Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Directed Spanners and Steiner Forests

We present online algorithms for directed spanners and Steiner forests. These problems fall under the unifying framework of online covering linear programming formulations, developed by Buchbinder and Naor (MOR, 34, 2009), based on primal-dual techniques. Our results include the following: For the pairwise spanner problem, in which the pairs of vertices to be spanned arrive online, we present an efficient randomized $\tilde{O}(n^{4/5})$-competitive algorithm for graphs with general lengths, where $n$ is the number of vertices. With uniform lengths, we give an efficient randomized $\tilde{O}(n^{2/3+\epsilon})$-competitive algorithm, and an efficient deterministic $\tilde{O}(k^{1/2+\epsilon})$-competitive algorithm, where $k$ is the number of terminal pairs. These are the first online algorithms for directed spanners. In the offline setting, the current best approximation ratio with uniform lengths is $\tilde{O}(n^{3/5 + \epsilon})$, due to Chlamtac, Dinitz, Kortsarz, and Laekhanukit (TALG 2020). For the directed Steiner forest problem with uniform costs, in which the pairs of vertices to be connected arrive online, we present an efficient randomized $\tilde{O}(n^{2/3 + \epsilon})$-competitive algorithm. The state-of-the-art online algorithm for general costs is due to Chakrabarty, Ene, Krishnaswamy, and Panigrahi (SICOMP 2018) and is $\tilde{O}(k^{1/2 + \epsilon})$-competitive. In the offline version, the current best approximation ratio with uniform costs is $\tilde{O}(n^{4/7 + \epsilon})$, due to Abboud and Bodwin (SODA 2018). A small modification of the online covering framework by Buchbinder and Naor implies a polynomial-time primal-dual approach with separation oracles, which a priori might perform exponentially many calls. We convert the online spanner problem and the online Steiner forest problem into online covering problems and round in a problem-specific fashion.
A Taxonomy of Similarity Metrics for Markov Decision Processes

Although the notion of task similarity is potentially interesting in a wide range of areas such as curriculum learning or automated planning, it has mostly been tied to transfer learning. Transfer is based on the idea of reusing the knowledge acquired in the learning of a set of source tasks to a new learning process in a target task, assuming that the target and source tasks are close enough. In recent years, transfer learning has succeeded in making Reinforcement Learning (RL) algorithms more efficient (e.g., by reducing the number of samples needed to achieve the (near-)optimal performance). Transfer in RL is based on the core concept of similarity: whenever the tasks are similar, the transferred knowledge can be reused to solve the target task and significantly improve the learning performance. Therefore, the selection of good metrics to measure these similarities is a critical aspect when building transfer RL algorithms, especially when this knowledge is transferred from simulation to the real world. In the literature, there are many metrics to measure the similarity between MDPs, hence, many definitions of similarity or its complement distance have been considered. In this paper, we propose a categorization of these metrics and analyze the definitions of similarity proposed so far, taking into account such categorization. We also follow this taxonomy to survey the existing literature, as well as suggesting future directions for the construction of new metrics.
Symmetry-protected gates of Majorana qubits in a high-$T_c$ superconductor platform

We propose a platform for braiding Majorana non-Abelian anyons based on a heterostructure between a d-wave high-Tc superconductor and a quantum spin-Hall insulator. It has been recently shown that such a setup for a quantum spin-Hall insulator leads to a pair of Majorana zero modes at each corner of the sample, and thus can be regarded as a higher-order topological superconductor. We show that upon applying a Zeeman field in the region, these Majorana modes split in space and can be manipulated for braiding processes by tuning the field and pairing phase. We show that such a setup can achieve full braiding, exchanging, and phase gates of the Majorana zero modes, all of which are robust and protected by symmetries. Our analysis naturally includes interaction effects and can be generalized to cases with fractional bulk excitations. As many of the ingredients of our proposed platform have been realized in recent experiments, our results provides a new route toward topological quantum computation.
A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAI

Explainable Artificial Intelligence (XAI) is a rising field in AI. It aims to produce a demonstrative factor of trust, which for human subjects is achieved through communicative means, which Machine Learning (ML) algorithms cannot solely produce, illustrating the necessity of an extra layer producing support to the model output. When approaching the medical field, we can see challenges arise when dealing with the involvement of human-subjects, the ideology behind trusting a machine to tend towards the livelihood of a human poses an ethical conundrum - leaving trust as the basis of the human-expert in acceptance to the machines decision. The aim of this paper is to apply XAI methods to demonstrate the usability of explainable architectures as a tertiary layer for the medical domain supporting ML predictions and human-expert opinion, XAI methods produce visualization of the feature contribution towards a given models output on both a local and global level. The work in this paper uses XAI to determine feature importance towards high-dimensional data-driven questions to inform domain-experts of identifiable trends with a comparison of model-agnostic methods in application to ML algorithms. The performance metrics for a glass-box method is also provided as a comparison against black-box capability for tabular data. Future work will aim to produce a user-study using metrics to evaluate human-expert usability and opinion of the given models.
Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot

