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Unsupervised domain adaptation remains a critical challenge in enabling the knowledge transfer of models across unseen domains. Existing methods struggle to balance the need for domain-invariant representations with preserving domain-specific features, which is often due to alignment approaches that impose the projection of samples with similar semantics close in the latent space despite their drastic domain differences. We introduce LAGUNA - LAnguage Guided UNsupervised Adaptation with structured spaces, a novel approach that shifts the focus from aligning representations in absolute coordinates to aligning the relative positioning of equivalent concepts in latent spaces. LAGUNA defines a domain-agnostic structure upon the semantic/geometric relationships between class labels in language space and guides adaptation, ensuring that the organization of samples in visual space reflects reference inter-class relationships while preserving domain-specific characteristics. We empirically demonstrate LAGUNA's superiority in domain adaptation tasks across four diverse images and video datasets. Remarkably, LAGUNA surpasses previous works in 18 different adaptation scenarios across four diverse image and video datasets with average accuracy improvements of +3.32% on DomainNet, +5.75% in GeoPlaces, +4.77% on GeoImnet, and +1.94% mean class accuracy improvement on EgoExo4D.

We present a systematic study of higher-order Airy-type differential equations providing the explicit form of the solutions, deriving their power series expansions and a probabilistic interpretation. Under suitable convergence hypotheses, we compute their integral on the real line and, by means of complex integration, we provide alternative explicit forms. We then focus on the differential equations governing their derivatives, their products, their convolutions and higher-order Scorer type equations. Then, we study higher-order heat-type fractional Cauchy, showing that their fundamental solutions can be expressed in terms of Airy-type functions and their convolutions, recovering as special cases several results of appeared in previous papers. Furthermore, pseudo-processes theory permits us to give nice interpretations of the results, extending them in the case of equations involving different fractional operators and study the moments of the solutions., Comment: 25 pages, 3 figures

This paper works on Binary Neural Networks (BNNs), a promising avenue for efficient deep learning, offering significant reductions in computational overhead and memory footprint to full precision networks. However, the challenge of energy-intensive training and the drop in performance have been persistent issues. Tackling the challenge, prior works focus primarily on task-related inference optimization. Unlike prior works, this study offers an innovative methodology integrating BNNs with cyclic precision training, introducing the CycleBNN. This approach is designed to enhance training efficiency while minimizing the loss in performance. By dynamically adjusting precision in cycles, we achieve a convenient trade-off between training efficiency and model performance. This emphasizes the potential of our method in energy-constrained training scenarios, where data is collected onboard and paves the way for sustainable and efficient deep learning architectures. To gather insights on CycleBNN's efficiency, we conduct experiments on ImageNet, CIFAR-10, and PASCAL-VOC, obtaining competitive performances while using 96.09\% less operations during training on ImageNet, 88.88\% on CIFAR-10 and 96.09\% on PASCAL-VOC. Finally, CycleBNN offers a path towards faster, more accessible training of efficient networks, accelerating the development of practical applications. The PyTorch code is available at \url{https://github.com/fedeloper/CycleBNN/}, Comment: Published at Workshop CADL, ECCV-2024

This paper introduces the Neural Transcoding Vision Transformer (\modelname), a generative model designed to estimate high-resolution functional Magnetic Resonance Imaging (fMRI) samples from simultaneous Electroencephalography (EEG) data. A key feature of \modelname is its Domain Matching (DM) sub-module which effectively aligns the latent EEG representations with those of fMRI volumes, enhancing the model's accuracy and reliability. Unlike previous methods that tend to struggle with fidelity and reproducibility of images, \modelname addresses these challenges by ensuring methodological integrity and higher-quality reconstructions which we showcase through extensive evaluation on two benchmark datasets; \modelname outperforms the current state-of-the-art by a significant margin in both cases, e.g. achieving a $10\times$ reduction in RMSE and a $3.14\times$ increase in SSIM on the Oddball dataset. An ablation study also provides insights into the contribution of each component to the model's overall effectiveness. This development is critical in offering a new approach to lessen the time and financial constraints typically linked with high-resolution brain imaging, thereby aiding in the swift and precise diagnosis of neurological disorders. Although it is not a replacement for actual fMRI but rather a step towards making such imaging more accessible, we believe that it represents a pivotal advancement in clinical practice and neuroscience research. Code is available at \url{https://github.com/rom42pla/ntvit}., Comment: ECCV24 Workshop on Synthetic Data for Computer Vision

