Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Augmentation using Feature Generation for Volumetric Medical Images

Medical image classiﬁcation is one of the most critical problems in the image recognition area. One of the major challenges in this ﬁeld is the scarcity of labelled training data. Additionally, there is often class imbalance in datasets as some cases are very rare to happen. As a result, accuracy in classiﬁcation task is normally low. Deep Learning models, in particular, show promising results on image segmentation and classiﬁcation problems, but they require very large datasets for training. Therefore, there is a need to generate more of synthetic samples from the same distribution. Previous work has shown that feature generation is more efﬁcient and leads to better performance than corresponding image generation [12]. We apply this idea in the Medical Imaging domain. We use transfer learning to train a segmentation model for the small dataset for which gold-standard class annotations are available. We extracted the learnt features and use them to generate synthetic features conditioned on class labels, using Auxiliary Classiﬁer GAN (ACGAN). We test the quality of the generated features in a downstream classiﬁcation task for brain tumors according to their severity level. Experimental results show a promising result regarding the validity of these generated features and their overall contribution to balancing the data and im-proving the classiﬁcation class-wise accuracy.
Attacking Compressed Vision Transformers

Vision Transformers are increasingly embedded in industrial systems due to their superior performance, but their memory and power requirements make deploying them to edge devices a challenging task. Hence, model compression techniques are now widely used to deploy models on edge devices as they decrease the resource requirements and make model inference very fast and efficient. But their reliability and robustness from a security perspective is another major issue in safety-critical applications. Adversarial attacks are like optical illusions for ML algorithms and they can severely impact the accuracy and reliability of models. In this work we investigate the transferability of adversarial samples across the SOTA Vision Transformer models across 3 SOTA compressed versions and infer the effects different compression techniques have on adversarial attacks.
Inverse heat transfer problems

This text develops a methodology for solving ill-posed heat transfer problems. It describes the systematization and formulation of various inverse problems and their application in the diagnosis and identification of heat transfer processes. Various methods and algorithms of inverse problem solving are presented and compared, and recommendations on how to use them are given. Particular attention is paid to iterative regularization as one of the most effective and rapidly developing methods in the theory of ill-posed inverse problems for systems with distributed parameters. The methods described in the book find important practical applications in various areas of engineering. Many examples are given and methods and algorithms are illustrated by numerical results.
SHADOWS OF OUR FORMER COMPANIONS: HOW THE SINGLE-DEGENERATE BINARY TYPE IA SUPERNOVA SCENARIO AFFECTS REMNANTS

Here we present three-dimensional high-resolution simulations of Type Ia supernova in the presence of a non-degenerate companion. We find that the presence of a nearby companion leaves a long-lived hole in the supernova ejecta. In particular, we aim to study the long-term evolution of this hole as the supernova ejecta interacts with the surrounding interstellar medium (ISM). Using estimates for the X-ray emission, we find that the hole generated by the companion remains for many centuries after the interaction between the ejecta and the ISM. We also show that the hole is discernible over a wide range of viewing angles and companion masses.
Integrability of N=6 Chern-Simons theory at six loops and beyond

We study issues concerning perturbative integrability of N=6 Chern-Simons theory at planar and weak 't Hooft coupling regime. By Feynman diagrammatics, we derive so-called maximal-ranged interactions in the quantum dilatation generator, originating from homogeneous and inhomogeneous diagrams. The dilatation operator requires proper regularization of ultraviolet and infrared divergences and also bears scheme dependence depending on operator-mixing or two-point function methods adopted. We first consider the standard operator-mixing method. We show that homogeneous diagrams are obtainable by recursive method to all orders. The method, however, is not easily extendable to inhomogeneous diagrams. We thus consider two-point function method and study both operator contents and the spectrum of the quantum dilatation generator up to six-loop orders. Within this scheme, we show that, of two possible classes of operators, only one linear combination actually contributes. Curiously, this is exactly the same combination as in N=4 super Yang-Mills theory. From these operators, we extract a spectrum of anomalous dimension up to six loops. We find that the spectrum agrees perfectly with the prediction based on quantum integrability. In evaluating the six-loop diagrams, we utilized a remarkable integer-relation algorithm developed by Ferguson, Baily, and Arno.
Entanglement entropy as a portal to the physics of quantum spin liquids

Quantum spin liquids (QSLs) are phases of interacting spins that do not order even at the absolute zero temperature, making it impossible to characterize them by a local order parameter. In this paper, we review the unique view provided by the quantum entanglement on QSLs. We illustrate the crucial role of topological entanglement entropy in diagnosing the non-local order in QSLs, using specific examples such as the chiral spin liquid. We also demonstrate the detection of anyonic quasi-particles and their braiding statistics using quantum entanglement. In the context of gapless QSLs, we discuss the detection of emergent fermionic spinons in a bosonic wavefunction, by studying the size dependence of entanglement entropy.
Interpreting the densities of the Kuiper belt's dwarf planets

