Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical and near-infrared stellar activity characterization of the early M dwarf Gl 205 with SOPHIE and SPIRou

The stellar activity of M dwarfs is the main limitation for discovering and characterizing exoplanets orbiting them since it induces quasi-periodic RV variations. We aim to characterize the magnetic field and stellar activity of the early, moderately active, M dwarf Gl205 in the optical and nIR domains. We obtained high-precision quasi-simultaneous spectra in the optical and nIR with the SOPHIE spectrograph and SPIRou spectropolarimeter between 2019 and 2022. We computed the RVs from both instruments and the SPIRou Stokes V profiles. We used ZDI to map the large-scale magnetic field over the time span of the observations. We studied the temporal behavior of optical and nIR RVs and activity indicators with the Lomb-Scargle periodogram and a quasi-periodic GP regression. In the nIR, we studied the equivalent width of Al I, Ti I, K I, Fe I, and He I. We modeled the activity-induced RV jitter using a multi-dimensional GP regression with activity indicators as ancillary time series. The optical and nIR RVs have similar scatter but nIR shows a more complex temporal evolution. We observe an evolution of the magnetic field topology from a poloidal dipolar field in 2019 to a dominantly toroidal field in 2022. We measured a stellar rotation period of Prot=34.4$\pm$0.5 d in the longitudinal magnetic field. Using ZDI we measure the amount of latitudinal differential rotation (DR) shearing the stellar surface yielding rotation periods of Peq=32.0$\pm$1.8 d at the stellar equator and Ppol=45.5$\pm$0.3 d at the poles. We observed inconsistencies in the activity indicators' periodicities that could be explained by these DR values. The multi-dimensional GP modeling yields an RMS of the RV residuals down to the noise level of 3 m/s for both instruments, using as ancillary time series H$\alpha$ and the BIS in the optical, and the FWHM in the nIR.
Endpoint mixed weak type extrapolation

The purpose of this note is to extend the extrapolation result by by Cruz-Uribe Martell and P\'erez as follows. Given a family $\mathcal{F}$ of pairs of functions suppose that for some $0<p<\infty$ and for every $w\in A_{\infty}$ \begin{equation} \int f^{p}w\leq c_{w}\int g^{p}w\qquad(f,g)\in\mathcal{F}\label{eq:Hip-1} \end{equation} provided the left-hand side of the estimate is finite. If we have that $A(t)=t\log(e+t)^{\rho}$ for some $\rho>0$, then, for every $u\in A_{1}$ and every $v\in A_{\infty}$ we have that \[ \left\Vert \frac{f}{v}\right\Vert_{L^{A,\infty}(uv)}\lesssim\left\Vert \frac{g}{v}\right\Vert_{L^{A,\infty}(uv)}, \] where \[ L^{A,\infty}(uv)=\inf\left\{ \lambda>0:\sup_{t>0}A(t)w\left(\left\{ x\in\mathbb{R}:|f(x)|>\lambda t\right\} \right)\leq1\right\} \] is the weak Orlicz type introduced by Iaffei. As a corollary of this extrapolation result we derive a mixed weak type inequality for Coifman-Rochberg-Weiss commutators.
Implicit Shape Model Trees: Recognition of 3-D Indoor Scenes and Prediction of Object Poses for Mobile Robots

This article describes an approach for mobile robots to identify scenes in configurations of objects spread across dense environments. This identification is enabled by intertwining the robotic object search and the scene recognition on already detected objects. We proposed “Implicit Shape Model (ISM) trees” as a scene model to solve these two tasks together. This article presents novel algorithms for ISM trees to recognize scenes and predict object poses. For us, scenes are sets of objects, some of which are interrelated by 3D spatial relations. Yet, many false positives may occur when using single ISMs to recognize scenes. We developed ISM trees, which is a hierarchical model of multiple interconnected ISMs, to remedy this. In this article, we contribute a recognition algorithm that allows the use of these trees for recognizing scenes. ISM trees should be generated from human demonstrations of object configurations. Since a suitable algorithm was unavailable, we created an algorithm for generating ISM trees. In previous work, we integrated the object search and scene recognition into an active vision approach that we called “Active Scene Recognition”. An efficient algorithm was unavailable to make their integration using predicted object poses effective. Physical experiments in this article show that the new algorithm we have contributed overcomes this problem.
Certifiable Object Pose Estimation: Foundations, Learning Models, and Self-Training