Action anticipation is the task of forecasting future activity from a partially observed sequence of events. However, this task is exposed to intrinsic future uncertainty and the difficulty of reasoning upon interconnected actions. Unlike previous works that focus on extrapolating better visual and temporal information, we concentrate on learning action representations that are aware of their semantic interconnectivity based on prototypical action patterns and contextual co-occurrences. To this end, we propose the novel Semantically Guided Representation Learning (S-GEAR) framework. S-GEAR learns visual action prototypes and leverages language models to structure their relationship, inducing semanticity. To gather insights on S-GEAR's effectiveness, we test it on four action anticipation benchmarks, obtaining improved results compared to previous works: +3.5, +2.7, and +3.5 absolute points on Top-1 Accuracy on Epic-Kitchen 55, EGTEA Gaze+ and 50 Salads, respectively, and +0.8 on Top-5 Recall on Epic-Kitchens 100. We further observe that S-GEAR effectively transfers the geometric associations between actions from language to visual prototypes. Finally, S-GEAR opens new research frontiers in anticipation tasks by demonstrating the intricate impact of action semantic interconnectivity., Comment: Accepted as a full paper at ECCV'24 with Paper ID #4140

Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models, the need for real-time detection demands greater efficiency. With this in mind, unlike previous work, we introduce a novel deepfake detection approach on images using Binary Neural Networks (BNNs) for fast inference with minimal accuracy loss. Moreover, our method incorporates Fast Fourier Transform (FFT) and Local Binary Pattern (LBP) as additional channel features to uncover manipulation traces in frequency and texture domains. Evaluations on COCOFake, DFFD, and CIFAKE datasets demonstrate our method's state-of-the-art performance in most scenarios with a significant efficiency gain of up to a $20\times$ reduction in FLOPs during inference. Finally, by exploring BNNs in deepfake detection to balance accuracy and efficiency, this work paves the way for future research on efficient deepfake detection., Comment: Accepted at CVPR24 DFAD Workshop

In this paper, we i) analyze and classify real-world failures of Kubernetes (the most popular container orchestration system), ii) develop a framework to perform a fault/error injection campaign targeting the data store preserving the cluster state, and iii) compare results of our fault/error injection experiments with real-world failures, showing that our fault/error injections can recreate many real-world failure patterns. The paper aims to address the lack of studies on systematic analyses of Kubernetes failures to date. Our results show that even a single fault/error (e.g., a bit-flip) in the data stored can propagate, causing cluster-wide failures (3% of injections), service networking issues (4%), and service under/overprovisioning (24%). Errors in the fields tracking dependencies between object caused 51% of such cluster-wide failures. We argue that controlled fault/error injection-based testing should be employed to proactively assess Kubernetes' resiliency and guide the design of failure mitigation strategies.

With the increasing use of multicore platforms to realize mixed-criticality systems, understanding the underlying shared resources, such as the memory hierarchy shared among cores, and achieving isolation between co-executing tasks running on the same platform with different criticality levels becomes relevant. In addition to safety considerations, a malicious entity can exploit shared resources to create timing attacks on critical applications. In this paper, we focus on understanding the shared DRAM dual in-line memory module and created a timing attack, that we named the "bank & row conflict bomb", to target a victim task in a multicore platform. We also created a "navigate" algorithm to understand how victim requests are managed by the Memory Controller and provide valuable inputs for designing the bank & row conflict bomb. We performed experimental tests on a 2nd Gen Intel Xeon Processor with an 8GB DDR4-2666 DRAM module to show that such an attack can produce a significant increase in the execution time of the victim task by about 150%, motivating the need for proper countermeasures to help ensure the safety and security of critical applications., Comment: To appear in the proceedings of the 27th IEEE international Symposium on Real-time Distributed Computing (ISORC)

The adoption of cloud computing technologies in the industry is paving the way to new manufacturing paradigms. In this paper we propose a model to optimize the orchestration of workloads with differentiated criticality levels on a cloud-enabled factory floor. Preliminary results show that it is possible to optimize the guarantees to deployed jobs without penalizing the number of schedulable jobs. We indicate future research paths to quantitatively evaluate job isolation., Comment: S. Bernardi, T. Zoppi (Editors), Fast Abstracts and Student Forum Proceedings - EDCC 2024 - 19th European Dependable Computing Conference, Leuven, Belgium, 8-11 April 2024