Kuiper Belt objects with absolute magnitude less than 3 (radius $\gtrsim$500 km), the dwarf planets, have a range of different ice/rock ratios, and are more rock-rich than their smaller counterparts. Many of these objects have moons, which suggests that collisions may have played a role in modifying their compositions. We show that the dwarf planets fall into two categories when analysed by their mean densities and satellite-to-primary size ratio. Systems with large moons, such as Pluto/Charon and Orcus/Vanth, can form in low-velocity grazing collisions in which both bodies retain their compositions. We propose that these systems retain a primordial composition, with a density of about 1.8 g/cm$^3$. Triton, thought to be a captured KBO, could have lost enough ice during its early orbital evolution to explain its rock-enrichment relative to the primordial material. Systems with small moons, Eris, Haumea, and Quaoar, formed from a different type of collision in which icy material, perhaps a few tens of percent of the total colliding mass, is lost. The fragments would not remain in physical or dynamical proximity to the parent body. The ice loss process has not yet been demonstrated numerically, which could be due to the paucity of KBO origin simulations, or missing physical processes in the impact models. If our hypothesis is correct, we predict that large KBOs with small moons should be denser than the primordial material, and that the mean density of Orcus should be close to the primordial value.
Integrating Cross-modal Interactions via Latent Representation Shift for Multi-modal Humor Detection

Multi-modal sentiment analysis has been an active research area and has attracted increasing attention from multi-disciplinary communities. However, it is still challenging to fuse the information from different modalities in an efficient way. In prior studies, the late fusion strategy has been commonly adopted due to its simplicity and efficacy. Unfortunately, it failed to model the interactions across different modalities. In this paper, we propose a transformer-based hierarchical framework to effectively model both the intrinsic semantics and cross-modal interactions of the relevant modalities. Specifically, the features from each modality are first encoded via standard transformers. Later, the cross-modal interactions from one modality to other modalities are calculated using cross-modal transformers. The derived intrinsic semantics and cross-modal interactions are used to determine the latent representation shift of a particular modality. We evaluate the proposed approach on the MuSe-Humor sub-challenge of Multi-modal Sentiment Analysis Challenge (MuSe) 2022. Experimental results show that an Area Under the Curve (AUC) of 0.9065 can be achieved on the test set of MuSe-Humor. With the promising results, our best submission ranked first place in the sub-challenge.
All Deceptions are Not Alike: Bayesian Mechanism Design with Social Norm Against Lying

We say that a society has a weak norm against lying if, all other things being equal, agents try to avoid getting caught lying. We show that if this is the case, and it usually is, then Bayesian monotonicity is no longer a constraint in implementation and all incentive-compatible social-choice functions are Bayesian implementable. In contrast to the previous literature, our result does not rely on any kind of intrinsic lying aversion, on which the experimental evidence is mixed.
Machine Learning Based Framework for Fast Resource Estimation of RTL Designs Targeting FPGAs

Field-programmable gate arrays (FPGAs) have grown to be an important platform for integrated circuit design and hardware emulation. However, with the dramatic increase in design scale, it has become a key challenge to partition very large scale integration into multi-FPGA systems. Fast estimation of FPGA on-chip resource usage for individual sub-circuit blocks early in the circuit design flow will provide an essential basis for reasonable circuit partition. It will also help FPGA designers to tune the circuits in hardware description language. In this article, we propose a framework for fast estimation of the on-chip resources consumed by register transfer level (RTL) designs with machine learning methods. We extensively collect RTL designs as a dataset, extract features from the result of a parser tool and analyze their roles, and train a targeted three-stage ensemble learning model. A 5,513× speedup is achieved while having 27% relative absolute error. Although the effect is sufficient to support RTL circuit partition, we discuss how the estimation quality continues to be improved.
Virtual Reality Solutions Employing Artificial Intelligence Methods: A Systematic Literature Review

Although there are methods of artificial intelligence (AI) applied to virtual reality (VR) solutions, there are few studies in the literature. Thus, to fill this gap, we performed a systematic literature review of these methods. In this review, we apply a methodology proposed in the literature that locates existing studies, selects and evaluates contributions, analyses, and synthesizes data. We used Google Scholar and databases such as Elsevier's Scopus, ACM Digital Library, and IEEE Xplore Digital Library. A set of inclusion and exclusion criteria were used to select documents. The results showed that when AI methods are used in VR applications, the main advantages are high efficiency and precision of algorithms. Moreover, we observe that machine learning is the most applied AI scientific technique in VR applications. In conclusion, this paper showed that the combination of AI and VR contributes to new trends, opportunities, and applications for human-machine interactive devices, education, agriculture, transport, 3D image reconstruction, and health. We also concluded that the usage of AI in VR provides potential benefits in other fields of the real world such as teleconferencing, emotion interaction, tourist services, and image data extraction.
Improving Dimensional Emotion Recognition via Feature-wise Fusion

