Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized solutions to opinion dynamics models with discontinuities

Social dynamics models may present discontinuities in the right-hand side of the dynamics for multiple reasons, including topology changes and quantization. Several concepts of generalized solutions for discontinuous equations are available in the literature and are useful to analyze these models. In this chapter, we study Caratheodory and Krasovsky generalized solutions for discontinuous models of opinion dynamics with state dependent interactions. We consider two definitions of "bounded confidence" interactions, which we respectively call metric and topological: in the former, individuals interact if their opinions are closer than a threshold; in the latter, individuals interact with a fixed number of nearest neighbors. We compare the dynamics produced by the two kinds of interactions, in terms of existence, uniqueness and asymptotic behavior of different types of solutions.
Ordered Reference Dependent Choice Xi Zhi “ RC ”

We study how violations of structural assumptions like expected utility and exponential discounting can be connected to reference dependent preferences with set-dependent reference points, even if behavior conforms with these assumptions when the reference is fixed. An axiomatic framework jointly and systematically relaxes general rationality (WARP) and structural assumptions to capture reference dependence across domains. It gives rise to a linear order that endogenously determines references points, which in turn determines the preference parameters for a choice problem. Its generality allows us to study risk, time, and social preferences collectively, where seemingly independent anomalies are interconnected through the lens of reference-dependent choice.
Stochastic Intervention for Causal Effect Estimation

Causal inference methods are widely applied in various decision-making domains such as precision medicine, optimal policy and economics. Central to these applications is the treatment effect estimation of intervention strategies. Current estimation methods are mostly restricted to the deterministic treatment, which however, is unable to address the stochastic space treatment policies. Moreover, previous methods can only make binary yes-or-no decisions based on the treatment effect, lacking the capability of providing fine-grained effect estimation degree to explain the process of decision making. In our study, we therefore advance the causal inference research to estimate stochastic intervention effect by devising a new stochastic propensity score and stochastic intervention effect estimator (SIE). Meanwhile, we design a customized genetic algorithm specific to stochastic intervention effect (Ge-SIO) with the aim of providing causal evidence for decision making. We provide the theoretical analysis and conduct an empirical study to justify that our proposed measures and algorithms can achieve a significant performance lift in comparison with state-of-the-art baselines.
The q-Gauss-Newton method for unconstrained nonlinear optimization

A q-Gauss-Newton algorithm is an iterative procedure that solves nonlinear unconstrained optimization problems based on minimization of the sum squared errors of the objective function residuals. Main advantage of the algorithm is that it approximates matrix of q-second order derivatives with the first-order q-Jacobian matrix. For that reason, the algorithm is much faster than q-steepest descent algorithms. The convergence of q-GN method is assured only when the initial guess is close enough to the solution. In this paper the influence of the parameter q to the non-linear problem solving is presented through three examples. The results show that the q-GD algorithm finds an optimal solution and speeds up the iterative procedure.
PyTouch: A Machine Learning Library for Touch Processing

With the increased availability of rich tactile sensors, there is an an equally proportional need for open-source and integrated software capable of efficiently and effectively processing raw touch measurements into high-level signals that can be used for control and decision-making. In this paper, we present PyTouch – the first machine learning library dedicated to the processing of touch sensing signals. PyTouch, is designed to be modular, easy-to-use and provides state-of-the-art touch processing capabilities as a service with the goal of unifying the tactile sensing community by providing a library for building scalable, proven, and performance-validated modules over which applications and research can be built upon. We evaluate PyTouch on real-world data from several tactile sensors on touch processing tasks such as touch detection, slip and object pose estimations. PyTouch is open-sourced at https://github.com/facebookresearch/pytouch.
Structurally Adaptive Multi-Derivative Regularization for Image Recovery from Sparse Fourier Samples

The importance of regularization has been well established in image reconstruction—which is the computational inversion of imaging forward model–with applications including deconvolution for microscopy, tomographic reconstruction, magnetic resonance imaging and so on. Originally, the primary role of the regularization was to stabilize the computational inversion of the imaging forward model against noise. However, a recent framework pioneered by Donoho and others, known as compressive sensing, brought the role of regularization beyond the stabilization of inversion. It established a possibility that regularization can recover full images from measurements that are highly undersampled. However, it was observed that the quality of reconstruction yielded by compressive sensing methods fall abruptly when the under-sampling and/or measurement noise goes beyond certain threshold. Recently developed learning based methods are believed to outperform the compressive sensing methods without abrupt drop in the reconstruction quality under such imaging conditions. However, the need for training data limits their applicability. In this paper, we develop a regularization method that outperforms compressive sensing methods as well as selected learning-based methods, without any need for training data. The regularization is constructed as a spatially varying weighted sum of firstand canonical second-oder derivatives, with the weights determined to be adaptive to the image structure such that the attenuation of sharp image features—which is inevitable with use of any regularization—is minimized. We demonstrate the effectiveness of the proposed method by performing reconstruction on sparse Fourier samples simulated from a variety of MRI images.
Simulating the Galactic multi-messenger emissions with HERMES

