Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Open-Set Recognition by Jacobian Norm of Representation

—In contrast to conventional closed-set recognition, open-set recognition (OSR) assumes the presence of an unknown class, which is not seen to a model during training. One predominant approach in OSR is metric learning, where a model is trained to separate the inter-class representations of known class data. Numerous works in OSR reported that, even though the models are trained only with the known class data, the models become aware of the unknown, and learn to separate the unknown class representations from the known class representations. This paper analyzes this emergent phenomenon by observing the Jacobian norm of representation. We theoretically show that minimizing the intra-class distances within the known set reduces the Jacobian norm of known class representations while maximizing the inter-class distances within the known set increases the Jacobian norm of the unknown class. The closed-set metric learning thus separates the unknown from the known by forcing their Jacobian norm values to differ. We empirically validate our theoretical framework with ample pieces of evidence using standard OSR datasets. Moreover, under our theoretical framework, we explain how the standard deep learning techniques can be helpful for OSR and use the framework as a guiding principle to develop an effective OSR model.
Semi-classical dust collapse and regular black holes

Semi-classical corrections at large curvature are employed in toy models of spherically symmetric gravitational collapse in order to avoid the formation of singularities. The resulting spacetimes may produce bounces, compact remnants or regular black holes in place of the usual Schwarzschild black hole. Within these models, a whole class of collapse scenarios leading to the formation of regular black holes may be obtained from General Relativity coupled to some theory of non-linear electrodynamics. In the present chapter we provide a thorough exposition of semi-classical dust collapse with particular attention to the conditions for the formation of regular black holes as the endstate of collapse.
The "Beatrix" Resurrections: Robust Backdoor Detection via Gram Matrices

Deep Neural Networks (DNNs) are susceptible to backdoor attacks during training. The model corrupted in this way functions normally, but when triggered by certain patterns in the input, produces a predefined target label. Existing defenses usually rely on the assumption of the universal backdoor setting in which poisoned samples share the same uniform trigger. However, recent advanced backdoor attacks show that this assumption is no longer valid in dynamic backdoors where the triggers vary from input to input, thereby defeating the existing defenses. In this work, we propose a novel technique, Beatrix (backdoor detection via Gram matrix). Beatrix utilizes Gram matrix to capture not only the feature correlations but also the appropriately high-order information of the representations. By learning class-conditional statistics from activation patterns of normal samples, Beatrix can identify poisoned samples by capturing the anomalies in activation patterns. To further improve the performance in identifying target labels, Beatrix leverages kernel-based testing without making any prior assumptions on representation distribution. We demonstrate the effectiveness of our method through extensive evaluation and comparison with state-of-the-art defensive techniques. The experimental results show that our approach achieves an F1 score of 91.1% in detecting dynamic backdoors, while the state of the art can only reach 36.9%.
Forming hot subluminous stars from hierarchical triples - I. The role of an outer tertiary on formation channels

We present evolutionary pathways for creating hot subdwarf OB (sdOB) stars from hierarchical triple configurations. We use the population synthesis code Multiple Stellar Evolution (MSE) to follow the stellar, binary, and gravitational dynamical evolution of triple-star systems. To ascertain the effect of the outer tertiary, we also consider the evolution of the inner binary with the tertiary component removed. We find we are able to create sdOB stars in single, binary and triple configurations. We also demonstrate that it is possible to form sdOBs in systems which undergo triple common envelope evolution, when the tertiary star undergoes unstable mass transfer on to the inner binary. We are unable to create single or wide sdOB systems without involving a merger earlier in the evolution. The triples can produce sdOBs in binaries with wide, non-interacting companions through binary interactions, which is impossible in isolated binaries. Owing to the closeness of the inner binary in hierarchical triples the formation channels associated with stable mass transfer are suppressed when compared to the isolated binary case.
Jensen-Shannon Divergence Based Loss Functions for Bayesian Neural Networks

The Kullback-Leibler (KL) divergence is widely used for the variational inference of Bayesian Neural Networks (BNNs) to approximate the posterior distribution of weights. However, the KL divergence is unbounded and asymmetric, which may lead to instabilities during optimization or may yield poor generalizations. To overcome these limitations, we examine the Jensen-Shannon (JS) divergence that is more general, bounded, and symmetric. Towards this, we propose two novel loss functions for BNNs: 1) a geometric JS divergence (JS-G) based loss function that is symmetric but unbounded with closed-form expression for Gaussian priors and 2) a generalized JS divergence (JS-A) based loss function that is symmetric and bounded. We show that the conventional KL divergence-based loss function is a special case of the loss functions presented in this work. To evaluate the divergence part of the proposed JS-G-based loss function, we use an exact closed-form expression for Gaussian priors. For any other priors of JS-G and for the JS-A-based loss function we use Monte Carlo approximation. We provide algorithms to optimize the loss function using both these methods. The proposed loss functions o ﬀ er additional parameters that can be tuned to control the regularisation. We explain the reason why the proposed loss functions should perform better than the state-of-the-art. Further, we derive the conditions under which the proposed JS-G-loss function regularises better than the KL divergence-based loss function for Gaussian priors and posteriors. The proposed JS divergence-based Bayesian convolutional neural networks (BCNN) perform better than the state-of-the-art BCNN, which is shown for the classiﬁcation of the CIFAR data set having various degrees of noise and a biased
SEGUE-2 and APOGEE: Revealing the History of the Milky Way

