Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Holographic Dual of a Topological Symmetry Operator

We study the holographic dual of a topological symmetry operator in the context of the AdS/CFT correspondence. Symmetry operators arise from topological field theories localized on a subspace of the boundary CFT spacetime. We use bottom up considerations to construct the topological sector associated with their bulk counterparts. In particular, by exploiting the structure of entanglement wedge reconstruction we argue that the bulk counterpart has a non-topological worldvolume action, i.e., it describes a dynamical object. As a consequence, we find that there are no global $p$-form symmetries for $p \geq 0$ in asymptotically AdS spacetimes, which includes the case of non-invertible symmetries. Provided one has a suitable notion of subregion-subregion duality, our argument for the absence of bulk global symmetries applies to more general spacetimes. These considerations also motivate us to consider for general QFTs (holographic or not) the notion of lower-form symmetries, namely, $(-m)$-form symmetries for $m \geq 2$.
A Survey on Energy Consumption and Environmental Impact of Video Streaming

Climate change challenges require a notable decrease in worldwide greenhouse gas (GHG) emissions across technology sectors. Digital technologies, especially video streaming, accounting for most Internet traffic, make no exception. Video streaming demand increases with remote working, multimedia communication services (e.g., WhatsApp, Skype), video streaming content (e.g., YouTube, Netflix), video resolution (4K/8K, 50 fps/60 fps), and multi-view video, making energy consumption and environmental footprint critical. This survey contributes to a better understanding of sustainable and efficient video streaming technologies by providing insights into the state-of-the-art and potential future directions for researchers, developers, and engineers, service providers, hosting platforms, and consumers. We widen this survey's focus on content provisioning and content consumption based on the observation that continuously active network equipment underneath video streaming consumes substantial energy independent of the transmitted data type. We propose a taxonomy of factors that affect the energy consumption in video streaming, such as encoding schemes, resource requirements, storage, content retrieval, decoding, and display. We identify notable weaknesses in video streaming that require further research for improved energy efficiency: (1) fixed bitrate ladders in HTTP live streaming; (2) inefficient hardware utilization of existing video players; (3) lack of comprehensive open energy measurement dataset covering various device types and coding parameters for reproducible research.
Mixed-Integer Linear Optimization for Semi-Supervised Optimal Classification Trees

Decision trees are one of the most famous methods for solving classification problems, mainly because of their good interpretability properties. Moreover, due to advances in recent years in mixed-integer optimization, several models have been proposed to formulate the problem of computing optimal classification trees. The goal is, given a set of labeled points, to split the feature space with hyperplanes and assign a class to each partition. In certain scenarios, however, labels are exclusively accessible for a subset of the given points. Additionally, this subset may be non-representative, such as in the case of self-selection in a survey. Semi-supervised decision trees tackle the setting of labeled and unlabeled data and often contribute to enhancing the reliability of the results. Furthermore, undisclosed sources may provide extra information about the size of the classes. We propose a mixed-integer linear optimization model for computing semi-supervised optimal classification trees that cover the setting of labeled and unlabeled data points as well as the overall number of points in each class for a binary classification. Our numerical results show that our approach leads to a better accuracy and a better Matthews correlation coefficient for biased samples compared to other optimal classification trees, even if only few labeled points are available.
Biorthogonal measures, polymer partition functions, and random matrices

We develop the study of a particular class of biorthogonal measures, encompassing at the same time several random matrix models and partition functions of polymers. This general framework allows us to characterize the partition functions of the Log Gamma polymer and the mixed polymer in terms of explicit biorthogonal measures, as it was previously done for the homogeneous O'Connell-Yor polymer by Imamura and Sasamoto. In addition, we show that the biorthogonal measures associated to these three polymer models (Log Gamma, O'Connell-Yor, and mixed polymer) converge to random matrix eigenvalue distributions in small temperature limits. We also clarify the connection between different Fredholm determinant representations and explain how our results might be useful for asymptotic analysis and large deviation estimates.
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies

In recent years there has been significant progress in time series anomaly detection. However, after detecting an (perhaps tentative) anomaly, can we explain it? Such explanations would be useful to triage anomalies. For example, in an oil refinery, should we respond to an anomaly by dispatching a hydraulic engineer, or an intern to replace the battery on a sensor? There have been some parallel efforts to explain anomalies, however many proposed techniques produce explanations that are indirect, and often seem more complex than the anomaly they seek to explain. Our review of the literature/checklists/user-manuals used by frontline practitioners in various domains reveals an interesting near-universal commonality. Most practitioners discuss, explain and report anomalies in the following format: The anomaly would be like normal data A, if not for the corruption B. The reader will appreciate that is a type of counterfactual explanation. In this work we introduce a domain agnostic counterfactual explanation technique to produce explanations for time series anomalies. As we will show, our method can produce both visual and text-based explanations that are objectively correct, intuitive and in many circumstances, directly actionable.
In Memory of Martin Davis