We study Cauchy problems of fractional differential equations in both space and time variables by expressing the solution in terms of ``stochastic composition" of the solutions to two simpler problems. These Cauchy sub-problems respectively concern the space and the time differential operator involved in the main equation. We provide some probabilistic and pseudo-probabilistic applications, where the solution can be interpreted as the pseudo-transition density of a time-changed pseudo-process. To extend our results to higher order time-fractional problems, we introduce pseudo-subordinators as well as its pseudo-inverse. Finally, we present our results in the case of more general differential operators and we interpret the results by means of a linear combination of pseudo-subordinators and its inverse., Comment: 21

Vision Transformer (ViT) self-attention mechanism is characterized by feature collapse in deeper layers, resulting in the vanishing of low-level visual features. However, such features can be helpful to accurately represent and identify elements within an image and increase the accuracy and robustness of vision-based recognition systems. Following this rationale, we propose a novel residual attention learning method for improving ViT-based architectures, increasing their visual feature diversity and model robustness. In this way, the proposed network can capture and preserve significant low-level features, providing more details about the elements within the scene being analyzed. The effectiveness and robustness of the presented method are evaluated on five image classification benchmarks, including ImageNet1k, CIFAR10, CIFAR100, Oxford Flowers-102, and Oxford-IIIT Pet, achieving improved performances. Additionally, experiments on the COCO2017 dataset show that the devised approach discovers and incorporates semantic and spatial relationships for object detection and instance segmentation when implemented into spatial-aware transformer models., Comment: 17 pages, single column, 9 figures

Inhomogeneous cloud formation and wavelength-dependent phenomena are expected to shape hot Jupiter atmospheres. We present a General Circulation Model (GCM) with multiwavelength "picket fence" radiative transfer and radiatively active, temperature dependent clouds, and compare the results to a double gray routine. The double gray method inherently fails to model polychromatic effects in hot Jupiter atmospheres, while picket fence captures these non-gray aspects and performs well compared to fully wavelength-dependent methods. We compare both methods with radiatively active clouds and cloud-free models, assessing the limitations of the double gray method. Although there are broad similarities, the picket fence models have larger day-night side temperature differences, non-isothermal upper atmospheres, and multiwavelength effects in the presence of radiatively active clouds. We model the well-known hot Jupiters HD 189733 b and HD 209458 b. For the hotter HD 209458 b, the picket fence method prevents clouds from thermostating dayside temperatures, resulting in hotter upper atmospheres and the dissipation of dayside clouds. Differences in the temperature structures are then associated with nuanced differences in the circulation patterns and clouds. Models of the cooler HD 189733 b have global cloud coverage, regardless of radiative transfer scheme, whereas there are larger differences in the models of HD 209458 b, particularly in the extent of the partial cloud coverage on its dayside. This results in minor changes to the thermal and reflected light phase curves of HD 189733 b, but more significant differences for the picket fence and double gray versions of HD 209458 b., Comment: Accepted in ApJ, 31 pages

Hemoglycin, a space polymer of glycine and iron, has been identified in the carbonaceous chondritic meteorites Allende, Acfer 086, Kaba, Sutters Mill and Orgueil. Its core form has a mass of 1494Da and is basically an antiparallel pair of polyglycine strands linked at each end by an iron atom. The polymer forms two- and three- dimensional lattices with an inter-vertex distance of 4.9nm. Here the extraction technique for meteorites is applied to a 2.1Gya fossil stromatolite to reveal the presence of hemoglycin by mass spectrometry. Intact ooids from a recent (3,000Ya) stromatolite exhibited the same visible hemoglycin fluorescence in response to x-rays as an intact crystal from the Orgueil meteorite. X-ray analysis confirmed the existence in ooids of an internal 3-dimensional lattice of 4.9nm inter-vertex spacing, matching the spacing of lattices in meteoritic crystals. FTIR measurements of acid-treated ooid and a Sutters Mill meteoritic crystal both show the presence, via the splitting of the Amide I band, of an extended anti-parallel beta sheet structure. It seems probable that the copious in-fall of carbonaceous meteoritic material, from Archaean times onward, has left traces of hemoglycin in sedimentary carbonates and potentially has influenced ooid formation., Comment: 45 pages 16 Figure 8 Tables

In this paper we give an explicit solution of Dzherbashyan-Caputo-fractional Cauchy problems related to equations with derivatives of order $\nu k$, for $k$ non-negative integer and $\nu>0$. The solution is obtained by connecting the differential equation with the roots of the characteristic polynomial and it is expressed in terms of Mittag-Leffler-type functions. Under the some stricter hypothesis the solution can be expressed as a linear combination of Mittag-Leffler functions with common fractional order $\nu$. We establish a probabilistic relationship between the solutions of differential problems with order $\nu/m$ and $\nu$, for natural $m$. Finally, we use the described method to solve fractional differential equations arising in the fractionalization of partial differential equations related to the probability law of planar random motions with finite velocities., Comment: 21 pages