In this article, we consider a <italic>certifiable</italic> object pose estimation problem, where—given a partial point cloud of an object—the goal is to not only estimate the object pose, but also provide a certificate of correctness for the resulting estimate. Our first contribution is a general theory of certification for end-to-end perception models. In particular, we introduce the notion of <italic><inline-formula><tex-math notation="LaTeX">$\zeta$</tex-math></inline-formula>-correctness</italic>, which bounds the distance between an estimate and the ground truth. We then show that <inline-formula><tex-math notation="LaTeX">$\zeta$</tex-math></inline-formula>-correctness can be assessed by implementing two certificates: 1) a certificate of <italic>observable correctness</italic>, which asserts if the model output is consistent with the input data and prior information; and 2) a certificate of <italic>nondegeneracy</italic>, which asserts whether the input data are sufficient to compute a unique estimate. Our second contribution is to apply this theory and design a new learning-based certifiable pose estimator. In particular, we propose <monospace>C-3PO</monospace>, a semantic-keypoint-based pose estimation model, augmented with the two certificates, to solve the certifiable pose estimation problem. <monospace>C-3PO</monospace> also includes a <italic>keypoint corrector</italic>, implemented as a differentiable optimization layer, that can correct large detection errors (e.g., due to the sim-to-real gap). Our third contribution is a novel self-supervised training approach that uses our certificate of observable correctness to provide the supervisory signal to <monospace>C-3PO</monospace> during training. In it, the model trains only on the observably correct input–output pairs produced in each batch and at each iteration. As training progresses, we see that the observably correct input–output pairs grow, eventually reaching near 100% in many cases. We conduct extensive experiments to evaluate the performance of the corrector, the certification, and the proposed self-supervised training using the ShapeNet and YCB datasets. The experiments show that 1) standard semantic-keypoint-based methods (which constitute the backbone of <monospace>C-3PO</monospace>) outperform more recent alternatives in challenging problem instances; 2) <monospace>C-3PO</monospace> further improves performance and significantly outperforms all the baselines; and 3) <monospace>C-3PO</monospace>’s certificates are able to discern correct pose estimates. <sup>1</sup>
Consensus Algorithm For Calculation Of Protein Binding Affinity Using Multiple Models

The major histocompatibility complex (MHC) molecules, which bind peptides for presentation on the cell surface, play an important role in cell-mediated immunity. In light of developing databases and technologies over the years, significant progress has been made in research on peptide binding affinity calculation. Several in techniques have been developed to predict peptide binding to MHC class I. Most of the research on MHC Class I due to its nature brings better performance and more. Considering the use of different methods and different technologies, and the approach of similar methods on different proteins, a classification was created according to the binding affinity of protein peptides. For this classification, MHC Class I was studied using the MHCflurry, NetMHCPan, NetMHC, NetMHCCons and ssmpmbec. In these simulations conducted within the scope of this thesis, no overall superiority was observed between the models. It has been determined that they are superior to each other in various points. Getting the best results may vary depending on the multiple uses of models. The important thing is to recognize the data and act with the appropriate model. But even that doesn’t make a huge difference. Since the consensus approach is directly related to the models, the better the models, the better. Xix
Disk Galaxies in the Magneticum Pathfinder Simulations

