June 2025 - Qtcg

Fwd: [Materia] 18th Granada Seminar Call for Participation Reminder
by Daniel Manzano 12 Jun '25

12 Jun '25

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Fwd: [qipc.spain] TalentQ Seminar | Carmen G. Almudever | June 17th @4pm
by Daniel Manzano 11 Jun '25

11 Jun '25

Sent from my iPhone Begin forwarded message: > From: TalentQ <javier.bouzas.arufe(a)usc.es> > Date: 11 June 2025 at 12:02:18 CEST > To: qipc.spain(a)listas.csic.es > Subject: [qipc.spain] TalentQ Seminar | Carmen G. Almudever | June 17th @4pm > Reply-To: TalentQ <javier.bouzas.arufe(a)usc.es> > > Speaker: Carmen G. Almudever, Associate Professor at UPV (Spain) > > Title: Toward Scalable Quantum Computers: Transitioning from Monolithic to > Modular Multicore Architectures > > Abstract: Current monolithic quantum computers already integrate tens to > hundreds of physical, error-prone qubits. A central challenge is scaling these > systems to thousands or even millions of qubits to enable the implementation > of fault-tolerant error correction protocols, which are essential to unlocking > their full computational power. Modular multicore quantum computing > architectures have emerged as a promising scalable paradigm. In this talk, we > will begin by revisiting the concept of the quantum full-stack, with a > particular emphasis on the software layers and core design principles. We will > then focus on scalable modular architectures across different quantum hardware > platforms, including trapped ions and superconducting qubits. We will discuss > the major challenges associated with transitioning from monolithic to > multicore architectures, particularly in the context of distributed quantum > computations. > > Registration: > https://urldefense.com/v3/__https://events.teams.microsoft.com/event/2f827e…

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SEMINAR: An unsupervised deep anomaly detection model combined with extreme value theory: Detecting sudden stratospheric warming events---Khulan Myanmar (Uni. Mongolia)
by Michalis Skotiniotis 09 Jun '25

09 Jun '25

El grupo de Termodinámica y Computación Cuántica les invita al siguiente seminario Fecha: Viernes 13, Junio Hora: 12:00 am Lugar: Laboratorio de Fïsica Computacional, Dept de Electromagnetismo y Física de al Materia, Planta Baja, Facultad de Ciencias (junto al péndulo gigante) Ponente: Khulan Myanmar (University of Mongolia) Titulo: An unsupervised deep anomaly detection model combined with extreme value theory: Detecting sudden stratospheric warming events. Resumen: Sudden stratospheric warming (SSW) events represent intense meteorological phenomena characterized by rapid and significant temperature increases within the polar stratosphere. These events can have a considerable impact on extreme weather conditions in mid-latitude countries. However, research on the estimation and detection of SSW remains limited. This study aims to develop a novel method for detecting SSW events using unsupervised deep anomaly detection algorithms. By leveraging unsupervised learning, patterns and structures in data can be rec- ognized, facilitating the development of predictive models without prelabelled datasets. The long short-term memory-autoencoder (LSTM-AE) with correlation weighting function model were used for the predicting and extreme value distributions (EVD) were applied for detecting anomaly. National Centres for Environmental Prediction (NCEP) and the National Centre for Atmospheric Research (NCAR) reanalysis1 temperature and zonal wind data were used for model training, whereas ERA5 reanalysis data were used for validation. This study focuses on the Northern Hemisphere (60°N-90°N) at the 10 hPa pressure level from 1979–2023. The results of this study indicate that the LSTM-AE-peaks over threshold (LSTM-AE-POT) hybrid model with dynamic correlation weighting loss function is the most effective for accurately predicting separate and joint fluctuations to detect the SSW events. Performance evaluation is conducted using confusion matrix-based metrics and temporal consistency metrics. The results demonstrate that the LSTM-AE-POT hybrid model identifies long-term, persistent anomalies with high accuracy and consistency. Additionally, the results of the validation confirm the model’s stability and smooth detection performance for SSW events. El seminario es accesible para los estudiantes de grado, a los que recomendamos encarecidamente que asistan. Nos vemos a todos allí DJM ________________________________________________________________________________ Dear all The Quantum Thermodynamics and Computation group cordially invites you to the following seminar Date: Friday 13 June Time: 12:00 am Location: Computational Physics Laboratory, Department of Electromagnetism and Condensed Mater, Ground Floor, Faculty of Science (next to the giant pendulum) Speaker: Khulan Myanmar (University of Mongolia) Title: An unsupervised deep anomaly detection model combined with extreme value theory: Detecting sudden stratospheric warming events. Abstract: Sudden stratospheric warming (SSW) events represent intense meteorological phenomena characterized by rapid and significant temperature increases within the polar stratosphere. These events can have a considerable impact on extreme weather conditions in mid-latitude countries. However, research on the estimation and detection of SSW remains limited. This study aims to develop a novel method for detecting SSW events using unsupervised deep anomaly detection algorithms. By leveraging unsupervised learning, patterns and structures in data can be rec- ognized, facilitating the development of predictive models without prelabelled datasets. The long short-term memory-autoencoder (LSTM-AE) with correlation weighting function model were used for the predicting and extreme value distributions (EVD) were applied for detecting anomaly. National Centres for Environmental Prediction (NCEP) and the National Centre for Atmospheric Research (NCAR) reanalysis1 temperature and zonal wind data were used for model training, whereas ERA5 reanalysis data were used for validation. This study focuses on the Northern Hemisphere (60°N-90°N) at the 10 hPa pressure level from 1979–2023. The results of this study indicate that the LSTM-AE-peaks over threshold (LSTM-AE-POT) hybrid model with dynamic correlation weighting loss function is the most effective for accurately predicting separate and joint fluctuations to detect the SSW events. Performance evaluation is conducted using confusion matrix-based metrics and temporal consistency metrics. The results demonstrate that the LSTM-AE-POT hybrid model identifies long-term, persistent anomalies with high accuracy and consistency. Additionally, the results of the validation confirm the model’s stability and smooth detection performance for SSW events. The seminar is accessible to undergraduate students, whom we strongly encourage to attend. See you all there DJM.

