Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond Embeddings: The Promise of Visual Table in Multi-Modal Models

Visual representation learning has been a cornerstone in computer vision, evolving from supervised learning with human-annotated labels to aligning image-text pairs from the Internet. Despite recent advancements in multi-modal large language models (MLLMs), the visual representations they rely on, such as CLIP embeddings, often lack access to external world knowledge critical for real-world visual reasoning. In this work, we propose Visual Table, a novel visual representation tailored for MLLMs. It provides hierarchical text descriptions of holistic visual scenes, consisting of a scene description and multiple object-centric descriptions that encompass categories, attributes, and knowledge at instance level. We further develop a scalable generator for visual table generation and train it on small-scale annotations from GPT4V. Extensive evaluations demonstrate that, with generated visual tables as additional visual representations, our model can consistently outperform the state-of-the-art (SOTA) MLLMs across diverse benchmarks. When visual tables serve as standalone visual representations, our model can closely match or even beat the SOTA MLLMs that are built on CLIP visual embeddings. Our code is available at https://github.com/LaVi-Lab/Visual-Table.
The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision

Resource efficiency plays an important role for machine learning nowadays. The energy and decision latency are two critical aspects to ensure a sustainable and practical application. Unfortunately, the energy consumption and decision latency are not robust against adversaries. Researchers have recently demonstrated that attackers can compute and submit so-called sponge examples at inference time to increase the energy consumption and decision latency of neural networks. In computer vision, the proposed strategy crafts inputs with less activation sparsity which could otherwise be used to accelerate the computation. In this paper, we analyze the mechanism how these energy-latency attacks reduce activation sparsity. In particular, we find that input uniformity is a key enabler. A uniform image, that is, an image with mostly flat, uniformly colored surfaces, triggers more activations due to a specific interplay of convolution, batch normalization, and ReLU activation. Based on these insights, we propose two new simple, yet effective strategies for crafting sponge examples: sampling images from a probability distribution and identifying dense, yet inconspicuous inputs in natural datasets. We empirically examine our findings in a comprehensive evaluation with multiple image classification models and show that our attack achieves the same sparsity effect as prior sponge-example methods, but at a fraction of computation effort. We also show that our sponge examples transfer between different neural networks. Finally, we discuss applications of our findings for the good by improving efficiency by increasing sparsity.
Few-shot Online Anomaly Detection and Segmentation

Detecting anomaly patterns from images is a crucial artificial intelligence technique in industrial applications. Recent research in this domain has emphasized the necessity of a large volume of training data, overlooking the practical scenario where, post-deployment of the model, unlabeled data containing both normal and abnormal samples can be utilized to enhance the model's performance. Consequently, this paper focuses on addressing the challenging yet practical few-shot online anomaly detection and segmentation (FOADS) task. Under the FOADS framework, models are trained on a few-shot normal dataset, followed by inspection and improvement of their capabilities by leveraging unlabeled streaming data containing both normal and abnormal samples simultaneously. To tackle this issue, we propose modeling the feature distribution of normal images using a Neural Gas network, which offers the flexibility to adapt the topology structure to identify outliers in the data flow. In order to achieve improved performance with limited training samples, we employ multi-scale feature embedding extracted from a CNN pre-trained on ImageNet to obtain a robust representation. Furthermore, we introduce an algorithm that can incrementally update parameters without the need to store previous samples. Comprehensive experimental results demonstrate that our method can achieve substantial performance under the FOADS setting, while ensuring that the time complexity remains within an acceptable range on MVTec AD and BTAD datasets.
Chronic and Acute Models of Retinal Neurodegeneration TrkA Activity Are Neuroprotective whereas p75NTR Activity Is Neurotoxic through a Paracrine Mechanism*