The history of the Milky Way is encoded in the spatial distributions, kinematics, and chemical enrichment patterns of its resolved stellar populations. SEGUE-2 and APOGEE, two of the four surveys that comprise SDSS-III (the Sloan Digital Sky Survey III), will map these distributions and enrichment patterns at optical and infrared wavelengths, respectively. Using the existing SDSS spectrographs, SEGUE-2 will obtain spectra of 140,000 stars in selected high-latitude fields to a magnitude limit r ~ 19.5, more than doubling the sample of distant halo stars observed in the SDSS-II survey SEGUE (the Sloan Extension for Galactic Understanding and Exploration). With spectral resolution R ~ 2000 and typical S/N per pixel of 20-25, SEGUE and SEGUE-2 measure radial velocities with typical precision of 5-10 km/s and metallicities ([Fe/H]) with a typical external error of 0.25 dex. APOGEE (the Apache Point Observatory Galactic Evolution Experiment) will use a new, 300-fiber H-band spectrograph (1.5-1.7 micron) to obtain high-resolution (R ~ 24,000), high signal-to-noise ratio (S/N ~ 100 per pixel) spectra of 100,000 red giant stars to a magnitude limit H ~ 12.5. Infrared spectroscopy penetrates the dust that obscures the inner Galaxy from our view, allowing APOGEE to carry out the first large, homogeneous spectroscopic survey of all Galactic stellar populations. APOGEE spectra will allow radial velocity measurements with < 0.5 km/s precision and abundance determinations (with ~ 0.1 dex precision) of 15 chemical elements for each program star, which can be used to reconstruct the history of star formation that produced these elements. (abridged)
Social Robot Scenarios for Real-World Child and Family Care Settings through Participatory Design

This paper discusses a 5-year PhD project, focused upon the implementation of social robots for general child and family care settings in the Netherlands. The project is a collaboration with general Dutch family care organisations as well as specialized child mental health care organisations. The project adapts a bottom-up, participatory design approach, where end users are included in all stages of the project. End users consist of children, parents, and family care professionals, who all have different needs, regarding the social robot behaviors as well as the participatory design methods. This paper provides suggestions to deal with these differences in designing social robots for child mental support in real-world settings.
Differentially private partitioned variational inference

Learning a privacy-preserving model from sensitive data which are distributed across multiple devices is an increasingly important problem. The problem is often formulated in the federated learning context, with the aim of learning a single global model while keeping the data distributed. Moreover, Bayesian learning is a popular approach for modelling, since it naturally supports reliable uncertainty estimates. However, Bayesian learning is generally intractable even with centralised non-private data and so approximation techniques such as variational inference are a necessity. Variational inference has recently been extended to the non-private federated learning setting via the partitioned variational inference algorithm. For privacy protection, the current gold standard is called differential privacy. Differential privacy guarantees privacy in a strong, mathematically clearly defined sense. In this paper, we present differentially private partitioned variational inference, the first general framework for learning a variational approximation to a Bayesian posterior distribution in the federated learning setting while minimising the number of communication rounds and providing differential privacy guarantees for data subjects. We propose three alternative implementations in the general framework, one based on perturbing local optimisation runs done by individual parties, and two based on perturbing updates to the global model (one using a version of federated averaging, the second one adding virtual parties to the protocol), and compare their properties both theoretically and empirically.
H I study of extremely metal-deficient dwarf galaxies - I. The Nançay radio telescope observations of twenty-two objects