The study of non-thermal processes such as synchrotron emission, inverse Compton scattering, bremsstrahlung and pion production is crucial to understand the properties of the Galactic cosmic-ray population, to shed light on their origin and confinement mechanisms, and to assess the significance of exotic signals possibly associated to new physics. We present a public code called HERMES aimed at generating sky maps associated to a variety of multi-messenger and multi-wavelength radiative processes, spanning from the radio domain all the way up to high-energy gamma-ray and neutrino production. We describe the physical processes under consideration, the code concept and structure, and the user interface, with particular focus on the python-based interactive mode. We especially present the modular and flexible design that allows to easily further extend the numerical package according to the user's needs. In order to demonstrate the capabilities of the code, we describe in detail a comprehensive set of sky maps and spectra associated to all physical processes included in the code. We comment in particular on the radio, gamma-ray, and neutrino maps, and mention the possibility to study signals stemming from dark matter annihilation. HERMES can be successfully applied to constrain the properties of the Galactic cosmic-ray population, improve our understanding of the diffuse Galactic radio, gamma--ray, and neutrino emission, and search for signals associated to particle dark matter annihilation or decay.
Hermetic chip-scale packaging using Au:Sn eutectic bonding for implantable devices

Advancements in miniaturisation and new capabilities of implantable devices impose a need for the development of compact, hermetic, and CMOS-compatible micro packaging methods. Gold-tin-based eutectic bonding presents the potential for achieving low-footprint seals with low permeability to moisture at process temperatures below 350 ∘C. This work describes a method for the deposition of Au:Sn eutectic alloy frames by sequential electroplating from commercially available solutions. Frames were bonded on the chip-level in the process of eutectic bonding. Bond quality was characterised through shear force measurements, scanning electron microscopy, visual inspection, and immersion tests. Characterisation of seals geometry, solder thickness, and bonding process parameters was evaluated, along with toxicity assessment of bonding layers to the human fibroblast cells. With a successful bond yield of over 70% and no cytotoxic effect, Au:Sn eutectic bonding appears as a suitable method for the protection of integrated circuitry in implantable applications.
SIMDRAM: An End-to-End Framework for Bit-Serial SIMD Computing in DRAM

Processing-using-DRAM has been proposed for a limited set of basic operations (i.e., logic operations, addition). However, in order to enable full adoption of processing-using-DRAM, it is necessary to provide support for more complex operations. In this paper, we propose SIMDRAM, a flexible general-purpose processing-using-DRAM framework that (1) enables the efficient implementation of complex operations, and (2) provides a flexible mechanism to support the implementation of arbitrary user-defined operations. The SIMDRAM framework comprises three key steps. The first step builds an efficient MAJ/NOT representation of a given desired operation. The second step allocates DRAM rows that are reserved for computation to the operation's input and output operands, and generates the required sequence of DRAM commands to perform the MAJ/NOT implementation of the desired operation in DRAM. The third step uses the SIMDRAM control unit located inside the memory controller to manage the computation of the operation from start to end, by executing the DRAM commands generated in the second step of the framework. We design the hardware and ISA support for SIMDRAM framework to (1) address key system integration challenges, and (2) allow programmers to employ new SIMDRAM operations without hardware changes. We evaluate SIMDRAM for reliability, area overhead, throughput, and energy efficiency using a wide range of operations and seven real-world applications to demonstrate SIMDRAM's generality. Using 16 DRAM banks, SIMDRAM provides (1) 88x and 5.8x the throughput, and 257x and 31x the energy efficiency, of a CPU and a high-end GPU, respectively, over 16 operations; (2) 21x and 2.1x the performance of the CPU and GPU, over seven real-world applications. SIMDRAM incurs an area overhead of only 0.2% in a high-end CPU.
Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Booster

We describe the offline machine learning (ML) development for an effort to precisely regulate the Gradient Magnet Power Supply (GMPS) at the Fermilab Booster accelerator complex via a Field-Programmable Gate Array (FPGA). As part of this effort, we created a digital twin of the Booster-GMPS control system by training a Long Short-Term Memory (LSTM) to capture its full dynamics. We outline the path we took to carefully validate our digital twin before deploying it as a reinforcement learning (RL) environment. Additionally, we demonstrate the use of a Deep Q-Network (DQN) policy model with the capability to regulate the GMPS against realistic time-varying perturbations.
Anomalous phase separation and hidden coarsening of super-clusters in the Falicov-Kimball model