In normal adult retinas, NGF receptor TrkA is expressed in retinal ganglion cells (RGC), whereas glia express p75NTR. During retinal injury, endogenous NGF, TrkA, and p75NTR are up-regulated. Paradoxically, neither endogenous NGF nor exogenous administration of wild type NGF can protect degenerating RGCs, even when administered at high frequency. Here we elucidate the relative contribution of NGF and each of its receptors to RGC degeneration in vivo. During retinal degeneration due to glaucoma or optic nerve transection, treatment with a mutant NGF that only activates TrkA, or with a biological response modifier that prevents endogenous NGF and pro-NGF from binding to p75NTR affords significant neuroprotection. Treatment of normal eyes with an NGF mutant-selective p75NTR agonist causes progressive RGC death, and in injured eyes it accelerates RGC death. The mechanism of p75NTR action during retinal degeneration due to glaucoma is paracrine, by increasing production of neurotoxic proteins TNF-α and α2-macroglobulin. Antagonists of p75NTR inhibit TNF-α and α2-macroglobulin up-regulation during disease, and afford neuroprotection. These data reveal a balance of neuroprotective and neurotoxic mechanisms in normal and diseased retinas, and validate each neurotrophin receptor as a pharmacological target for neuroprotection.
Formally Modelling the Rijkswaterstaat Tunnel Control Systems in a Constrained Industrial Environment

Rijkswaterstaat, the National Dutch body responsible for infrastructure, recognised the importance of formal modelling and set up a program to model the control of road tunnels. This is done to improve the standardisation of tunnel control and make communication with suppliers smoother. A subset of SysML is used to formulate the models, which are substantial. In an earlier paper we have shown that these models can be used to prove behavioural properties by manually translating the models to mCRL2. In this paper we report on an automatic translation to mCRL2. As the results of the translation became unwieldy, we also investigated modelling tunnel control in the specification language Dezyne which has built-in verification capabilities and compared the results.
An analytic method for quadratic polarons in non-parabolic bands

Including the effect of lattice anharmonicity on electron-phonon interactions has recently garnered attention due to its role as a necessary and significant component in explaining various phenomena, including superconductivity, optical response, and the temperature dependence of mobility. This study focuses on analytically treating the effects of anharmonic electron-phonon coupling on the polaron self-energy, combined with numerical Diagrammatic Monte Carlo data. Specifically, we incorporate a quadratic interaction into the method of squeezed phonon states, which has proven effective for analytically calculating the polaron parameters. Additionally, we extend this method to non-parabolic finite-width conduction bands while maintaining the periodic translation symmetry of the system. Our results are compared with those obtained from Diagrammatic Monte Carlo, partially reported in a recent study [Phys. Rev. B 107, L121109(2023)], covering a wide range of coupling strengths for the nonlinear interaction. Remarkably, our analytic method predicts the same features as the Diagrammatic Monte Carlo simulation.
Differentially Private Dual Gradient Tracking for Distributed Resource Allocation

This paper investigates privacy issues in distributed resource allocation over directed networks, where each agent holds a private cost function and optimizes its decision subject to a global coupling constraint through local interaction with other agents. Conventional methods for resource allocation over directed networks require all agents to transmit their original data to neighbors, which poses the risk of disclosing sensitive and private information. To address this issue, we propose an algorithm called differentially private dual gradient tracking (DP-DGT) for distributed resource allocation, which obfuscates the exchanged messages using independent Laplacian noise. Our algorithm ensures that the agents' decisions converge to a neighborhood of the optimal solution almost surely. Furthermore, without the assumption of bounded gradients, we prove that the cumulative differential privacy loss under the proposed algorithm is finite even when the number of iterations goes to infinity. To the best of our knowledge, we are the first to simultaneously achieve these two goals in distributed resource allocation problems over directed networks. Finally, numerical simulations on economic dispatch problems within the IEEE 14-bus system illustrate the effectiveness of our proposed algorithm.
Feeling the Need for (Personalized) Speed: How Natural Controls and Customization Contribute to Enjoyment of a Racing Game Through Enhanced Immersion

