The Dark Dimension and the Standard Model Landscape

We study the landscape of lower-dimensional vacua of the SM coupled to gravity in the presence of the ``dark dimension'' of size $R_\perp$ in the micron range, focusing on the validity of the swampland conjecture forbidding the presence of non-SUSY AdS vacua in a consistent quantum gravity theory. We first adopt the working assumption that right-handed neutrinos propagate in the bulk, so that neutrino Yukawa couplings become tiny due to a volume suppression, leading to naturally light Dirac neutrinos. We show that the neutrino KK towers compensate for the graviton tower to maintain stable dS vacua found in the past, but neutrino oscillation data set restrictive bounds on $R_\perp$ and therefore the first KK neutrino mode is too heavy to alter the shape of the radon potential or the required maximum mass for the lightest neutrino to carry dS rather than AdS vacua found in the absence of the dark dimension, $m_{1,{\rm max}}\lesssim 7.63~{\rm meV}$. We also show that a very light gravitino (with mass in the meV range) could help relax the neutrino mass constraint $m_{1,{\rm max}} \lesssim 50~{\rm meV}$. The differences for the predicted total neutrino mass $\sum m_\nu$ among these two scenarios are within reach of next-generation cosmological probes that may measure the total neutrino mass with an uncertainty $\sigma (\sum m_\nu) = 0.014~{\rm eV}$. We also demonstrate that the KK tower of a very light gravitino can compensate for the graviton tower to sustain stable dS vacua and thus right-handed neutrinos can (in principle) be locked on the brane. For this scenario, Majorana neutrinos could develop dS vacua, which is not possible in the SM coupled to gravity. Finally, we investigate the effects of bulk neutrino masses in suppressing oscillations of the 0-modes into the first KK modes to relax the oscillation bound on $R_\perp$.Comment: Matching version to be published in PR

Domain-Invariant Proposals based on a Balanced Domain Classifier for Object Detection

Object recognition from images means to automatically find object(s) of interest and to return their category and location information. Benefiting from research on deep learning, like convolutional neural networks~(CNNs) and generative adversarial networks, the performance in this field has been improved significantly, especially when training and test data are drawn from similar distributions. However, mismatching distributions, i.e., domain shifts, lead to a significant performance drop. In this paper, we build domain-invariant detectors by learning domain classifiers via adversarial training. Based on the previous works that align image and instance level features, we mitigate the domain shift further by introducing a domain adaptation component at the region level within Faster \mbox{R-CNN}. We embed a domain classification network in the region proposal network~(RPN) using adversarial learning. The RPN can now generate accurate region proposals in different domains by effectively aligning the features between them. To mitigate the unstable convergence during the adversarial learning, we introduce a balanced domain classifier as well as a network learning rate adjustment strategy. We conduct comprehensive experiments using four standard datasets. The results demonstrate the effectiveness and robustness of our object detection approach in domain shift scenarios.Comment: fixed some issue

Tracking sustainability: co-evolution of economic and ecological activities in the industrialization of the United Kingdom and China

The co-evolution of economic and ecological activities represents one of the fundamental challenges in the realm of sustainable development. This study on the word trends in mainstream newspapers from the UK and China reveals that both early-industrialised countries and latecomers follow three modes of economic and ecological co-evolution. First, both economic and ecological words demonstrate an S-shaped growth trajectory, and the mode underscores the importance of information propagation, whilst also highlighting the crucial role of self-organisation in the accept society. Second, the co-occurrence of these two type words exhibits a Z-shaped relationship: for two-thirds of the observed period, they display synergistic interactions, while the remaining time shows trade-offs. Lastly, the words related to ecological degradation follow M-shaped trajectories in parallel with economic growth, suggesting periodic disruptions and reconstructions in their interrelationships. Our findings contribute to a more nuanced understanding of the co-evolutionary mechanisms that govern collective behaviours in human society

Overwhelmed software developers: An Interpretative Phenomenological Analysis

In this paper, we report on an Interpretive Phenomenological Analysis (IPA) study on experiencing overwhelm in a software development context. The objectives of our study are, hence, to understand the experiences developers have when being overwhelmed, how this impacts their productivity and which role stress plays in the process. To this end, we interviewed two software developers who have experienced overwhelm recently. Throughout a qualitative analysis of the shared experiences, we uncover seven categories of overwhelm (communication, disturbance, organizational, variety, technical, temporal, and positive overwhelm). While the first six themes all are related to negative outcomes, including low productivity and stress, the participants reported that overwhelm can sometimes be experienced to be positive and pleasant, and it can increase their mental focus, self ambition, and productivity. Stress was the most mentioned feeling experienced when overwhelmed. Our findings, for the most, are along the same direction of similar studies from other disciplines and with other participants. However, there may be unique attributes to software developers that mitigate the negative experiences of overwhelm.Comment: 46 pages, technical repor

Sequential Monte Carlo Graph Convolutional Network for Dynamic Brain Connectivity