The present paper gives an account for the general mathematical reader of the life and work of Martin Davis. Since two rather comprehensive autobiographical accounts and two long biographical interviews already exist, the present work focusses on Davis's scientific achievements, including work on computably enumerable sets, universal Turing machines, the hyperarithmetical hierarchy, neural networks, Hilbert's Tenth Problem, and automated reasoning.
Polarity switch of PMMA powder transported through a PMMA duct

During pneumatic conveying, powder electrifies rapidly due to the high flow velocities. In our experiments, the particles even charge if the conveying duct is made of the same material, which might be caused by triboelectrification between two asymmetric contact surfaces. Surprisingly, we found the airflow rate to determine the polarity of the overall powder charge. This study investigates the charging of microscale PMMA particles in turbulent flows passing through a square PMMA duct. The particles are spherical and monodisperse. A Faraday at the duct outlet measured the total charge of the particles. At low flow velocities, the particles charged negatively after passing through the duct. However, the powder’s overall charge switched to a positive polarity when increasing the flow velocity.
Blackout Mitigation via Physics-guided RL

This paper considers the sequential design of remedial control actions in response to system anomalies for the ultimate objective of preventing blackouts. A physics-guided reinforcement learning (RL) framework is designed to identify effective sequences of real-time remedial look-ahead decisions accounting for the long-term impact on the system's stability. The paper considers a space of control actions that involve both discrete-valued transmission line-switching decisions (line reconnections and removals) and continuous-valued generator adjustments. To identify an effective blackout mitigation policy, a physics-guided approach is designed that uses power-flow sensitivity factors associated with the power transmission network to guide the RL exploration during agent training. Comprehensive empirical evaluations using the open-source Grid2Op platform demonstrate the notable advantages of incorporating physical signals into RL decisions, establishing the gains of the proposed physics-guided approach compared to its black box counterparts. One important observation is that strategically~\emph{removing} transmission lines, in conjunction with multiple real-time generator adjustments, often renders effective long-term decisions that are likely to prevent or delay blackouts.
Supervised Fine-tuning in turn Improves Visual Foundation Models

Image-text training like CLIP has dominated the pretraining of vision foundation models in recent years. Subsequent efforts have been made to introduce region-level visual learning into CLIP's pretraining but face scalability challenges due to the lack of large-scale region-level datasets. Drawing inspiration from supervised fine-tuning (SFT) in natural language processing such as instruction tuning, we explore the potential of fine-grained SFT in enhancing the generation of vision foundation models after their pretraining. Thus a two-stage method ViSFT (Vision SFT) is proposed to unleash the fine-grained knowledge of vision foundation models. In ViSFT, the vision foundation model is enhanced by performing visual joint learning on some in-domain tasks and then tested on out-of-domain benchmarks. With updating using ViSFT on 8 V100 GPUs in less than 2 days, a vision transformer with over 4.4B parameters shows improvements across various out-of-domain benchmarks including vision and vision-linguistic scenarios.
Robustness Evaluation of Machine Learning Models for Robot Arm Action Recognition in Noisy Environments

In the realm of robot action recognition, identifying distinct but spatially proximate arm movements using vision systems in noisy environments poses a significant challenge. This paper studies robot arm action recognition in noisy environments using machine learning techniques. Specifically, a vision system is used to track the robot's movements followed by a deep learning model to extract the arm's key points. Through a comparative analysis of machine learning methods, the effectiveness and robustness of this model are assessed in noisy environments. A case study was conducted using the Tic-Tac-Toe game in a 3-by-3 grid environment, where the focus is to accurately identify the actions of the arms in selecting specific locations within this constrained environment. Experimental results show that our approach can achieve precise key point detection and action classification despite the addition of noise and uncertainties to the dataset.
The proton-neutron resonance states by solving Schrodinger equation

The proton-neutron interaction is investigated by solving the Schrodinger equation, where a Yukawa type of potential with one pion exchanging between the proton and the neutron is assumed. Since the deutron is the unique bound state of the proton-neutron system, the coupling constant is fixed according to the binding energy of the deutron. The scattering process of the proton and the neutron is studied when the outgoing wave condition is taken into account, and two proton-neutron resonance states are obtained by solving the Schrodinger equation, which lie at $1905-i13$MeV and $2150-i342$MeV on the complex energy plane, respectively. It is no doubt that the calculation results would give some hints on the experimental research on the proton-neutron interaction in future.
Voila-A: Aligning Vision-Language Models with User's Gaze Attention

