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Search for a boosted Higgs boson decaying to bottom quark pairs in association with a W or Z boson in proton-proton collisions at √s=13 TeV
Item type: Journal Article
The CMS Collaboration; Hayrapetyan, Aram; Makarenko, Vladimir; et al. (2026)
A search is conducted for standard model Higgs bosons with large transverse momentum (p(T)) decaying to bottom quark pairs and produced in association with a hadronically decaying W or Z boson at the LHC. The result is based on a dataset of proton-proton collisions at a center-of-mass energy of 13 TeVcollected with the CMS detector in 2016-2018, corresponding to an integrated luminosity of 138 fb(-1). Boosted Higgs, W, and Z boson decays are reconstructed using large-radius jets with p(T) > 450 GeV and identified with heavy-flavor classifiers based on a graph convolutional neural network. The observed signal strength relative to the standard model expectation is mu = 0.72(-0.71)(+0.75) including statistical and systematic uncertainties.
Estudio de impacto de CES: Costa Rica
Item type: Report
Burke Theiler, Ariea Anne; McDonald, Patrick; Renold, Ursula (2026)
Parametrically amplified Josephson plasma waves inYBa2Cu3O6+x: Evidence for local superconductingfluctuations up to the pseudogap temperature T∗
Item type: Journal Article
Michael, Marios H.; Demler, Eugene; Lee, Patrick A. (2026)
Experiments that subject underdoped YBa2Cu3O6+x (YBCO) to intense terahertz pulses at temperatures between the transition temperature T-c, and the pseudogap scale T* have revealed a reflectivity edge that resembles that of the superconducting state, together with second harmonic generation of a probe pulse modulated at a similar frequency. These have been interpreted in terms of parametric amplification of the lower Josephson plasmon mode. Since this mode is often associated with coherent oscillations between bilayers in the YBCO structure, these experiments have led to the suggestion that the intense pump has created (or revealed) in-plane pair coherence up to T * approximate to 400 K. In this paper we propose an alternative explanation by assuming the existence of local pair amplitude and phase at equilibrium for T, < T < T * . The phase correlation spans only a few lattice constants and we do not assume any pump-induced enhancement of this correlation, either in-plane or between bilayers. Instead, the coherent drive, via a parametric amplification process, induces coherence in the Josephson currents between members of bilayers. When combined with a Floquet framework, the reflectivity data can be explained. The key point is that in the lower Josephson plasmon, the coupling between bilayers is mainly capacitive; the Josephson current between bilayers can be set to zero without strongly affecting the parametric amplification process. Importantly, while superconducting coherence may not be created by the pump, the pseudogap phase must possess a local pairing amplitude at equilibrium. Consequently, these experiments have strong implications for the understanding of the pseudogap phase.
PIMDAL: Mitigating the Memory Bottleneck in Data Analytics using a Real Processing-in-Memory System
Item type: Working Paper
Frouzakis, Manos; Gómez Luna, Juan; Oliveira, Geraldo F.; et al. (2025)
Database Management Systems (DBMSs) are crucial for efficient data management and analytics, and are used in several different application domains. Due to the increasing volume of data a DBMS deals with, current processor-centric architectures (e.g., CPUs, GPUs) suffer from data movement bottlenecks when executing key DBMS operations (e.g., selection, aggregation, ordering, and join). This happens mostly due to the limited memory bandwidth between compute and memory resources. Data-centric architectures like Processing-in-Memory (PIM) are a promising alternative for applications bottlenecked by data, placing compute resources close to where data resides. Previous works have evaluated using PIM for data analytics. However, they either do not use real-world architectures or they consider only a subset of the operators used in analytical queries. This work aims to fully evaluate a data-centric approach to data analytics, by using the real-world UPMEM PIM system. To this end we first present the PIM Data Analytics Library (PIMDAL), which implements four major DB operators: selection, aggregation, ordering and join. Second, we use hardware performance metrics to understand which properties of a PIM system are important for a high-performance implementation. Third, we compare PIMDAL to reference implementations on high-end CPU and GPU systems. Fourth, we use PIMDAL to implement five TPC-H queries to gain insights into analytical queries. We analyze and show how to overcome the three main limitations of the UPMEM system when implementing DB operators: (I) low arithmetic performance, (II) explicit memory management and (III) limited communication between compute units. Our evaluation shows PIMDAL achieves 3.9x the performance of a high-end CPU, on average across the five TPC-H queries.
TinyMyo: a Tiny Foundation Model for Flexible EMG Signal Processing at the Edge
Item type: Working Paper
Fasulo, Matteo; Spacone, Giusy; Ingolfsson, Thorir; et al. (2025)
Objective: Surface electromyography (EMG) is a non-invasive sensing modality widely used in biomechanics, rehabilitation, prosthetic control, and human-machine interfaces. Despite decades of use, achieving robust generalization across subjects, recording systems, and acquisition protocols remains challenging. While foundation models (FMs) are gaining traction for EMG, existing approaches remain limited to single downstream tasks and lack deployability on embedded platforms. This work addresses these limitations. Methods: We present TinyMyo, a lightweight FM based on a Transformer encoder architecture. The model is pre-trained in a self-supervised manner using masked reconstruction on publicly available datasets. With only 3.6M parameters, TinyMyo is designed to support multiple downstream tasks through minimal task-specific head adaptations. Results: We demonstrate generalization across hand gesture classification, hand kinematic regression, speech production and speech recognition, with performance comparable to or surpassing the state of the art (SoA), and model size below 5M parameters. We achieve SoA results compared to previous FM-based works on the NinaPro DB5 (89.4%), UCI-EMG (97.56%), and EPN-612 (96.74%) datasets. We demonstrate the first-time deployment of an EMG FM on an ultra-low power microcontroller (GAP9), with an inference time of 0.785 s, energy of 44.91 mJ and power envelope of 57.18 mW. Conclusion: TinyMyo demonstrates that compact, self-supervised EMG FM can guarantee strong generalization across multiple downstream tasks while remaining compatible with low-power edge devices. Significance: TinyMyo is the first EMG FM for ultra-low power edge devices, enabling scalable and energy-efficient sensing for motor intent decoding, neuromuscular assessment, and biosignal driven human-machine interaction.