We show that the celebrated Falicov-Kimball model exhibits rich and intriguing phase-ordering dynamics. Applying modern machine learning methods to enable large-scale quantum kinetic Monte Carlo simulations, we uncover an unusual phase-separation scenario in which the growth of charge checkerboard clusters competes with domain coarsening related to a hidden symmetry-breaking. A self-trapping mechanism as a result of this competition gives rise to arrested growth of checkerboard patterns and their super-clusters. Glassy behaviors similar to the one reported in this work could be generic for other correlated electron systems.
Robust Navigation for Racing Drones based on Imitation Learning and Modularization

This paper presents a vision-based modularized drone racing navigation system that uses a customized convolutional neural network (CNN) for the perception module to produce high-level navigation commands and then leverages a state-of-the-art planner and controller to generate low-level control commands, thus exploiting the advantages of both data- based and model-based approaches. Unlike the state-of-the-art method, which only takes the current camera image as the CNN input, we further add the latest three estimated drone states as part of the inputs. Our method outperforms the state-of-the-art method in various track layouts and offers two switchable navigation behaviors with a single trained network. The CNN-based perception module is trained to imitate an expert policy that automatically generates ground truth navigation commands based on the pre-computed global trajectories. Owing to the extensive randomization and our modified dataset aggregation (DAgger) policy during data collection, our navigation system, which is purely trained in simulation with synthetic textures, successfully operates in environments with randomly-chosen photo-realistic textures without further fine-tuning.
Multi-turn Dialog System on Single-turn Data in Medical Domain

Recently there has been a huge interest in dialog systems. This interest has also been developed in the field of the medical domain where researchers are focusing on building a dialog system in the medical domain. This research is focused on the multi-turn dialog system trained on the multi-turn dialog data. It is difficult to gather a huge amount of multi-turn conversational data in the medical domain that is verified by professionals and can be trusted. However, there are several frequently asked questions (FAQs) or single-turn QA pairs that have information that is verified by the experts and can be used to build a multi-turn dialog system.
Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval

With the need of fast retrieval speed and small memory footprint, document hashing has been playing a crucial role in large-scale information retrieval. To generate high-quality hashing code, both semantics and neighborhood information are crucial. However, most existing methods leverage only one of them or simply combine them via some intuitive criteria, lacking a theoretical principle to guide the integration process. In this paper, we encode the neighborhood information with a graph-induced Gaussian distribution, and propose to integrate the two types of information with a graph-driven generative model. To deal with the complicated correlations among documents, we further propose a tree-structured approximation method for learning. Under the approximation, we prove that the training objective can be decomposed into terms involving only singleton or pairwise documents, enabling the model to be trained as efficiently as uncorrelated ones. Extensive experimental results on three benchmark datasets show that our method achieves superior performance over state-of-the-art methods, demonstrating the effectiveness of the proposed model for simultaneously preserving semantic and neighborhood information.
Maria: A Visual Experience Powered Conversational Agent

Arguably, the visual perception of conversational agents to the physical world is a key way for them to exhibit the human-like intelligence. Image-grounded conversation is thus proposed to address this challenge. Existing works focus on exploring the multimodal dialog models that ground the conversation on a given image. In this paper, we take a step further to study image-grounded conversation under a fully open-ended setting where no paired dialog and image are assumed available. Specifically, we present Maria, a neural conversation agent powered by the visual world experiences which are retrieved from a large-scale image index. Maria consists of three flexible components, i.e., text-to-image retriever, visual concept detector and visual-knowledge-grounded response generator. The retriever aims to retrieve a correlated image to the dialog from an image index, while the visual concept detector extracts rich visual knowledge from the image. Then, the response generator is grounded on the extracted visual knowledge and dialog context to generate the target response. Extensive experiments demonstrate Maria outperforms previous state-of-the-art methods on automatic metrics and human evaluation, and can generate informative responses that have some visual commonsense of the physical world.
Oort cloud Ecology