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Fiestas locales 19-20 junio
by Juani Bermejo Vega 09 Jun '25

09 Jun '25

Hola a todos, Next week Thursday and Friday we have local holidays and my kid's school closes. If we need to have any meeting those days, it would be good to know it as soon as possible :) Best, Juani

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Fwd: FTAE events next week
by Daniel Manzano 06 Jun '25

06 Jun '25

Sent from my iPhone Begin forwarded message: > From: FTAE - High Energy Theory <ftae(a)ugr.es> > Date: 6 June 2025 at 12:00:19 CEST > Subject: FTAE events next week > > > Dear all, > > This is an announcement of next week's events: > > > Seminars: > > Date: 09/06/2025 11:00 > Speaker: Carlos García Canal (U. La Plata) > Title: Do you remember the brachistochrone? > Abstract: After recalling the classical problem of the brachistochrone, the problem of the quantum brachistochrone is introduced. By means of the decomplexification procedure the mathematical equivalence between both problems is analyzed. The time of passage for the quantum cases corresponding to Hermitian and PT-symmetric Hamiltonians is computed. The equivalence between classical and quantum dynamics allows one to connect the time of passage of the brachistochrone with measurements in electric resonant circuits coupled by a gyrator. Reflexion about the mentioned equivalence are included. > > Date: 10/06/2025 12:00 > Speaker: Kuldeep Deka (New York U., Abu Dha) > Title: Dark Matter Genesis in the Reheating Era > Abstract: The post-inflationary reheating era plays a pivotal role in shaping the thermal history of the Universe, yet its dynamics remain poorly understood. In this talk, I will examine how various reheating scenarios impact dark matter (DM) production, encompassing both thermal and nonthermal origins. Using general parametrizations for the Hubble expansion rate and the evolution of the Standard Model temperature, I will present a unified framework to study DM genesis—including freeze-out mechanisms (WIMPs, SIMPs, ELDERs, Cannibals) and freeze-in processes (both infrared and ultraviolet regimes). These dynamics can significantly alter the viable DM parameter space, enabling much heavier thermal DM candidates and extending the reach for light feebly interacting particles. I will highlight the interplay between theoretical predictions and experimental constraints, emphasizing how future searches could uncover distinctive imprints of DM production during reheating. > > Date: 13/06/2025 11:00 > Speaker: Andrija Rasovic (Toronto U.) > Title: RG Improvement of the Scalar Effective Potential in Finite Temperature Quantum Field Theory > Abstract: Stochastic gravitational wave (GW) backgrounds from first-order phase transitions are a compelling target for next-generation GW observatories, offering a novel probe of dark sectors with strong phase transitions. However, reliable theoretical predictions for the GW signal strength remain challenging, particularly beyond the high-temperature regime where standard techniques like dimensional reduction become unreliable. In this talk, we present the Optimized Partial Dressing (OPD) framework, a thermal mass resummation method that uses gap equation solutions inserted into the tadpole of the potential to systematically improve perturbative calculations of the finite-temperature effective potential without relying on high-temperature expansion. We will review how OPD controls perturbation theory at finite temperature and then introduce a self-consistent renormalization group (RG) improvement of the scalar potential within the OPD formalism. This RG improvement substantially reduces the scale dependence of physical quantities and improves the robustness of predictions for gravitational wave signals from cosmological phase transitions. > > > See you all there.