In recent years, the integration of vision and language understanding has led to significant advancements in artificial intelligence, particularly through Vision-Language Models (VLMs). However, existing VLMs face challenges in handling real-world applications with complex scenes and multiple objects, as well as aligning their focus with the diverse attention patterns of human users. In this paper, we introduce gaze information, feasibly collected by AR or VR devices, as a proxy for human attention to guide VLMs and propose a novel approach, Voila-A, for gaze alignment to enhance the interpretability and effectiveness of these models in real-world applications. First, we collect hundreds of minutes of gaze data to demonstrate that we can mimic human gaze modalities using localized narratives. We then design an automatic data annotation pipeline utilizing GPT-4 to generate the VOILA-COCO dataset. Additionally, we innovate the Voila Perceiver modules to integrate gaze information into VLMs while preserving their pretrained knowledge. We evaluate Voila-A using a hold-out validation set and a newly collected VOILA-GAZE Testset, which features real-life scenarios captured with a gaze-tracking device. Our experimental results demonstrate that Voila-A significantly outperforms several baseline models. By aligning model attention with human gaze patterns, Voila-A paves the way for more intuitive, user-centric VLMs and fosters engaging human-AI interaction across a wide range of applications.
Multiple temperatures and melting of a colloidal active crystal

Thermal fluctuations constantly and evenly excite all vibrational modes in an equilibrium crystal. As the temperature rises, these fluctuations promote the formation of defects and eventually melting. In active solids, the self-propulsion of"atomic"units provides another source of strong non-equilibrium fluctuations whose effect on the melting scenario is still largely unexplored. Here we show that when a colloidal crystal is activated by a bath of swimming bacteria, solvent temperature and active temperature cooperate to define dynamic and thermodynamic properties. Our system consists of repulsive paramagnetic particles confined in two dimensions and immersed in a bath of light-driven E. coli. The relative balance between fluctuations and interactions can be adjusted in two ways: by changing the strength of the magnetic field and by tuning activity with light. When the persistence time of active fluctuations is short, a single effective temperature controls both the amplitudes of vibrational modes and the melting transition. For more persistent active noise, energy equipartition is broken and multiple temperatures emerge, whereas melting occurs before the Lindemann parameter reaches its equilibrium critical value. We show that this phenomenology is fully confirmed by numerical simulations and can be framed within a minimal model of a single active particle in a periodic potential.
On the Convergence of Gossip Learning in the Presence of Node Inaccessibility

—Gossip learning (GL), as a decentralized alternative to federated learning (FL), is more suitable for resource-constrained wireless networks, such as FANETs that are formed by unmanned aerial vehicles (UAVs). GL can significantly enhance the efficiency and extend the battery life of UAV networks. Despite the advantages, the performance of GL is strongly affected by data distribution, communication speed, and network connectivity. However, how these factors influence the GL convergence is still unclear. Existing work studied the convergence of GL based on a virtual quantity for the sake of convenience, which fail to reflect the real state of the network when some nodes are inaccessible. In this paper, we formulate and investigate the impact of inaccessible nodes to GL under a dynamic network topology. We first decompose the weight divergence by whether the node is accessible or not. Then, we investigate the GL convergence under the dynamic of node accessibility and theoretically provide how the number of inaccessible nodes, data non-i.i. d. -ness, and duration of inaccessibility affect the convergence. Extensive experiments are carried out in practical settings to comprehensively verify the correctness of our theoretical findings.
Intrasubject variability of early markers of sensorineural hearing damage

The pursuit of noninvasive early markers for sensorineural hearing loss (SNHL) has yielded diverse measures of interest. However, comprehensive studies evaluating the test-retest reliability of multiple measures and stimuli within a single study are scarce, and a standardized clinical protocol for robust SNHL-markers remains elusive. To address these gaps, this study covers the intra-subject variability of potential EEG-biomarkers for cochlear synaptopathy (CS) and other SNHL-markers to determine their clinical suitability. Fifteen normal-hearing young adults underwent repeated measures of (extended high-frequency) pure-tone audiometry, speech-in-noise intelligibility, distortion-product otoacoustic emissions (DPOAEs), and auditory evoked potentials; comprising envelope following responses (EFR) and auditory brainstem responses (ABR). Results show high reliability in pure-tone audiometry, whereas the matrix sentence-test showed a significant learning effect. DP-grams and input-output functions' reliability varied across three evaluation methods with distinct SNR-based criteria for DPOAE-datapoints. EFRs demonstrated superior test-retest reliability compared to ABR-amplitudes. Our findings underscore careful interpretation of presumed noninvasive SNHL measures. While we confirm the robustness of tonal-audiometry, we found a confounding learning effect in longitudinal speech audiometry. DPOAE variability underscores the need for consistent ear probe replacement and meticulous measurement techniques and renders I/O-functions unsuitable for clinical application. As potential EEG-biomarkers of CS, EFRs are favored over ABR-amplitudes.
Non-Degenerate Two-Photon Imaging of Deep Rodent Cortex using Indocyanine Green in the water absorption window