Jan Hendrik Oort hypothesized the existence of a distant cloud of cometary objects that orbit the Sun based on a spike in the reciprocal orbital separation at 1∕a ≲ 10−4 au−1. The Oort cloud is the source of long-period comets, but has not been observed directly, and its origin remains theoretical. Theories on its origin evoke a sequence of events that have been tested individually but never as a consistent chronology. We present a chronology of the formation and early evolution of the Oort cloud, and test the sequence of events by simulating the formation process in subsequent amalgamated steps. These simulations start with the Solar System being born with planets and asteroids in a stellar cluster orbiting the Galactic center. Upon ejection from its birth environment, we continue to follow the evolution of the Solar System while it navigates the Galaxy as an isolated planetary system. We conclude that the range in semi-major axis between ~100 au and several ~103 au still bears the signatures of the Sun being born in a ≳1000 M⊙ pc−3 star cluster, and that most of the outer Oort cloud formed after the Solar System was ejected. The ejection of the Solar System, we argue, happened between ~20 Myr and 50 Myr after its birth. Trailing and leading trails of asteroids and comets along the Sun’s orbit in the Galactic potential are the by-product of the formation of the Oort cloud. These arms are composed of material that became unbound from the Solar System when the Oort cloud formed. Today, the bulk of the material in the Oort cloud (~70%) originates from the region in the circumstellar disk that was located between ~15 au and ~35 au, near the current location of the ice giants and the Centaur family of asteroids. According to our simulations, this population is eradicated if the ice-giant planets are born in orbital resonance. Planet migration or chaotic orbital reorganization occurring while the Solar System is still a cluster member is, according to our model, inconsistent with the presence of the Oort cloud. About half the inner Oort cloud, between 100 and 104 au, and a quarter of the material in the outer Oort cloud, ≳104 au, could be non-native to the Solar System but was captured from free-floating debris in the cluster or from the circumstellar disks of other stars in the birth cluster. Characterizing this population will help us to reconstruct the history of the Solar System.
Axonal transport of Hrs is activity-dependent and rate limiting for synaptic vesicle protein degradation

Turnover of synaptic vesicle (SV) proteins is vital for the maintenance of healthy and functional synapses. SV protein turnover is driven by neuronal activity in an ESCRT (endosomal sorting complex required for transport)-dependent manner. Here, we characterize a critical step in this process: axonal transport of ESCRT-0 component Hrs, necessary for sorting proteins into the ESCRT pathway and recruiting downstream ESCRT machinery to catalyze multivesicular body (MVB) formation. We find that neuronal activity stimulates the formation of presynaptic endosomes and MVBs, as well as the motility of Hrs+ vesicles in axons and their delivery to SV pools. Hrs+ vesicles co-transport ESCRT-0 component STAM1 and comprise a subset of Rab5+ vesicles, likely representing pro-degradative early endosomes. Furthermore, we identify kinesin motor protein KIF13A as essential for the activity-dependent transport of Hrs to SV pools and the degradation of SV membrane proteins. Together, these data demonstrate a novel activity- and KIF13A-dependent mechanism for mobilizing axonal transport of ESCRT machinery to facilitate the degradation of SV membrane proteins.
Trends in Managing Cardiac and Orthopaedic Device-Associated Infections by Using Therapeutic Biomaterials

Over the years, there has been an increasing number of cardiac and orthopaedic implanted medical devices, which has caused an increased incidence of device-associated infections. The surfaces of these indwelling devices are preferred sites for the development of biofilms that are potentially lethal for patients. Device-related infections form a large proportion of hospital-acquired infections and have a bearing on both morbidity and mortality. Treatment of these infections is limited to the use of systemic antibiotics with invasive revision surgeries, which had implications on healthcare burdens. The purpose of this review is to describe the main causes that lead to the onset of infection, highlighting both the biological and clinical pathophysiology. Both passive and active surface treatments have been used in the field of biomaterials to reduce the impact of these infections. This includes the use of antimicrobial peptides and ionic liquids in the preventive treatment of antibiotic-resistant biofilms. Thus far, multiple in vivo studies have shown efficacious effects against the antibiotic-resistant biofilm. However, this has yet to materialize in clinical medicine.
The Effects of Marine Algal Polyphenols, Phlorotannins, on Skeletal Muscle Growth in C2C12 Muscle Cells via Smad and IGF-1 Signaling Pathways

Skeletal muscle is an important tissue in energy metabolism and athletic performance. The use of effective synthetic supplements and drugs to promote muscle growth is limited by various side effects. Moreover, their use is prohibited by anti-doping agencies; hence, natural alternatives are needed. Therefore, we evaluated the muscle growth effect of substances that can act like synthetic supplements from edible marine algae. First, we isolated six marine algal polyphenols belonging to the phlorotannin class, namely dieckol (DK), 2,7″-phloroglucinol-6,6′-bieckol (PHB), phlorofucofuroeckol A (PFFA), 6,6′-bieckol (6,6-BK), pyrogallol-phloroglucinol-6,6′-bieckol (PPB), and phloroglucinol (PG) from an edible brown alga, Ecklonia cava and evaluated their effects on C2C12 myoblasts proliferation and differentiation. Of the six phlorotannin isolates evaluated, DK and PHB induced the highest degree of C2C12 myoblast proliferation. In addition, DK and PHB regulates myogenesis by down-regulating the Smad signaling, a negative regulator, and up-regulating the insulin-like growth factor-1 (IGF-1) signaling, a positive regulator. Interestingly, DK and PHB bind strongly to myostatin, which is an inhibitor of myoblast proliferation, while also binding to IGF-1 receptors. Moreover, they bind to IGF-1 receptor. These results suggest that DK and PHB are potential natural muscle building supplements and could be a safer alternative to synthetic drugs.