Machine learning is now playing important role in robotic object manipulation. In addition, force control is necessary for manipulating various objects to achieve robustness against perturbations of configurations and stiffness. The au-thor's group revealed that fast and dynamic object manipulation with force control can be obtained by bilateral control-based imitation learning. However, the method is applicable only in robots that can control torque, while it is not applicable in robots that can only follow position or velocity commands like many commercially available robots. Then, in this research, a way to implement bilateral control-based imitation learning to velocity-controlled robots is proposed. The validity of the proposed method is experimentally verified by a mopping task.
Phonon-number resolution of voltage-biased mechanical oscillators with weakly anharmonic superconducting circuits

Observing quantum phenomena in macroscopic objects, and the potential discovery of a fundamental limit in the applicability of quantum mechanics, has been a central topic of modern experimental physics. Highly coherent and heavy micro-mechanical oscillators controlled by superconducting circuits are a promising system for this task. Here, we focus in particular on the electrostatic coupling of motion to a weakly anharmonic circuit, namely the transmon qubit. In the case of a megahertz mechanical oscillator coupled to a gigahertz transmon, we explain the difficulties in bridging the large electro-mechanical frequency gap. To remedy this issue, we explore the requirements to reach phonon-number resolution in the resonant coupling of a megahertz transmon and a mechanical oscillator.
Quantifying the mobility of chromatin during embryogenesis: Nuclear size matters

Chromatin moves dynamically inside the cell nucleus, and its motion is often correlated with gene functions such as DNA recombination and transcription. A recent study has shown that during early embryogenesis of the nematode, Caenorhabiditis elegans, the chromatin motion markedly decreases. However, the underlying mechanism for this transition has yet to be elucidated. We systematically investigated the impact of nuclear size to demonstrate that it is indeed a decisive factor in chromatin mobility. To this end, we established a method to quantify chromatin motion inside the nucleus, while excluding the contribution of the movement of the nucleus itself, which allowed us to extract the intrinsic mean-squared displacement (iMSD) of individual chromosomal loci in moving nuclei from the correlated motion of two loci. We show that a simple theoretical description, which takes into account the topological constraints of chromatin polymers, can quantitatively describe the relationship between the nucleus size and the chromatin motion in vivo. Our results emphasize a regulatory role of nuclear size in restricting chromatin motion, and a generic polymer physics model plays a guiding role in capturing this essential feature.
Modeling the Locomotion of Articulated Soft Robots in Granular Medium

We introduce a numerical tool for modeling articulated soft robots that couples discrete differential geometry-based simulation of elastic rods, our model for the articulated structure, and other external forces. Parallel to simulations, we build an untethered robot testbed, in the granular medium, comprised of multiple flexible flagella that are rotated about an axis by a motor. Drag from the granules causes the flagella to deform and the deformed shape generates a net forward propulsion. External drag depends on the flagellar shape, while the change in flagellar shape is the result of the competition between the external loading and elastic forces. We find reasonable quantitative agreement between experiments and simulations. Owing to a rod-based kinematic representation of the robot, the simulation can run faster than real-time in some cases, and, therefore, we can use it as a design tool for this class of soft robots. We find that there is an optimal rotational speed at which maximum efficiency is achieved. Moreover, both experiments and simulations show that increasing the number of flagella from two to three decreases the speed of the robot. This indicates that our simulator is potentially applicable for unknown physics exploration. We also gain insight into the mechanics of granular medium - while resistive force theory can successfully describe the propulsion at low number of flagella, it fails when more flagella are added to the robot.
Learning to Continually Learn Rapidly from Few and Noisy Data

Neural networks suffer from catastrophic forgetting and are unable to sequentially learn new tasks without guaranteed stationarity in data distribution. Continual learning could be achieved via replay -- by concurrently training externally stored old data while learning a new task. However, replay becomes less effective when each past task is allocated with less memory. To overcome this difficulty, we supplemented replay mechanics with meta-learning for rapid knowledge acquisition. By employing a meta-learner, which \textit{learns a learning rate per parameter per past task}, we found that base learners produced strong results when less memory was available. Additionally, our approach inherited several meta-learning advantages for continual learning: it demonstrated strong robustness to continually learn under the presence of noises and yielded base learners to higher accuracy in less updates.
Volcano: Stateless Cache Side-channel Attack by Exploiting Mesh Interconnect