Prior research suggests that video game features that appear natural or that otherwise allow players to identify with their in-game experience will promote enjoyment. Using a 2 × 2 experiment, this study demonstrates the positive effects of a steering-wheel controller and the opportunity to customize the driven vehicle on enjoyment of a console driving game, as mediated by transportation and challenge-skill balance. The role of presence is also probed, with results suggesting no direct link to enjoyment.
Toward a Computational Model of Social Relations for Artificial Companions

The objective of my Ph.D is to develop a computational model of social relations for artificial companions. Such a model should consider the representation of the social relation, its initialization and its dynamics over time. Moreover, the influence of this relation on the agent's decision-making should be modeled. In the proposed computational model described in this paper, the representation, initialization, and dynamics of the social relation are based on the research in Human and Social Sciences, considering the influence of individual and social parameters such as the personality of the agent or its social role.
Compactifications of the Eisenstein ancestral Deligne-Mostow variety

All arithmetic non-compact ball quotients by Deligne-Mostow's unitary monodromy group arise as sub-ball quotients of either of two spaces called ancestral cases, corresponding to Gaussian or Eisenstein Hermitian forms respectively. In our previous paper, we investigated the compactifications of the Gaussian Deligne-Mostow variety. Here we work on the remaining case, namely the ring of Eisenstein integers, which is related to the moduli space of unordered 12 points on $\mathbb P^1$. In particular, we show that Kirwan's partial resolution of the moduli space is not a semi-toroidal compactification and Deligne-Mostow's period map does not lift to the unique toroidal compactification. We give two interpretations of these phenomena in terms of the log minimal model program and automorphic forms. As an application, we prove that the above two compactifications are not (stacky) derived equivalent, as the $DK$-conjecture predicts. Furthermore, we construct an automorphic form on the moduli space of non-hyperelliptic curves of genus 4, which is isogenous to the Eisenstein Deligne-Mostow variety, giving another intrinsic proof, independent of lattice embeddings, of a result by Casalaina-Martin, Jensen and Laza.
DCU@FIRE-2014: Fuzzy Queries with Rule-based Normalization for Mixed Script Information Retrieval

We describe the participation of Dublin City University (DCU) in the FIRE-2014 shared task on transliteration search, hereby referred to as the TST (Transliteration Search Task). The TST involves an ad-hoc search over a collection of Hindi film song lyrics. The Hindi language content of each document of the collection is either written in the native script or transliterated in Roman script or both. The queries may also be in mixed script. The task is challenging primarily because of the vocabulary mismatch which may arise due to the multiple transliteration alternatives. We attempt to address the vocabulary mismatch problem both during the indexing and retrieval. During indexing, we apply a rule-based normalization on some character sequences of the transliterated words in order to have a single representation in the index for the multiple transliteration alternatives. During the retrieval phase, we make use of prefix matched fuzzy query terms to account for the morphological variations of the transliterated words. The results show significant improvement of about 56% over a standard baseline query likelihood language modelling (LM) approach. Additionally, we also apply statistical machine transliteration to train a transliteration model in order to predict the transliteration of out-of-vocabulary words. Surprisingly, even with satisfactory transliteration accuracy, we find that automatic transliteration of query terms degrades retrieval effectiveness.
Topology Optimization for the Full-Cell Design of Porous Electrodes in Electrochemical Energy Storage Devices

In this paper, we introduce a density-based topology optimization framework to design porous electrodes for maximum energy storage. We simulate the full cell with a model that incorporates electronic potential, ionic potential, and electrolyte concentration. The system consists of three materials, namely pure liquid electrolyte and the porous solids of the anode and cathode, for which we determine the optimal placement. We use separate electronic potentials to model each electrode, which allow interdigitated designs. As the result, a penalization is required to ensure that the anode and cathode do not touch, i.e. causing a short circuit. We compare multiple 2D designs generated for different fixed conditions, e.g. material properties. A 3D design with complex channel and interlocking structure is also created. All optimized designs are far superior to the traditional monolithic electrode design with respect to energy storage metrics. We observe up to 750% increase in energy storage for cases with slow effective ionic diffusion within the porous electrode.
Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time