Aims. The goal of this study is to measure parameters of the integrated $\ion{H}{i}$ emission for twenty-two dwarf galaxies with oxygen abundance 12+$\log$ (O/H) in the range of 7.42 to 7.65, which are representatives of the eXtremely Metal-Deficient (XMD) galaxy group. Some of them are expected to be similar to the well-known candidates for local young galaxies, I Zw 18 and SBS 0335-052 that have most of their baryon mass in the form of neutral gas. Therefore, the $\ion{H}{i}$ 21-cm line observations are crucial to understanding their group and individual properties. Methods. The Nancay Radio Telescope (NRT) with the upgraded focal receiver was used for observations of the 21-cm $\ion{H}{i}$-line. This permitted the detection of the faintest sources with rms of ~1 mJy per 10.5 km s -1 resolution element. Results. For eighteen detected galaxies we present the parameters of their integrated $\ion{H}{i}$ line emission and describe the data on individual objects in more detail. For four undetected XMD galaxies, we give upper limits on their M ($\ion{H}{i}$). For 70% of the twenty studied non-LSB (low surface brightness) XMD galaxies, we find evidence (both from $\ion{H}{i}$ and optical data) for their interaction with neighboring objects. In the brief discussion of the group $\ion{H}{i}$ properties of the observed subsample (the total O/H range is of 0.23 dex, or a factor of 1.7), we underline the broad distributions of the $\ion{H}{i}$ mass (range is of 2 orders of magnitude), of the ratio M ($\ion{H}{i}$)/ L B (of 1 order of magnitude), and of the blue luminosity (range is of 2 orders of magnitude). We also obtained $\ion{H}{i}$ parameters of six galaxies that do not belong to the XMD sample. As a by-product, we have detected an $\ion{H}{i}$-object with $V_{\rm hel} =$188 km s -1 , which is probably a part of a new high-velocity $\ion{H}{i}$ cloud (HVC 234.3-16.8+208) near our Galaxy, in the direction of AM 0624–261. Conclusions. These data increase the number of XMD galaxies with known integrated $\ion{H}{i}$ parameters (or upper limits) by a factor of two. This allows us to address statistical properties of this group, which will be presented in a forthcoming paper.
Stochastic sensing of polynucleotides using patterned nanopores

The effect of the microscopic structure of a pore on polymer translocation is studied using Langevin dynamics simulation, and the consequence of introducing patterned stickiness inside the pore is investigated. It is found that the translocation process is extremely sensitive to the detailed structure of such patterns with faster than exponential dependence of translocation times on the stickiness of the pore. The stochastic nature of the translocation process leads to discernable differences between how polymers with different sequences go through specifically patterned pores. This notion is utilized to propose a stochastic sensing protocol for polynucleotides, and it is demonstrated that the method, which would be significantly faster than the existing methods, could be made arbitrarily robust.
Facilitating Change Implementation for Continuous ML-Safety Assurance

We propose a method for deploying a safety-critical machine-learning component into continuously evolving environments where an increased degree of automation in the engineering process is desired. We associate semantic tags with the safety case argumentation and turn each piece of evidence into a quantitative metric or a logic formula. With proper tool support, the impact can be characterized by a query over the safety argumentation tree to highlight evidence turning invalid. The concept is exemplified using a vision-based emergency braking system of an autonomous guided vehicle for factory automation.
Sensing Aided OTFS Channel Estimation for Massive MIMO Systems

—Orthogonal time frequency space (OTFS) modula- tion has the potential to enable robust communications in highly-mobile scenarios. Estimating the channels for OTFS systems, however, is associated with high pilot signaling overhead that scales with the maximum delay and Doppler spreads. This becomes particularly challenging for massive MIMO systems where the overhead also scales with the number of antennas. An important observation however is that the delay, Doppler, and angle of departure/arrival information are directly related to the distance, velocity, and direction information of the mobile user and the various scatterers in the environment. With this motivation, we propose to leverage radar sensing to obtain this information about the mobile users and scatterers in the environment and leverage it to aid the OTFS channel estimation in massive MIMO systems. As one approach to realize this vision, this paper formulates the OTFS channel estimation problem in massive MIMO systems as a sparse recovery problem and utilizes the radar sensing information to determine the support (locations of the non-zero delay-Doppler taps). The proposed radar sensing aided sparse recovery algorithm is evaluated based on an accurate 3D ray-tracing framework with co-existing radar and communication data. The results show that the developed sensing-aided solution consistently outperforms the standard sparse recovery algorithms (that do not leverage radar sensing data) and leads to a signiﬁcant reduction in the pilot overhead, which highlights a promising direction for OTFS based massive MIMO systems.
Online Ridesharing with Meeting Points