Cache side-channel attacks lead to severe security threats to the settings that a CPU is shared across users, e.g., in the cloud. The existing attacks rely on sensing the micro-architectural state changes made by victims, and this assumption can be invalidated by combining spatial (\eg, Intel CAT) and temporal isolation (\eg, time protection). In this work, we advance the state of cache side-channel attacks by showing stateless cache side-channel attacks that cannot be defeated by both spatial and temporal isolation. This side-channel exploits the timing difference resulted from interconnect congestion. Specifically, to complete cache transactions, for Intel CPUs, cache lines would travel across cores via the CPU mesh interconnect. Nonetheless, the mesh links are shared by all cores, and cache isolation does not segregate the traffic. An attacker can generate interconnect traffic to contend with the victim's on a mesh link, hoping that extra delay will be measured. With the variant delays, the attacker can deduce the memory access pattern of a victim program, and infer its sensitive data. Based on this idea, we implement Volcano and test it against the existing RSA implementations of JDK. We found the RSA private key used by a victim process can be partially recovered. In the end, we propose a few directions for defense and call for the attention of the security community.
Scaling behaviour of small-scale dynamos driven by Rayleigh–Bénard convection

Abstract A numerical investigation of convection-driven dynamos is carried out in the plane layer geometry. Dynamos with different magnetic Prandtl numbers $Pm$ are simulated over a broad range of the Rayleigh number $Ra$. The heat transport, as characterized by the Nusselt number $Nu$, shows an initial departure from the heat transport scaling of non-magnetic Rayleigh–Bénard convection (RBC) as the magnetic field grows in magnitude; as $Ra$ is increased further, the data suggest that $Nu$ grows approximately as $Ra^{2/7}$, but with a smaller prefactor in comparison with RBC. Viscous ($\epsilon _u$) and ohmic ($\epsilon _B$) dissipation contribute approximately equally to $Nu$ at the highest $Ra$ investigated; both ohmic and viscous dissipation approach a Reynolds-number-dependent scaling of the form $Re^a$, where $a \approx 2.8$. The ratio of magnetic to kinetic energy approaches a $Pm$-dependent constant as $Ra$ is increased, with the constant value increasing with $Pm$. The ohmic dissipation length scale depends on $Ra$ in such a way that it is always smaller, and decreases more rapidly with increasing $Ra$, than the viscous dissipation length scale for all investigated values of $Pm$.
Let's be friends! A rapport-building 3D embodied conversational agent for the Human Support Robot

Partial subtle mirroring of nonverbal behaviors during conversations (also known as mimicking or parallel empathy), is essential for rapport building, which in turn is essential for optimal human-human communication outcomes. Mirroring has been studied in interactions between robots and humans, and in interactions between Embodied Conversational Agents (ECAs) and humans. However, very few studies examine interactions between humans and ECAs that are integrated with robots, and none of them examine the effect of mirroring nonverbal behaviors in such interactions. Our research question is whether integrating an ECA able to mirror its interlocutor's facial expressions and head movements (continuously or intermittently) with a human-service robot will improve the user's experience with the support robot that is able to perform useful mobile manipulative tasks (e.g. at home). Our contribution is the complex integration of an expressive ECA, able to track its interlocutor's face, and to mirror his/her facial expressions and head movements in real time, integrated with a human support robot such that the robot and the agent are fully aware of each others', and of the users', nonverbals cues. We also describe a pilot study we conducted towards answering our research question, which shows promising results for our forthcoming larger user study.
Causal Reinforcement Learning: An Instrumental Variable Approach

In the standard data analysis framework, data is first collected (once for all), and then data analysis is carried out. Moreover, the data-generating process is typically assumed to be exogenous. This approach is natural when the data analyst has no impact on how the data is generated. The advancement of digital technology, however, has facilitated firms to learn from data and make decisions at the same time. As these decisions generate new data, the data analyst---a business manager or an algorithm---also becomes the data generator. In this article, we formulate the problem as a Markov decision process (MDP) and show that the interaction generates a new type of bias---reinforcement bias---that exacerbates the endogeneity problem in static data analysis. When the data are independent and identically distributed, we embed the instrumental variable (IV) approach in the stochastic gradient descent algorithm to correct for the bias. For general MDP problems, we propose a class of IV-based reinforcement learning (RL) algorithms to correct for the bias. We establish asymptotic properties of the algorithms by incorporating them into two-timescale stochastic approximation (SA). Our formulation requires unbounded state space and more importantly, Markovian noise. Therefore, standard techniques in RL and SA literature, which rely on boundedness of the state space and martingale-difference structure of the noise, do not apply. We develop new techniques to establish finite-time risk bounds, finite-time bounds for trajectory stability, and asymptotic distribution of a class of IV-RL algorithms.
Modeling Web Browsing Behavior across Tabs and Websites with Tracking and Prediction on the Client Side