We present a detailed analysis of random motions moving in higher spaces with a natural number of velocities. In the case of the so-called minimal random dynamics, under some wide assumptions, we show the joint distribution of the position of the motion (both for the inner part and the border of the support) and the number of displacements performed with each velocity. Explicit results for cyclic and complete motions are derived. We establish useful relationships between motions moving in different spaces and we derive the form of the distribution of the movements in arbitrary dimension. Finally, we investigate further properties for stochastic motions governed by non-homogeneous Poisson processes., Comment: 29 pages

Nowadays, industries are looking into virtualization as an effective means to build safe applications, thanks to the isolation it can provide among virtual machines (VMs) running on the same hardware. In this context, a fundamental issue is understanding to what extent the isolation is guaranteed, despite possible (or induced) problems in the virtualization mechanisms. Uncovering such isolation issues is still an open challenge, especially for hardware-assisted virtualization, since the search space should include all the possible VM states (and the linked hypervisor state), which is prohibitive. In this paper, we propose IRIS, a framework to record (learn) sequences of inputs (i.e., VM seeds) from the real guest execution (e.g., OS boot), replay them as-is to reach valid and complex VM states, and finally use them as valid seed to be mutated for enabling fuzzing solutions for hardware-assisted hypervisors. We demonstrate the accuracy and efficiency of IRIS in automatically reproducing valid VM behaviors, with no need to execute guest workloads. We also provide a proof-of-concept fuzzer, based on the proposed architecture, showing its potential on the Xen hypervisor., Comment: 13 pages, Accepted for publication at The 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

Though the global atmospheres of hot Jupiters have been extensively studied using phase curve observations, the level of time variability in these data is not well constrained. To investigate possible time variability in a planetary phase curve, we observed two full-orbit phase curves of the hot Jupiter WASP-43b at 4.5 microns using the Spitzer Space Telescope, and reanalyzed a previous 4.5 micron phase curve from Stevenson et al. (2017). We find no significant time variability between these three phase curves, which span timescales of weeks to years. The three observations are best fit by a single phase curve with an eclipse depth of 3907 +- 85 ppm, a dayside-integrated brightness temperature of 1479 +- 13 K, a nightside-integrated brightness temperature of 755 +- 46 K, and an eastward-shifted peak of 10.4 +- 1.8 degrees. To model our observations, we performed 3D general circulation model simulations of WASP-43b with simple cloud models of various vertical extents. In comparing these simulations to our observations, we find that WASP-43b likely has a cloudy nightside that transitions to a relatively cloud-free dayside. We estimate that any change in WASP-43bs vertical cloud thickness of more than three pressure scale heights is inconsistent with our observed upper limit on variation. These observations, therefore, indicate that WASP-43bs clouds are stable in their vertical and spatial extent over timescales up to several years. These results strongly suggest that atmospheric properties derived from previous, single Spitzer phase curve observations of hot Jupiters likely show us the equilibrium properties of these atmospheres., Comment: 24 pages, 9 figures, Published in the Astronomical Journal (AJ)

The goal of this paper is to introduce pooling strategies for simplicial convolutional neural networks. Inspired by graph pooling methods, we introduce a general formulation for a simplicial pooling layer that performs: i) local aggregation of simplicial signals; ii) principled selection of sampling sets; iii) downsampling and simplicial topology adaptation. The general layer is then customized to design four different pooling strategies (i.e., max, top-k, self-attention, and separated top-k) grounded in the theory of topological signal processing. Also, we leverage the proposed layers in a hierarchical architecture that reduce complexity while representing data at different resolutions. Numerical results on real data benchmarks (i.e., flow and graph classification) illustrate the advantage of the proposed methods with respect to the state of the art.

Orchestration systems are becoming a key component to automatically manage distributed computing resources in many fields with criticality requirements like Industry 4.0 (I4.0). However, they are mainly linked to OS-level virtualization, which is known to suffer from reduced isolation. In this paper, we propose RunPHI with the aim of integrating partitioning hypervisors, as a solution for assuring strong isolation, with OS-level orchestration systems. The purpose is to enable container orchestration in mixed-criticality systems with isolation requirements through partitioned containers., Comment: 2 pages, accepted for publication in Proc. ISSREW, 2022