Nowadays, ridesharing becomes a popular commuting mode. Dynamically arriving riders post their origins and destinations, then the platform assigns drivers to serve them. In ridesharing, different groups of riders can be served by one driver if their trips can share common routes. Recently, many ridesharing companies (e.g., Didi and Uber) further propose a new mode, namely "ridesharing with meeting points". Specifically, with a short walking distance but less payment, riders can be picked up and dropped off around their origins and destinations, respectively. In addition, meeting points enables more flexible routing for drivers, which can potentially improve the global profit of the system. In this paper, we first formally define the Meeting-Point-based Online Ridesharing Problem (MORP). We prove that MORP is NP-hard and there is no polynomial-time deterministic algorithm with a constant competitive ratio for it. We notice that a structure of vertex set, k -skip cover, fits well to the MORP. k -skip cover tends to find the vertices (meeting points) that are convenient for riders and drivers to come and go. With meeting points, MORP tends to serve more riders with these convenient vertices. Based on the idea, we introduce a convenience-based meeting point candidates selection algorithm. We further propose a hierarchical meeting-point oriented graph (HMPO graph), which ranks vertices for assignment effectiveness and constructs k -skip cover to accelerate the whole assignment process. Finally, we utilize the merits of k -skip cover points for ridesharing and propose a novel algorithm, namely SMDB, to solve MORP. Extensive experiments on real and synthetic datasets validate the effectiveness and efficiency of our algorithms.
Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes

Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. Industrial processes are a domain where predicitve models are needed for finding anomalous data instances for quality enhancement. A main challenge, however, is absence of labels in this environment. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production. With a use case from additive manufacturing for automotive components we present a deep-learning-based image processing pipeline. Additionally, we integrate the concept of domain randomisation and synthetic data in the loop that shows promising results for bridging advances in deep learning and its application to real-world, industrial production processes.
Balancing SRPT and FCFS via Starvation Mitigation

The assessment of a job's Quality of Service (QoS) often revolves around its flow time, also referred to as response time. This study delves into two fundamental objectives for scheduling jobs: the average flow time and the maximum flow time. While the Shortest Remaining Processing Time (SRPT) algorithm minimizes average flow time, it can result in job starvation, causing certain jobs to experience disproportionately long and unfair flow times. In contrast, the First-Come-First-Served (FCFS) algorithm minimizes the maximum flow time but may compromise the average flow time. To strike a balance between these two objectives, a common approach is to minimize the $\ell_2$ norm of flow time. In this study, we provide theoretical and experimental evidence that a judicious combination of SRPT and FCFS yields superior performance compared to standard heuristics for minimizing the $\ell_2$ norm of flow time.
Magnetically limited X-ray filaments in young SNR

We discuss the damping of strong magnetic turbulence downstream of the forward shock of young supernova remnants (SNR). We find that strong magnetic fields, that have been produced by the streaming instability in the upstream region of the shock, or by other kinetic instabilities at the shock, will be efficiently reduced, so the region of enhanced magnetic field strength would typically have a thickness of the order ld = (10 16 − 10 17 ) cm. The non-thermal X-ray filaments observed in young SNR are thus likely limited by the magnetic field and not by the energy losses of the radiating electrons. Consequently the thickness of the filaments would not be a measure of the magnetic field strength and claims of efficient cosmic-ray acceleration on account of a run-away streaming instability appear premature.
Atoms, Stars, and Nebulae

1. Introducing stars and nebulae 2. Stellar rainbows 3. Atoms and molecules 4. The climate in a stellar atmosphere 5. Analysing the stars 6. Dwarfs, giants, and supergiants 7. What makes a star shine? 8. The youth and middle age of a common star 9. Wind, dust and pulsations 10. A star's last hurray? 11. The interstellar medium and gaseous nebulae 12. Uncommon stars and their sometimes violent behaviour 13. High energy astronomy.
How do predisposing factors differ between delirium motor subtypes? A systematic review and meta-analysis

Abstract Background Delirium is a common neurocognitive disorder in hospitalised older adults with vast negative consequences. The predominant method of subtyping delirium is by motor activity profile into hypoactive, hyperactive and mixed groups. Objective This systematic review and meta-analysis investigated how predisposing factors differ between delirium motor subtypes. Methods Databases (Medline, PsycINFO, Embase) were systematically searched for studies reporting predisposing factors (prior to delirium) for delirium motor subtypes. A total of 61 studies met inclusion criteria (N = 14,407, mean age 73.63 years). Random-effects meta-analyses synthesised differences between delirium motor subtypes relative to 22 factors. Results Hypoactive cases were older, had poorer cognition and higher physical risk scores than hyperactive cases and were more likely to be women, living in care homes, taking more medications, with worse functional performance and history of cerebrovascular disease than all remaining subtypes. Hyperactive cases were younger than hypoactive and mixed subtypes and were more likely to be men, with better cognition and lower physical risk scores than all other subtypes. Those with no motor subtype (unable to be classified) were more likely to be women and have better functional performance. Effect sizes were small. Conclusions Important differences in those who develop motor subtypes of delirium were shown prior to delirium occurrence. We provide robust quantitative evidence for a common clinical assumption that indices of frailty (institutional living, cognitive and functional impairment) are seen more in hypoactive patients. Motor subtypes should be measured across delirium research. Motor subtyping has great potential to improve the clinical risk assessment and management of delirium.