Clickstreams on individual websites have been studied for decades to gain insights into user interests and to improve website experiences. This paper proposes and examines a novel sequence modeling approach for web clickstreams, that also considers multi-tab branching and backtracking actions across websites to capture the full action sequence of a user while browsing. All of this is done using machine learning on the client side to obtain a more comprehensive view and at the same time preserve privacy. We evaluate our formalism with a model trained on data collected in a user study with three different browsing tasks based on different human information seeking strategies from psychological literature. Our results show that the model can successfully distinguish between browsing behaviors and correctly predict future actions. A subsequent qualitative analysis identified five common web browsing patterns from our collected behavior data, which help to interpret the model. More generally, this illustrates the power of overparameterization in ML and offers a new way of modeling, reasoning with, and prediction of observable sequential human interaction behaviors.
Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy

Randomized smoothing (RS) is an effective and scalable technique for constructing neural network classifiers that are certifiably robust to adversarial perturbations. Most RS works focus on training a good base model that boosts the certified robustness of the smoothed model. However, existing RS techniques treat every data point the same, i.e., the variance of the Gaussian noise used to form the smoothed model is preset and universal for all training and test data. This preset and universal Gaussian noise variance is suboptimal since different data points have different margins and the local properties of the base model vary across the input examples. In this paper, we examine the impact of customized handling of examples and propose Instance-wise Randomized Smoothing (Insta-RS) -- a multiple-start search algorithm that assigns customized Gaussian variances to test examples. We also design Insta-RS Train -- a novel two-stage training algorithm that adaptively adjusts and customizes the noise level of each training example for training a base model that boosts the certified robustness of the instance-wise Gaussian smoothed model. Through extensive experiments on CIFAR-10 and ImageNet, we show that our method significantly enhances the average certified radius (ACR) as well as the clean data accuracy compared to existing state-of-the-art provably robust classifiers.
PUBLIC POLICY AND THE CONDITIONAL VALUE OF HAPPINESS

This paper examines the increasingly popular view that new insights from the science of subjective well-being (SSWB) should play a prominent role in the determination of public policy. Though there are instrumental reasons for caring about societal happiness too, these political aspirations of the SSWB appear to be mostly intrinsically motivated. As the intrinsic value of happiness is endorsed across the political–philosophical spectrum, there is some initial plausibility to the expectation that it should not be too difficult to develop intrinsically motivated policies that can count on widespread support. This paper argues, however, that intrinsically motivated policies based on SSWB findings will always be highly controversial. This is because, although happiness is widely held to be intrinsically valuable, it is usually not deemed unconditionally valuable. By exploring the policy implications of three different views of this conditionality – happiness as a fitting response to the state of the world, authenticity, and merit – it is shown that different views of the conditionality of the intrinsic value of happiness have widely diverging policy implications, which greatly undermines the political aspirations of the SSWB.
A non-invasive optical monitoring system for the multi-spectral determination of absorption changes in blood

The development of this photometric device is based on the realisation of a photoplethysmography measurement device developed for the German Space Agency DLR. It is well known in biomedical engineering that pulsatile changes of blood volume in tissue can be observed by measuring the transmission or the reflection of light. The non-invasive multi-spectral method described here is based on the radiation of monochromatic light, emitted by laser diodes in the range of 600 nm to 1400 nm, through an area of skin on the finger. After interaction with the tissue the transmitted light is detected non-invasively by photo-diodes. The method makes use of the intensity fluctuations caused by the pulse wave. The ratio between the peak to peak pulse amplitudes measured at different wavelengths and its dependence on the optical absorbability characteristics of human blood yields information on the blood composition. Deferrals between the proportions of haemoglobin and water in the intravasal volume should be detected photo-electrically by signal-analytic evaluation of the signals. The computed coefficients are used for the measurement and calculation of the arterial oxygenic saturation (SaO2) and the relative haemoglobin concentration change. Results of clinical measurements are presented for a de-oxygenation study with ICG-bolus injection (indocyanine green).
Development of dense films from Melia azedarach polysaccharides

The aim of this work was to prepare dense films from polysaccharides extracted from Melia azedarach (MA) to be used e.g. in agricultural industry. Crosslinking of MA films with glutaraldehyde (Glu) through immersion method were performed. Struc- tural features of films were elucidated by FTIR and XRD analysis. The influence of crosslinking in mechanical properties, water uptake and water vapor permeation was evaluated. Results showed neat Melia azedarach film presented high elongation and elastic modulus. Hydroxyl functional groups present in MA were reacted with glutaraldheyde to render a crosslinked matrix. It was observed through FTIR analysis that OH band reduces intensity due to the formation of acetal linkage, as the crosslinking time increases. XRD evidenced structural changes in MA arrangement with the crosslinking time. Chemical crosslinking during 12 h and 24 h rendered insoluble but less deformable films. Water uptake and water vapor permeability were reduced as the crosslinking time increased. V C 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 41624.