The Influence Maximization (IM) problem seeks to discover the set of nodes in a graph that can spread the information propagation at most. This problem is known to be NP-hard, and it is usually studied by maximizing the influence (spread) and, optionally, optimizing a second objective, such as minimizing the seed set size or maximizing the influence fairness. However, in many practical scenarios multiple aspects of the IM problem must be optimized at the same time. In this work, we propose a first case study where several IM-specific objective functions, namely budget, fairness, communities, and time, are optimized on top of the maximization of influence and minimization of the seed set size. To this aim, we introduce MOEIM (Many-Objective Evolutionary Algorithm for Influence Maximization) a Multi-Objective Evolutionary Algorithm (MOEA) based on NSGA-II incorporating graph-aware operators and a smart initialization. We compare MOEIM in two experimental settings, including a total of nine graph datasets, two heuristic methods, a related MOEA, and a state-of-the-art Deep Learning approach. The experiments show that MOEIM overall outperforms the competitors in most of the tested many-objective settings. To conclude, we also investigate the correlation between the objectives, leading to novel insights into the topic. The codebase is available at https://github.com/eliacunegatti/MOEIM.
Frequency-Dependent Changes in Cerebral Metabolic Rate of Oxygen during Activation of Human Visual Cortex

To test the hypothesis that brain oxidative metabolism is significantly increased upon adequate stimulation, we varied the presentation of a visual stimulus to determine the frequency at which the metabolic response would be at maximum. The authors measured regional CMR O 2 in 12 healthy normal volunteers with the ECAT EXACT HR+ (CTI/Siemens, Knoxville, TN, U.S.A.) three-dimensional whole-body positron emission tomograph (PET). In seven successive activating conditions, subjects viewed a yellow-blue annular checkerboard reversing its contrast at frequencies of 0, 1, 4, 8, 16, 32, and 50 Hz. Stimulation began 4 minutes before and continued throughout the 3-minute dynamic scan. In the baseline condition, the subjects began fixating a cross hair 30 seconds before the scan and continued to do so for the duration of the 3-minute scan. At the start of each scan, the subjects inhaled 20 mCi of 15O-O2 in a single breath. The CMR O 2 value was calculated using a two-compartment, weighted integration method. Normalized PET images were averaged across subjects and coregistered with the subjects' magnetic resonance imaging in stereotaxic space. Mean subtracted image volumes (activation minus baseline) of CMR O 2 then were obtained and converted to z statistic volumes. The authors found a statistically significant focal change of CMR O 2 in the striate cortex (x = 9; y = −89; z = −1) that reached a maximum at 4 Hz and dropped off sharply at higher stimulus frequencies.
Compact Propositional Encodings of First-Order Theories

In this paper we present polynomial-time algorithms that translate First-Order Logic (FOL) theories to smaller propositional encodings than achievable before in polynomial time. For example, we can sometimes reduce the number of propositions to O(|P| + |C|), or O(|P|k ċ log |P|), for |P| predicates of arity k and |C| constant symbols. The guarantee depends on availability of some graphical structure in the FOL representation. Our algorithms accept all FOL theories, and preserve soundness and completeness (sometimes requiring the Domain Closure Assumption). Our experiments show significant speedup in inference with a SAT solver on real-world problems. Our results address a common approach that translates inference and decision problems that originate in FOL into propositional logic, later applying efficient SAT solvers. Standard translation techniques result in very large propositional encodings (O(|P||C|k) for predicates of arity k) that are often infeasible to solve. Our approach scales up inference for many objects, and has potential applications in planning, probabilistic reasoning, and formal verification.
Shelf hypoxia in response to global warming after the Cretaceous-Paleogene boundary impact

