoder and Graph Neural Networks. It encodes quantum circuits using self-attention mechanisms and produce outputs through an attention-based pointer mechanism that directly signifies the probability of matching logical qubits with physical cores. This enables the selection of optimal cores for logical qubits efficiently. Experimental evaluations show that the proposed method can outperform baseline approaches in terms of reducing inter-core communications and minimizing online time-to-solution. This research contributes to the advancement of scalable quantum computing systems by introducing a novel learning-based heuristic approach for efficient quantum circuit compilation and mapping.

Multi-core quantum architectures offer a solution to the scalability limitations of traditional monolithic designs. However, dividing the system into multiple chips introduces a critical bottleneck: communication between cores. This paper introduces qcomm, a simulation tool designed to assess the impact of communication on the performance of scalable multi-core quantum architectures. Qcomm allows users to adjust various architectural and physical parameters of the system, and outputs various communication metrics. We use qcomm to perform a preliminary study on how these parameters affect communication performance in a multi-core quantum system.

Neurodevelopmental disorders (NDD), encompassing conditions like Intellectual Disability, Attention Deficit Hyperactivity Disorder, and Autism Spectrum Disorder, present challenges across various cognitive capacities. Attention deficits are often common in individuals with NDD due to the sensory system dysfunction that characterizes these disorders. Consequently, limited attention capability can affect the overall quality of life and the ability to transfer knowledge from one circumstance to another. The literature has increasingly recognized the potential benefits of virtual reality (VR) in supporting NDD learning and rehabilitation due to its interactive and engaging nature, which is critical for consistent practice. In previous studies, we explored the usage of a VR application called Wildcard to enhance attention skills in persons with NDD. The application has been redesigned in this study, exploiting eye-tracking technology to enable novel and more fine-grade interactions. A four-week experiment with 38 NDD participants was conducted to evaluate its usability and effectiveness in improving Visual Attention Skills. Results show the usability and effectiveness of Wildcard in enhancing attention skills, advocating for continued exploration of VR and eye-tracking technology's potential in NDD interventions., Comment: 15 pages, 6 figures, conference HCII24

Currently, there is a growing trend of outsourcing the execution of DNNs to cloud services. For service providers, managing multi-tenancy and ensuring high-quality service delivery, particularly in meeting stringent execution time constraints, assumes paramount importance, all while endeavoring to maintain cost-effectiveness. In this context, the utilization of heterogeneous multi-accelerator systems becomes increasingly relevant. This paper presents RELMAS, a low-overhead deep reinforcement learning algorithm designed for the online scheduling of DNNs in multi-tenant environments, taking into account the dataflow heterogeneity of accelerators and memory bandwidths contentions. By doing so, service providers can employ the most efficient scheduling policy for user requests, optimizing Service-Level-Agreement (SLA) satisfaction rates and enhancing hardware utilization. The application of RELMAS to a heterogeneous multi-accelerator system composed of various instances of Simba and Eyeriss sub-accelerators resulted in up to a 173% improvement in SLA satisfaction rate compared to state-of-the-art scheduling techniques across different workload scenarios, with less than a 1.5% energy overhead.

This paper presents the design of an 800 V 11 kVA three-level three-phase active neutral point clamped inverter, utilizing 650 V gallium nitride enhancement-mode high-electron-mobility transistors, to evaluate its feasibility in electric traction systems. The modular approach of the presented power converter design is detailed discussed and the different printed circuit boards composing the power converter are presented, together with critical design issues. The paper includes the gate driver design, as well as the thermal analysis and parasitics extraction using ANSYS Q3D. Extractor., Comment: 6 pages, 9 figures, 15th International Conference ELEKTRO 2024. This work has been carried out in the framework of the ECSEL-JU Project GaN4AP (Gallium Nitride for Advanced Power Applications) - Grant Agreement No.101007310

This paper addresses the critical challenge of managing Quality of Service (QoS) in cloud services, focusing on the nuances of individual tenant expectations and varying Service Level Indicators (SLIs). It introduces a novel approach utilizing Deep Reinforcement Learning for tenant-specific QoS management in multi-tenant, multi-accelerator cloud environments. The chosen SLI, deadline hit rate, allows clients to tailor QoS for each service request. A novel online scheduling algorithm for Deep Neural Networks in multi-accelerator systems is proposed, with a focus on guaranteeing tenant-wise, model-specific QoS levels while considering real-time constraints.

We introduce a variational quantum solver for combinatorial optimizations over $m=\mathcal{O}(n^k)$ binary variables using only $n$ qubits, with tunable $k>1$. The number of parameters and circuit depth display mild linear and sublinear scalings in $m$, respectively. Moreover, we analytically prove that the specific qubit-efficient encoding brings in a super-polynomial mitigation of barren plateaus as a built-in feature. This leads to unprecedented quantum-solver performances. For $m=7000$, numerical simulations produce solutions competitive in quality with state-of-the-art classical solvers. In turn, for $m=2000$, an experiment with $n=17$ trapped-ion qubits featured MaxCut approximation ratios estimated to be beyond the hardness threshold $0.941$. To our knowledge, this is the highest quality attained experimentally on such sizes. Our findings offer a novel heuristics for quantum-inspired solvers as well as a promising route towards solving commercially-relevant problems on near term quantum devices.

This study explores a VR-based intervention for Visuospatial neglect (VSN), a post-stroke condition. It aims to develop a VR task utilizing interactive visual-audio cues to improve sensory-motor training and assess its impact on VSN patients' engagement and performance. Collaboratively designed with physiotherapists, the VR task uses directional and auditory stimuli to alert and direct patients, tested over 12 sessions with two individuals. Results show a consistent decrease in task completion variability and positive patient feedback, highlighting the VR task's potential for enhancing engagement and suggesting its feasibility in rehabilitation. The study underlines the significance of collaborative design in healthcare technology and advocates for further research with a larger sample size to confirm the benefits of VR in VSN treatment, as well as its applicability to other multimodal disorders., Comment: 29 pages, 8 figures, 5 tables

Divergences that occur in density matrices of decay and scattering processes are shown to be regularized by tracing and unitarity or the optical theorem. These divergences are regularized by the lifetime of the decaying particle or the total scattering cross section. Also, this regularization is shown to give the expected helicities of final particles. The density matrix is derived for the weak decay of a polarized muon at rest, $\mu^- \rightarrow \nu_{\mu} (e^- \bar \nu_e)$, with Lorentz invariant density matrix entries and unitarity upheld at tree level. The electron's von Neumann entanglement entropy distributions are calculated with respect to both the electron's emission angle and energy. The angular entropy distribution favors an electron emitted backwards with respect to the muon's polarization given a minimum volume regularization. The kinematic entropy distribution is maximal at half the muon's rest mass energy. These results are similar to the electron's angular and kinematic decay rate distributions. Both the density matrix and entanglement entropy can be cast either in terms of ratios of areas or volumes.