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Fwd: [dasci] [Dptoccia] 7th School on Belief Functions and their Applications. Call for Participation and Financial Support
by Michalis Skotiniotis 06 Jun '25

06 Jun '25

Interesting school on decision theory with belief functions M. > Begin forwarded message: > > From: FRANCISCO HERRERA TRIGUERO <herrera(a)decsai.ugr.es> > Subject: [dasci] Fwd: [Dptoccia] 7th School on Belief Functions and their Applications. Call for Participation and Financial Support > Date: 6 June 2025 at 11:23:19 CEST > To: "dasci(a)listas.ugr.es" <dasci(a)listas.ugr.es> > > > > > > -------- Mensaje reenviado -------- > Asunto: [Dptoccia] 7th School on Belief Functions and their Applications. Call for Participation and Financial Support > Fecha: Fri, 6 Jun 2025 10:54:10 +0200 > De: Serafin Moral <smc(a)ugr.es> <mailto:smc@ugr.es> > Para: dptoccia(a)decsai.ugr.es <mailto:dptoccia@decsai.ugr.es> > > Por si es de interés para alguno, esta escuela se celebrará en Octubre en el Carmen de la Victoria. > > Seafin > > #################################################### > BFTA 2025 - Call for Participation > > The 7th School on Belief Functions and their Applications > University of Granada, Granada, Spain > October 19 — October 23, 2025 > > <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fbfasociet…>https://bfasociety.org/BFTA2025/index.html > ################################################### > > The BELIEF school is a biennial event organized by the Belief Functions and Applications Society (BFAS) that offers a unique opportunity for students and researchers to learn about fundamental and advanced aspects of the theory of belief functions (also referred to as Dempster-Shafer theory, or evidence theory), a formalism for reasoning with uncertainty. > > The school will be organized around a set of lectures by prominent researchers. Lectures will gradually tackle basic to more advanced theoretical concepts. They will also highlight the links with other uncertainty theories such as random sets and possibility theory, and present applications of belief functions in various domains including machine learning, information fusion, statistical inference and materials science. > > Student early registration (which include the tuition fees, lunches, coffee breaks and the social event): 200€ > > Financial support: > > Financial support will be offered by BFAS and the journal of Artificial Intelligence to some students for attending the school. Grants will cover registration fees. An additional amount may also be granted to cover part of the travel or accommodation, depending on individual circumstances. > > ISIF members can ask for a 50% discount on the registration fee. > > Lectures for this edition available at: > > https://bfasociety.org/BFTA2025/index.html#programme > > > Important dates: > > • April 15, 2025: Registration opens > • July 15, 2025: Deadline for early-bird registration > • July 5, 2025: Deadline for BFAS and AIJ grants application > • September 15, 2025: Deadline for registration > > For further information, contact: bfta2025(a)bfasociety.org <mailto:bfta2025@bfasociety.org> > With the support of: > > Belief Functions and Applications Society (BFAS) > University of Granada > International Society on Information Fusion (ISIF) > Journal of Artificial Intelligence (Elsevier) > > > > > > <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_cam…> Libre de virus.www.avg.com <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_cam…> <x-msg://38/#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>