This paper introduces the solution of the RiHNU team for the MuSe-Stress sub-challenge as part of Multimodal Sentiment Analysis (MuSe) 2022. The MuSe-Stress is a task to discern human emotional states via internal or external responses (e.g., audio, physiological signal, and facial expression) in a job-interview setting. Multimodal learning is extensively considered an available approach for multimodal sentiment analysis tasks. However, most multimodal models fail to capture the association among each modality, resulting in limited generalizability. We argue that those methods are incapable of establishing discriminative features, mainly because they typically neglect fine-grained information. To address this problem, we first encode spatio-temporal features via a feature-wise fuse mechanism to learn more informative representations. Then we exploit the late fusion strategy to capture fine-grained relations between multiple modalities. The ensemble strategy is also used to enhance the final performance. Our method achieves CCC of 0.6803 and 0.6689 for valence and physiological arousal, respectively, on the test set.
Eco-activism and the State: the Situation in Russia in the Digital Age

The main trend of modern social development is determined by the fourth industrial revolution, the COVID-19 epidemic and urgent ecological problems. To solve the ecological question there must be synergy of technological innovations, economic improvement and legal support of changes and transformation ideas. This solving involves all public actors. This article considers civil participation and public ecological control in the digital age, as well as the participation of the state in their implementation on the empirical data on eco-activism in Russia: public monitoring of environmental emergencies by WWF Russia, Gazprom Space Systems, Norilsk Nickel Environmental and Climate Change Strategy, Russian Ecological Society, Change.org, Federal resource "Russian public initiative" and others. Analysis shows that two classical models of monitoring and environmental protection systems (“top-down” and “bottom-top”) couldn't be realized because of the state passivity and lack of the trust for the civil society. The most effective way of interaction seems the model "civil society + corporations - top". Also, Russia is taking first steps to legal support of eco-activities, but current legislation is still a framework for the future development which also includes: implementation of the necessary regulatory concepts that would correspond to a wide range of social and economic challenges; involvement of all interested parties, vulnerable groups and stakeholders in eco-projects; readiness to review state policy and flexibility.
Fabrication of nano-structured Titania - Thin-Films on Carbon-coated Nickel sheets

This research for Eco-materials focuses on the improvement of photo response of titania thin films. The goal of this research is to find a suitable substrate for decreasing the band-gap of titania, to improve the microstructure and to seek for new processing techniques in nano-meter scale. The sol-gel technique using titanium-tetra-iso-propoxide (TTIP), di-ethanol-amine (DEA) and ethanol was applied by using the dip-coating method. The substrate is a nickel-sheet covered with amorphous carbon-layers or carbon-nano-tubes (CNT), both produced with plasma- enhanced chemical vapor deposition (PECVD) by varying the temperature and the gas molecule velocity. The microstructure of these titania thin films show nanometer-structured pores, which are supposed to decrease the band gap according to the literature. Possible applications for photo catalytic devices will be discussed.
The Use of Faraday Rotation Sign Maps as a Diagnostic for Helical Jet Magnetic Fields

We present maps of the sign of the Faraday Rotation measure obtained from multi-frequency radio observations made with the Very Long Baseline Array (VLBA). The Active Galactic Nuclei (AGN) considered have B-field structures with a central "spine" of B-field orthogonal to the jet and/or a longitudinal B-field near one or both edges of the jet. This structure can plausibly be interpreted as being caused by a helical/toroidal jet magnetic field. Faraday Rotation is a rotation of the plane of polarization that occurs when the polarized radiation passes through a magnetized plasma. The sign of the RM is determined by the direction of the line-of-sight B-field in the region causing the Faraday Rotation, and an ordered toroidal or helical magnetic field associated with an AGN jet will thus produce a distinctive bilateral distribution of the RMs across the jet. We present and discuss RM-sign maps and their possible interpretation regarding the magnetic field geometries for several sources.
The Pairing of Accreting Massive Black Holes in Multiphase Circumnuclear Disks: the Interplay Between Radiative Cooling, Star Formation, and Feedback Processes