An increasingly important brain function analysis modality is functional connectivity analysis which regards connections as statistical codependency between the signals of different brain regions. Graph-based analysis of brain connectivity provides a new way of exploring the association between brain functional deficits and the structural disruption related to brain disorders, but the current implementations have limited capability due to the assumptions of noise-free data and stationary graph topology. We propose a new methodology based on the particle filtering algorithm, with proven success in tracking problems, which estimates the hidden states of a dynamic graph with only partial and noisy observations, without the assumptions of stationarity on connectivity. We enrich the particle filtering state equation with a graph Neural Network called Sequential Monte Carlo Graph Convolutional Network (SMC-GCN), which due to the nonlinear regression capability, can limit spurious connections in the graph. Experiment studies demonstrate that SMC-GCN achieves the superior performance of several methods in brain disorder classification

Equivariant localization and holography

We discuss the theory of equivariant localization focussing on applications relevant for holography. We consider geometries comprising compact and non-compact toric orbifolds, as well as more general non-compact toric Calabi-Yau singularities. A key object in our constructions is the equivariant volume, for which we describe two methods of evaluation: the Berline-Vergne fixed-point formula and the Molien-Weyl formula, supplemented by the Jeffrey-Kirwan prescription. We present two applications in supersymmetric field theories. Firstly, we describe a method for integrating the anomaly polynomial of SCFTs on compact toric orbifolds. Secondly, we discuss equivariant orbifold indices that are expected to play a key role in the computation of supersymmetric partition functions. In the context of supergravity, we propose that the equivariant volume can be used to characterise universally the geometry of a large class of supersymmetric solutions. As an illustration, we employ equivariant localization to prove the factorization in gravitational blocks of various supergravity free energies, recovering previous results as well as obtaining generalizations.Comment: 69 pages, 10 figures; published version, few typos correcte

Combining Matrix Product States and Noisy Quantum Computers for Quantum Simulation

Matrix Product States (MPS) and Operators (MPO) have been proven to be a powerful tool to study quantum many-body systems but are restricted to moderately entangled states as the number of parameters scales exponentially with the entanglement entropy. While MPS can efficiently find ground states of 1D systems, their capacities are limited when simulating their dynamics, where the entanglement can increase ballistically with time. On the other hand, quantum devices appear as a natural platform to encode and perform the time evolution of correlated many-body states. However, accessing the regime of long-time dynamics is hampered by quantum noise. In this study we use the best of worlds: the short-time dynamics is efficiently performed by MPSs, compiled into short-depth quantum circuits, and is performed further in time on a quantum computer thanks to efficient MPO-optimized quantum circuits. We quantify the capacities of this hybrid classical-quantum scheme in terms of fidelities taking into account a noise model. We show that using classical knowledge in the form of tensor networks provides a way to better use limited quantum resources and lowers drastically the noise requirements to reach a practical quantum advantage. Finally we successfully demonstrate our approach with an experimental realization of the technique. Combined with efficient circuit transpilation we simulate a 10-qubit system on an actual quantum device over a longer time scale than low-bond-dimension MPSs and purely quantum Trotter evolution

Histopathology Slide Indexing and Search: Are We There Yet?

The search and retrieval of digital histopathology slides is an important task that has yet to be solved. In this case study, we investigate the clinical readiness of three state-of-the-art histopathology slide search engines, Yottixel, SISH, and RetCCL, on three patients with solid tumors. We provide a qualitative assessment of each model's performance in providing retrieval results that are reliable and useful to pathologists. We found that all three image search engines fail to produce consistently reliable results and have difficulties in capturing granular and subtle features of malignancy, limiting their diagnostic accuracy. Based on our findings, we also propose a minimal set of requirements to further advance the development of accurate and reliable histopathology image search engines for successful clinical adoption

Track Lab: extensible data acquisition software for fast pixel detectors, online analysis and automation

Fast, incremental evolution of physics instrumentation raises the question of efficient software abstraction and transferability of algorithms across similar technologies. This contribution aims to provide an answer by introducing Track Lab, a modern data acquisition program focusing on extensibility and high performance. Shipping with documented API and more than 20 standard modules, Track Lab allows complex analysis pipelines to be constructed from simple, reusable building blocks. Thanks to multi-threaded infrastructure, data can be clustered, filtered, aggregated and plotted concurrently in real-time. In addition, full hardware support for Timepix2, Timepix3 pixel detectors and embedded photomultiplier systems enables such analysis to be carried out online during data acquisition. Repetitive procedures can be automated with support for motorized stages and X-ray tubes. Freely distributed on 7 popular operating systems and 2 CPU architectures, Track Lab is a versatile tool for high energy physics research.Comment: Proceedings of the 24th International Workshop on Radiation Imaging Detectors (IWORID 2023

Parabolic frequency monotonicity on the conformal Ricci flow

This paper is devoted to the investigation of the monotonicity of parabolic frequency functional under conformal Ricci flow defined on a closed Riemannian manifold of constant scalar curvature and dimension not less than 3. Parabolic frequency functional for solutions of certain linear heat equation coupled with conformal pressure is defined and its monotonicity under the conformal Ricci flow is proved by applying Bakry-Emery Ricci curvature bounds. Some consequences of the monotonicity are also presented.Comment: 18 page