The Chicxulub asteroid impact at the Cretaceous-Paleogene (K-Pg) boundary resulted in one of the most abrupt global warming events in the past 100 m.y. , presenting an analogue to current global warming. Here, we present high-resolution geochemical, micropaleontological, and palynological records of the Brazos-1 (Texas, USA), Stevns Klint (Denmark), and Caravaca (Spain) K-Pg boundary sections to assess the rapid environmental changes during the global warming following the brief K-Pg boundary impact winter. Warming during the first millennia after the impact is associated with hypoxic bottom waters at the studied shelf sites, as indicated by molybdenum enrichments, causing major stress for benthic communities. We attribute this decline in dissolved oxygen to a combination of decreased gas solubility and ocean ventilation resulting from the warming of the sea water, and increased oxygen demand in shelf bottom waters due to increased nutrient inputs and associated high productivity.
Multi-AGV Path Planning Method via Reinforcement Learning and Particle Filters

The Reinforcement Learning (RL) algorithm, renowned for its robust learning capability and search stability, has garnered significant attention and found extensive application in Automated Guided Vehicle (AGV) path planning. However, RL planning algorithms encounter challenges stemming from the substantial variance of neural networks caused by environmental instability and significant fluctuations in system structure. These challenges manifest in slow convergence speed and low learning efficiency. To tackle this issue, this paper presents the Particle Filter-Double Deep Q-Network (PF-DDQN) approach, which incorporates the Particle Filter (PF) into multi-AGV reinforcement learning path planning. The PF-DDQN method leverages the imprecise weight values of the network as state values to formulate the state space equation. Through the iterative fusion process of neural networks and particle filters, the DDQN model is optimized to acquire the optimal true weight values, thus enhancing the algorithm's efficiency. The proposed method's effectiveness and superiority are validated through numerical simulations. Overall, the simulation results demonstrate that the proposed algorithm surpasses the traditional DDQN algorithm in terms of path planning superiority and training time indicators by 92.62% and 76.88%, respectively. In conclusion, the PF-DDQN method addresses the challenges encountered by RL planning algorithms in AGV path planning. By integrating the Particle Filter and optimizing the DDQN model, the proposed method achieves enhanced efficiency and outperforms the traditional DDQN algorithm in terms of path planning superiority and training time indicators.
Phonons in stringlet-land and the boson peak

Solid materials that deviate from the harmonic crystal paradigm exhibit characteristic anomalies in the specific heat and vibrational density of states (VDOS) with respect to Debye's theory predictions. The boson peak (BP), a low-frequency excess in the VDOS over Debye law $g(\omega) \propto \omega^2$, is certainly the most famous among them; nevertheless, its origin is still subject of fierce debate. Recent simulation works provided strong evidence that localized one-dimensional string-like excitations (stringlets) might be the microscopic origin of the BP. In this work, we study the dynamics of acoustic phonons interacting with a bath of vibrating 1D stringlets with exponentially distributed size, as observed in simulations. We show that stringlets strongly renormalize the phonon propagator and naturally induce a BP anomaly in the vibrational density of states, corresponding to the emergence of a dispersionless BP flat mode. Additionally, phonon-stringlet interactions produce a strong enhancement of sound attenuation and a dip in the speed of sound near the BP frequency, consistent with experimental and simulation data. The qualitative trends of the BP frequency and intensity are predicted within the model and shown to be in good agreement with previous observations. In summary, our results substantiate with a simple theoretical model the recent simulation results by Hu and Tanaka claiming the origin of the BP from stringlet dynamics.
ACES: Evaluating Automated Audio Captioning Models on the Semantics of Sounds

Automated Audio Captioning is a multimodal task that aims to convert audio content into natural language. The performance of audio captioning systems is evaluated on quantitative metrics applied to the text representations. Previously, researchers have applied metrics from machine translation and image captioning to evaluate a generated audio caption. Inspired by cognitive neuroscience research on auditory cognition, in this paper we present a novel metric approach that evaluates captions taking into account how human listeners derive semantic information from sounds: Audio Captioning Evaluation on Semantics of Sound (ACES).