We study the orbital decay of a pair of massive black holes (BHs) with masses and 107 , using hydrodynamical simulations of circumnuclear disks (CNDs) with the alternating presence of sub-grid physics, such as radiative cooling, star formation, supernova feedback, BH accretion, and BH feedback. In the absence of such processes, the orbit of the secondary BH decays over timescales of to the center of the CND, where the primary BH resides. When strong dissipation operates in CNDs, fragmentation into massive objects the size of giant molecular clouds with densities in the range 104–107 amu cm−3 occurs, causing stochastic torques and hits that can eject the secondary BH from the midplane. Outside the plane, the low-density medium provides only weak drag, and the BH return is governed by inefficient dynamical friction. In rare cases, clump–BH interactions can lead to a faster decay. Feedback processes lead to outflows, but do not significantly change the overall density of the CND midplane. However, with a spherically distributed BH feedback, a hot bubble is generated behind the secondary, which almost shuts off dynamical friction. We dub this phenomenon “wake evacuation. ” It leads to delays in the decay, possibly of . We discuss the non-trivial implications on the discovery space of the eLISA telescope. Our results suggest that the largest uncertainty in predicting BH merger rates lies in the potentially wide variety of galaxy host systems, with different degrees of gas dissipation and heating, yielding decay timescales from to .
Effective one body description of tidal effects in inspiralling compact binaries

The late part of the gravitational wave signal of binary neutron-star (or black-hole-neutron-star) inspirals can in principle yield crucial information on the nuclear equation of state via its dependence on relativistic tidal parameters. In the hope of analytically describing the gravitational wave phasing during the late inspiral (essentially up to merger) we propose an extension of the effective one body (EOB) formalism which includes tidal effects. We compare the prediction of this tidal-EOB formalism to recently computed nonconformally flat quasiequilibrium circular sequences of binary neutron-star systems. Our analysis suggests the importance of higher-order (post-Newtonian) corrections to tidal effects, even beyond the first post-Newtonian order, and their tendency to significantly increase the 'effective tidal polarizability' of neutron stars. We propose to use the EOB description up to the moment where the tidally deformed compact objects formally enter 'into contact'. We compare the EOB predictions to some recently advocated, nonresummed, post-Newtonian based ('Taylor-T4') description of the phasing of inspiralling systems. This comparison shows the strong sensitivity of the late-inspiral phasing to the choice of the analytical model. A sufficiently accurate numerical-relativity - 'calibrated' EOB model might, however, give us a reliable analytical description of the late inspiral of compact binaries (and mightmore » also help in predicting the conditions necessary for the generation of short gamma-ray bursts). « less
CSM‐peptides: A computational approach to rapid identification of therapeutic peptides

Peptides are attractive alternatives for the development of new therapeutic strategies due to their versatility and low complexity of synthesis. Increasing interest in these molecules has led to the creation of large collections of experimentally characterized therapeutic peptides, which greatly contributes to development of data‐driven computational approaches. Here we propose CSM‐peptides, a novel machine learning method for rapid identification of eight different types of therapeutic peptides: anti‐angiogenic, anti‐bacterial, anti‐cancer, anti‐inflammatory, anti‐viral, cell‐penetrating, quorum sensing, and surface binding. Our method has shown to outperform existing approaches, achieving an AUC of up to 0.92 on independent blind tests, and consistent performance on cross‐validation. We anticipate CSM‐peptides to be of great value in helping screening large libraries to identify novel peptides with therapeutic potential and have made it freely available as a user‐friendly web server and Application Programming Interface at https://biosig.lab. uq.edu. au/csm_peptides.
Sur les fonctions thêta de deux variables et les surfaces Hyperelliptiques

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Elastic cloud computing for QoS-aware data processing

Infrastructure-as-a-Service (IaaS) clouds such as Amazon EC2 offer various types of virtual machines (VMs) through pay-per-use pricing. Elastic resource allocation allows us to allocate and release VMs as computing demand changes while satisfying Quality-of-Service (QoS) requirements. In this thesis, we explore QoS-aware elastic resource allocation for three different data processing models: batch, micro-batch, and streaming. First, we present two frameworks for elastic batch data processing. The first elastic batch data processing framework supports autonomous VM scaling using application-level migration. It does not require any prior knowledge about the target application, but dynamically reconfigures the application to keep the CPU utilization within a certain range. The second framework uses Workload-tailored Elastic Compute Units as a measure of computing resources analogous to Amazon EC2’s ECUs. Given a deadline, our framework finds the cost-optimal resource configuration of heterogeneous VMs to satisfy the required throughput. Next, we propose an elastic micro-batch data processing framework for continuous air traffic optimization. Air traffic optimization is commonly formulated as an integer linear programming (ILP) problem. For continuous optimization, we periodically solve ILP problems with regular intervals, where each problem is a micro-batch data processing job. Since the fluctuating number of flights creates dynamically changing computational demand, our framework predicts future workload and proactively schedules VMs to solve the ILP problems in a timely manner. Finally, we propose a framework for sustainable elastic stream processing based on the concept of Maximum Sustainable Throughput (MST). It is the maximum processing throughput a streaming application can process indefinitely for a number of VMs. Stream processing is sustainable if the system’s MST is always greater than the input data rates of incoming workload. Using MST and future workload prediction models, our framework proactively