Abstract We present Magneticum Pathfinder, a new set of hydrodynamical cosmological simulations covering a large range of cosmological scales. Among the important physical processes included in the simulations are the chemical and thermodynamical evolution of the diffuse gas as well as the evolution of stars and black holes and the corresponding feedback channels. In the high resolution boxes aimed at studies of galaxy formation and evolution, populations of both disk and spheroidal galaxies are self-consistently reproduced. These galaxy populations match the observed stellar mass function and show the same trends for disks and spheroids in the mass–size relation as observations from the SDSS. Additionally, we demonstrate that the simulated galaxies successfully reproduce the observed specific angular-momentum–mass relations for the two different morphological types of galaxies. In summary, the Magneticum Pathfinder simulations are a valuable tool for studying the assembly of cosmic and galactic structures in the universe.
Huff-DP: Huffman Coding based Differential Privacy Mechanism for Real-Time Data

—With the advancements in connected devices, a huge amount of real-time data is being generated. Efﬁcient storage, transmission, and analysation of this real-time big data is important, as it serves a number of purposes ranging from decision making to fault prediction, etc. Alongside this, real- time big data has rigorous utility and privacy requirements, therefore, it is also signiﬁcantly important to choose the handling strategies meticulously. One of the optimal way to store and transmit data in the form of lossless compression is Huffman coding, which compresses the data into a variable length binary stream. Similarly, in order to protect the privacy of such big data, differential privacy is being used nowadays, which perturbs the data on the basis of privacy budget and sensitivity. Neverthe- less, traditional differential privacy mechanisms provide privacy guarantees. However, on the other hand, real-time data cannot be dealt as an ordinary set of records, because it usually has certain underlying patterns and cycles, which can be used for forming a link to a speciﬁc individuals’ private information that can lead to severe privacy leakages (e.g., analysing smart metering data can lead to classiﬁcation of individuals daily routine). Thus, it is equally important to develop a privacy preservation model, which preserves the privacy on the basis of occurrences and patterns in the data. In this paper, we design a novel Huffman coding based differential privacy budget selection mechanism (Huff-DP), which selects the optimal privacy budget on the basis of privacy requirement for that speciﬁc record. In order to further enhance the budget determination, we propose static, sine, and fuzzy logic based decision algorithms. Furthermore, we carried out extensive theoretical analysis alongside performance evaluation and com- parison on real-world datasets in order to show the effectiveness of our Huff-DP approach. From the experimental evaluations, it can be concluded that our proposed Huff-DP mechanism provides effective privacy protection alongside reducing the privacy budget computational cost.
Cross-lingual Argument Mining in the Medical Domain

Nowadays the medical domain is receiving more and more attention in applications in-volving Artiﬁcial Intelligence. Clinicians have to deal with an enormous amount of unstructured textual data to make a conclusion about patients’ health in their everyday life. Argument mining helps to provide a structure to such data by detecting argumentative components in the text and classifying the relations between them. However, as it is the case for many tasks in Natural Language Processing in general and in medical text processing in particular, the large majority of the work on computational argumentation has been done only for English. This is also the case with the only dataset available for argumentation in the medical domain, namely, the annotated medical data of abstracts of Randomized Controlled Trials (RCT) from the MEDLINE database. In order to mitigate the lack of annotated data for other languages, we empirically investigate several strategies to perform argument mining and classiﬁcation in medical texts for a language for which no annotated data is available. This project shows that automatically translating and project annotations from English to a target language (Spanish) is an eﬀective way to generate annotated data without manual intervention. Furthermore, our experiments demonstrate that the translation and projection approach outperforms zero-shot cross-lingual approaches using a large masked multilingual language model. Finally, we show how the automatically generated data in Spanish can also be used to improve results in the original English evaluation setting.
Current fluctuations in an interacting active lattice gas