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Fwd: [dasci] Curso de Ángelo Porrello entre el 9 y el 12 de Junio
by Daniel Manzano 05 Jun '25

05 Jun '25

Sent from my iPhone Begin forwarded message: > From: Daniel Molina Cabrera <dmolina(a)decsai.ugr.es> > Date: 4 June 2025 at 21:17:03 CEST > To: dasci(a)listas.ugr.es > Cc: Rosana Montes <rosana(a)ugr.es> > Subject: [dasci] Curso de Ángelo Porrello entre el 9 y el 12 de Junio > > > Buenos días a todos y todas, > > Escribo porque, fruto de nuestra colaboración con la Universidad de Módena, la semana que viene contaremos con el estupendo investigador Ángelo Porrello, Google Schoolar: https://scholar.google.com/citations?user=pGNOOk0AAAAJ, especialista en Inteligencia Artificial y Visión por Ordenador. > > Dará un estupendo e interesantísimo curso la semana que viene. El curso se divide en tres días, el 9, 10 y 12 de Junio, de 9:30 a 13:00 (con pausa para el café). > > Day 1: Monday 9 June 2025 9.30-13h > > Parameter-Efficient Fine-Tuning (2h in total): techniques to adapt large models with minimal parameter updates. > > Continual Learning (1h): methods to mitigate catastrophic forgetting and enable lifelong learning. > > Day 2: Tuesday 10 June 2025 9.30-13h > > Continuation of Continual learning (1h) > > Federated Learning and Model Merging (2h): decentralized training and aggregation strategies. > > Day 3: Thursday 12 June 2025 9.30-13:30h > > Mammoth Framework (2h): introduction to an open-source benchmarking library for continual learning. > > Hands-on with Mammoth (2h): practical session with code examples and usage scenarios. > > The sessions include a coffee break of 30-40min around 11am/Las sesiones incluyen una pausa para el café de 30-40 minutos en torno a las 11.00 horas. > > Se realizará presencialmente en la sala de "Pioneras Informáticas" en el Edificio UGR-AI en el PTS. También se podrá asistir de forma online en https://meet.google.com/tnd-xvxr-mrh > > Inscripción: Hasta el 6 de Junio a las 18:00. > > Para inscribiros, solo tenéis que rellenar el siguiente formulario: https://forms.gle/NKk6s4Ufsfj3AfKd7 > > ¡¡ os esperamos !! > > Esta formación es parte del Proyecto “Inteligencia Artificial Ética, Responsable y de Propósito General: Aplicaciones En Escenarios De Riesgo. (IAFER) Exp.: TSI-100927-2023-1 financiado a través de la Creación de cátedras universidad-empresa (Cátedras Enia), destinadas a la investigación y desarrollo de la inteligencia artificial, para su difusión y la formación en el marco del Plan de Recuperación, Transformación y Resiliencia Europeo, financiado por la Unión Europea-Next Generation EU. > > Responsable de datos: Universidad de Granada y el Instituto Interuniversitario de Ciencia de Datos e Inteligencia Computacional (DaSCI). La Universidad de Granada se encuentra legitimada para el tratamiento de sus datos personales por ser necesario para el cumplimiento de una misión realizada en interés público o en el ejercicio de los poderes públicos conferidos al responsable del mismo: art. 6.1 e) RGPD. La finalidad del tratamiento es gestionar su solicitud de inscripción. Destinatarios: Universidad de Granada, para inscripción en la plataforma virtual. Derechos: Tiene derecho a solicitar el acceso, oposición, rectificación, supresión, limitación o portabilidad del tratamiento de sus datos, tal y como se explica en la información adicional.

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Noticia UGR
by Daniel Manzano 02 Jun '25

02 Jun '25

Os dejo una noticia que protagoniza nuestra querida universidad. https://www.granadahoy.com/granada/profesores-universidad-granada-reclaman-… Profesores de la Universidad de Granada reclaman el pago de complementos por méritos docentes e investigadores logrados antes de ser funcionarios granadahoy.com --------------------------------------------------------------------------------- Daniel Manzano Quantum Thermodynamics and Quantum Computation Group University of Granada Facultad de Ciencias, Av. Fuentenueva s/n Granada 18071, Spain Phone: +34 958241000 Ext: 20569 https://ic1.ugr.es/members/dmanzano/

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