We present a novel approach for deep vascular imaging in rodent cortex at excitation wavelengths susceptible to water absorption using two-photon microscopy with photons of dissimilar wavelengths. We demonstrate that non-degenerate two-photon excitation (ND-2PE) enables imaging in the water absorption window from 1400-1550 nm using two synchronized excitation sources at 1300 nm and 1600 nm that straddle the absorption window. We explore the brightness spectra of indocyanine green (ICG) and assess its suitability for imaging in the water absorption window. Further, we demonstrate in vivo imaging of the rodent cortex vascular structure up to 1.2 mm using ND-2PE. Lastly, a comparative analysis of ND-2PE at 1435 nm and single-wavelength, two-photon imaging at 1300 nm and 1435 nm is presented. Our work extends the excitation range for fluorescent dyes to include water absorption regimes and underscores the feasibility of deep two-photon imaging at these wavelengths.
Establishment of a novel amyotrophic lateral sclerosis patient-derived blood-brain barrier model: Investigating barrier dysfunction and immune cell interaction

Amyotrophic lateral sclerosis (ALS) is a major neurodegenerative disease for which there is currently no effective treatment. The main feature of the disease is the loss of motor neurons in the central nervous system (CNS), but other CNS components and the immune system also appear to be involved in the pathogenesis of the disease. The blood-brain barrier (BBB) is formed by specialized brain microvascular endothelial cells (BMECs) and plays a crucial role in maintaining homeostasis within the central nervous system (CNS). Autopsy studies have reported BBB abnormalities in ALS patients, and studies in animal models suggest that BBB breakdown may precede neurodegeneration. However, the difficulty of obtaining patient samples has hampered further studies. We used the method of differentiating BMEC-like cells from human induced pluripotent stem cells (hiPSCs), which have robust barrier functions and express adhesion molecules necessary for immune cell adhesion, to investigate BBB functions in ALS patients. We also established a method using automated analysis techniques to analyze immune cell adhesion to the BBB in multiple samples simultaneously and with minimal bias. BMEC-like cells derived from ALS patients carrying TARDBP N345K/WT mutations exhibited increased permeability to small molecules due to loss of tight junction, as well as increased expression of cell surface adhesion molecules, leading to enhanced immune cell adhesion. BMEC-like cells derived from hiPSCs with other types of TARDBP gene mutations (TARDBP K263E/K263E and TARDBP G295S/G295S) introduced by genome editing technology did not show such BBB dysfunction compared to the isogenic control. These results indicate that our model can recapitulate BBB abnormalities reported in ALS patients using patient (TARDBP N345K/WT) -derived hiPSCs and that two types of missense mutation, TARDBP K263E/K263E and TARDBP G295S/G295S, do not contribute to the BBB dysfunction. This novel ALS patient-derived BBB model is useful for the challenging analysis of the involvement of BBB dysfunction in the pathogenesis and to promote therapeutic drug discovery.
Does early-stage Alzheimer’s disease affect the dynamics of motor adaptation?

Alzheimer’s disease (AD) is characterized by an initial decline in declarative memory, while non-declarative memory processing remains relatively intact. Error-based motor adaptation is traditionally seen as a form of non-declarative memory, but recent findings suggest that it involves both fast, declarative and slow, non-declarative adaptive processes. If the declarative memory system shares resources with the fast process in motor adaptation, it can be hypothesized that the fast, but not the slow, process is disturbed in AD patients. To test this, we studied 20 early-stage AD patients and 21 age-matched controls of both sexes using a reach adaptation paradigm that relies on spontaneous recovery after sequential exposure to opposing force fields. Adaptation was measured using error clamps and expressed as an adaptation index (AI). Although patients with AD showed slightly lower adaptation to the force field than the controls, both groups demonstrated effects of spontaneous recovery. The time course of the AI was fitted by a hierarchical Bayesian two-state model in which each dynamic state is characterized by a retention and learning rate. Compared to controls, the retention rate of the fast process was the only parameter that was significantly different (lower) in the AD patients, confirming that the memory of the declarative, fast process is disturbed by AD. The slow adaptive process was virtually unaffected. Since the slow process learns only weakly from error, our results provide neurocomputational evidence for the clinical practice of errorless learning of everyday tasks in people with dementia.