We study the fluctuations of the integrated density current across the origin up to time T in a lattice model of active particles with hard-core interactions. This model is amenable to an exact description within a fluctuating hydrodynamics framework. We focus on quenched initial conditions for both the density and the magnetization fields and derive expressions for the cumulants of the density current, which can be matched with direct numerical simulations of the microscopic lattice model. For the case of uniform initial profiles, we show that the variance of the integrated current displays three regimes: an initial T rise with a coefficient given by the symmetric simple exclusion process, a cross-over regime where the effects of activity increase the fluctuations, and a large-time T behavior with a prefactor that depends on the initial conditions, the Péclet number, and the mean density of particles. Additionally, we study the limit of zero diffusion, where the fluctuations intriguingly exhibit a T 2 behavior at short times. However, at large times, the fluctuations still grow as T , with a coefficient that can be calculated explicitly. For low densities, we show that this coefficient can be expressed in terms of the effective diffusion constant D eff for non-interacting active particles.
Negative Acceleration and Adaptive Automation Approaches to Counter the Effect of Acceleration Induced Loss of Consciousness

Gravity-induced loss of consciousness (GLOC) is a major human factors problem facing fighter pilots today. A recent study by Tripp et al. (2006) reported that the GLOC episode consists of 24 sec of unconsciousness and confusion/disorientation followed by 55 sec in which performance efficiency is compromised. Hence, fighter pilots in the throes of GLOC can travel 12 miles while not in control of their aircraft. Using a centrifuge to simulate gravitational forces together with tracking and math tasks to simulate flight control and navigation, this study attacked the GLOC problem in two ways: (1) by employing negative acceleration following GLOC onset to decrease GLOC recovery time and (2) by assessing the utility of a of cerebral tissue oxygen saturation (rSO2) measure as a possible triggering mechanism for an adaptive automation recovery system. Negative acceleration had no significant effect on the duration of the GLOC episode. Declines in rSO2 from baseline pinpointed when pilots would cease active flight control and when GLOC would set in. Thus, the rSO2 measure may be of effective value as an adaptive automation GLOC recovery system trigger. The rSO2 measure also showed that loss of blood oxygen is not the sole factor underlying performance deficits in GLOC. Tissue oxygen saturation retuned to baseline levels shortly after the centrifuge came to a complete stop following GLOC onset but the performance deficits continued for 55 sec after rSO2 returned to baseline. Cleared for Public Release Distribution Unlimited
HoLA Robots: Mitigating Plan-Deviation Attacks in Multi-Robot Systems with Co-Observations and Horizon-Limiting Announcements

Emerging multi-robot systems rely on cooperation between humans and robots, with robots following automatically generated motion plans to service application-level tasks. Given the safety requirements associated with operating in proximity to humans and expensive infrastructure, it is important to understand and mitigate the security vulnerabilities of such systems caused by compromised robots who diverge from their assigned plans. We focus on centralized systems, where a *central entity* (CE) is responsible for determining and transmitting the motion plans to the robots, which report their location as they move following the plan. The CE checks that robots follow their assigned plans by comparing their expected location to the location they self-report. We show that this self-reporting monitoring mechanism is vulnerable to *plan-deviation attacks* where compromised robots don't follow their assigned plans while trying to conceal their movement by mis-reporting their location. We propose a two-pronged mitigation for plan-deviation attacks: (1) an attack detection technique leveraging both the robots' local sensing capabilities to report observations of other robots and *co-observation schedules* generated by the CE, and (2) a prevention technique where the CE issues *horizon-limiting announcements* to the robots, reducing their instantaneous knowledge of forward lookahead steps in the global motion plan. On a large-scale automated warehouse benchmark, we show that our solution enables attack prevention guarantees from a stealthy attacker that has compromised multiple robots.
A survey of Digital Manufacturing Hardware and Software Trojans

Digital Manufacturing (DM) refers to the on-going adoption of smarter, more agile manufacturing processes and cyber-physical systems. This includes modern techniques and technologies such as Additive Manufacturing (AM)/3D printing, as well as the Industrial Internet of Things (IIoT) and the broader trend toward Industry 4.0. However, this adoption is not without risks: with a growing complexity and connectivity, so too grows the cyber-physical attack surface. Here, malicious actors might seek to steal sensitive information or sabotage products or production lines, causing financial and reputational loss. Of particular concern are where such malicious attacks may enter the complex supply chains of DM systems as Trojans—malicious modifications that may trigger their payloads at later times or stages of the product lifecycle. In this work, we thus present a comprehensive overview of the threats posed by Trojans in Digital Manufacturing. We cover both hardware and software Trojans which may exist in products or their production and supply lines. From this, we produce a novel taxonomy for classifying and analyzing these threats, and elaborate on how different side channels (e.g. visual, thermal, acoustic, power, and magnetic) may be used to either enhance the impact of a given Trojan or utilized as part of a defensive strategy. Other defenses are also presented—including hardware, web-, and software-related. To conclude, we discuss seven different case studies and elaborate how they fit into our taxonomy. Overall, this paper presents a detailed survey of the Trojan landscape for Digital Manufacturing: threats, defenses, and the importance of implementing secure practices.
Rate-Perception Optimized Preprocessing for Video Coding

In the past decades, lots of progress have been done in the video compression field including traditional video codec and learning-based video codec. However, few studies focus on using preprocessing techniques to improve the rate-distortion performance. In this paper, we propose a rate-perception optimized preprocessing (RPP) method. We first introduce an adaptive Discrete Cosine Transform loss function which can save the bitrate and keep essential high frequency components as well. Furthermore, we also combine several state-of-the-art techniques from low-level vision fields into our approach, such as the high-order degradation model, efficient lightweight network design, and Image Quality Assessment model. By jointly using these powerful techniques, our RPP approach can achieve on average, 16.27% bitrate saving with different video encoders like AVC, HEVC, and VVC under multiple quality metrics. In the deployment stage, our RPP method is very simple and efficient which is not required any changes in the setting of video encoding, streaming, and decoding. Each input frame only needs to make a single pass through RPP before sending into video encoders. In addition, in our subjective visual quality test, 87% of users think videos with RPP are better or equal to videos by only using the codec to compress, while these videos with RPP save about 12% bitrate on average. Our RPP framework has been integrated into the production environment of our video transcoding services which serve millions of users every day.
Distilling Text into Circuits

Abstract This paper concerns the structure of meanings within natural language. In [13] a framework for natural language named DisCoCirc was sketched that has the following features: (1) it is both compositional and distributional (a.k.a. vectorial); (2) it applies to general text, not just sentences; (3) it captures linguistic ‘connections’ between meanings (cf. grammar); (4) word meanings get updated as text progresses; (5) sentence types reflect the words that have their meaning updated; (6) language ambiguity is naturally accommodated. In this paper, we realise DisCoCirc for a substantial fragment of English. We moreover show that when passing to DisCoCirc’s text circuits, some ‘grammatical bureaucracy’ is eliminated. That is, DisCoCirc displays a significant degree of: (7) interand intra-language independence. By inter-language independence we mean for example independence from word-order conventions that differ across languages, and by intra-language independence we mean for example independence from choices like using many short sentences versus few long sentences. This inter-language independence means our text circuits should carry over to other languages, unlike the language-specific typings of categorial grammars. Hence, text circuits are a lean structure for the ‘actual substance of text’, that is, the inner-workings of meanings within text across several layers of expressiveness (cf. words, sentences, text), and may capture that what is truly universal beneath grammar. The elimination of grammatical bureaucracy also explains why DisCoCirc: (8) applies beyond language, e.g. to spatial [76], visual [13, §4.7] and other cognitive modes. Indeed, while humans could not verbally communicate in terms of text circuits, machines can, and abstract text circuits can be conceived as linguistic structure tailored for machines. Concretely, we begin by defining a ‘hybrid grammar’ for a fragment of English this is a purposebuilt, minimal grammatical formalism needed to obtain text circuits. We then detail a translation process such that all text generated by this grammar yields a text circuit: