Miracle-dz/UniFAR-FTUs huggingface dataset:
ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA

Many unsupervised domain adaptation (UDA) methods have been proposed to bridge the domain gap by utilizing domain invariant information. Most approaches have chosen depth as such information and achieved remarkable success. Despite their effectiveness, using depth as domain invariant information in UDA tasks may lead to multiple issues, such as excessively high extraction costs and difficulties in achieving a reliable prediction quality. As a result, we introduce Edge Learning based Domain Adaptation (ELDA), a framework which incorporates edge information into its training process to serve as a type of domain invariant information. In our experiments, we quantitatively and qualitatively demonstrate that the incorporation of edge information is indeed beneficial and effective and enables ELDA to outperform the contemporary state-of-the-art methods on two commonly adopted benchmarks for semantic segmentation based UDA tasks. In addition, we show that ELDA is able to better separate the feature distributions of different classes. We further provide an ablation analysis to justify our design decisions.
Phenomenological Causality

Discussions on causal relations in real life often consider variables for which the definition of causality is unclear since the notion of interventions on the respective variables is obscure. Asking 'what qualifies an action for being an intervention on the variable X' raises the question whether the action impacted all other variables only through X or directly, which implicitly refers to a causal model. To avoid this known circularity, we instead suggest a notion of 'phenomenological causality' whose basic concept is a set of elementary actions. Then the causal structure is defined such that elementary actions change only the causal mechanism at one node (e.g. one of the causal conditionals in the Markov factorization). This way, the Principle of Independent Mechanisms becomes the defining property of causal structure in domains where causality is a more abstract phenomenon rather than being an objective fact relying on hard-wired causal links between tangible objects. We describe this phenomenological approach to causality for toy and hypothetical real-world examples and argue that it is consistent with the causal Markov condition when the system under consideration interacts with other variables that control the elementary actions.
Vacuum polarization induced coupling between Maxwell and Kalb-Ramond fields

We present here a manifestly gauge invariant calculation of vacuum polarization to fermions in the presence of a constant Maxwell and a constant Kalb-Ramond field in four dimensions. The formalism is a generalization of the one used by Schwinger in his famous paper on gauge invariance and vacuum polarization. We get an explicit expression for the vacuum polarization induced effective Lagrangian for a constant Maxwell field interacting with a constant Kalb-Ramond field. In the weak field limit we get the coupling between the Maxwell field and the Kalb-Ramond field to be $(\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{H}\ensuremath{\cdot}\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{F}{)}^{2},$ where ${H}_{\ensuremath{\mu}}=(1/3! ){\ensuremath{\epsilon}}_{\ensuremath{\mu}\ensuremath{\alpha}\ensuremath{\beta}\ensuremath{\lambda}}{H}^{\ensuremath{\alpha}\ensuremath{\beta}\ensuremath{\lambda}}$ and $\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{F}$ is the dual of ${F}_{\ensuremath{\mu}\ensuremath{\nu}}. $
Millimeter Wave Channel Estimation for Lens based Hybrid MIMO with Low Resolution ADCs

The high path loss associated with millimeter wave (mmWave) frequency communication can be compensated by large scale antenna arrays such as multiple-input multiple-output (MIMO) systems. The hybrid beamforming architecture which uses fewer radio frequency chains is implemented to reduce power consumption and hardware complexity, while still supporting multi-stream communication. We propose an efficient expectation-maximization (EM)-based mmWave channel estimator for a lens-based hybrid MIMO system with low resolution sampling at the receiver. The lens-based beamformer is investigated to provide increased antenna gain and reduced implementation complexity as the conventional beam selection network is excluded. Low resolution sampling at the analog-to-digital converters is implemented for reduced power consumption. The proposed solution with a robust maximum a posteriori estimator based on the EM algorithm performs better than the conventional EM approach and minimum mean square error baselines in medium to high signal-to-noise ratio regions.
Scalar Invariant Networks with Zero Bias

Just like weights, bias terms are the learnable parameters of many popular machine learning models, including neural networks. Biases are thought to enhance the representational power of neural networks, enabling them to solve a variety of tasks in computer vision. However, we argue that biases can be disregarded for some image-related tasks such as image classification, by considering the intrinsic distribution of images in the input space and desired model properties from first principles. Our findings suggest that zero-bias neural networks can perform comparably to biased networks for practical image classification tasks. We demonstrate that zero-bias neural networks possess a valuable property called scalar (multiplication) invariance. This means that the prediction of the network remains unchanged when the contrast of the input image is altered. We extend scalar invariance to more general cases, enabling formal verification of certain convex regions of the input space. Additionally, we prove that zero-bias neural networks are fair in predicting the zero image. Unlike state-of-the-art models that may exhibit bias toward certain labels, zero-bias networks have uniform belief in all labels. We believe dropping bias terms can be considered as a geometric prior in designing neural network architecture for image classification, which shares the spirit of adapting convolutions as the transnational invariance prior. The robustness and fairness advantages of zero-bias neural networks may also indicate a promising path towards trustworthy and ethical AI.
Data-pooling Reinforcement Learning for Personalized Healthcare Intervention

Motivated by the emerging needs of personalized preventative intervention in many healthcare applications, we consider a multi-stage, dynamic decision-making problem in the online setting with unknown model parameters. To deal with the pervasive issue of small sample size in personalized planning, we develop a novel data-pooling reinforcement learning (RL) algorithm based on a general perturbed value iteration framework. Our algorithm adaptively pools historical data, with three main innovations: (i) the weight of pooling ties directly to the performance of decision (measured by regret) as opposed to estimation accuracy in conventional methods; (ii) no parametric assumptions are needed between historical and current data; and (iii) requiring data-sharing only via aggregate statistics, as opposed to patient-level data. Our datapooling algorithm framework applies to a variety of popular RL algorithms, and we establish a theoretical performance guarantee showing that our pooling version achieves a regret bound strictly smaller than that of the no-pooling counterpart. We substantiate the theoretical development with empirically better performance of our algorithm via a case study in the context of post-discharge intervention to prevent unplanned readmissions, generating practical insights for healthcare management. In particular, our algorithm alleviates privacy concerns about sharing health data, which (i) opens the door for individual organizations to levering public datasets or published studies to better manage their own patients; and (ii) provides the basis for public policy makers to encourage organizations to share aggregate data to improve population health outcomes for the broader community.
Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations

Due to the huge amount of parameters, finetuning of pretrained language models (PLMs) is prone to overfitting in the low resource scenarios. In this work, we present a novel method that operates on the hidden representations of a PLM to reduce overfitting. During fine-tuning, our method inserts random autoencoders between the hidden layers of a PLM, which transform activations from the previous layers into multi-view compressed representations before feeding them into the upper layers. The autoencoders are plugged out after fine-tuning, so our method does not add extra parameters or increase computation cost during inference. Our method demonstrates promising performance improvement across a wide range of sequence- and token-level lowresource NLP tasks.
Neural Unsupervised Reconstruction of Protolanguage Word Forms

We present a state-of-the-art neural approach to the unsupervised reconstruction of ancient word forms. Previous work in this domain used expectation-maximization to predict simple phonological changes between ancient word forms and their cognates in modern languages. We extend this work with neural models that can capture more complicated phonological and morphological changes. At the same time, we preserve the inductive biases from classical methods by building monotonic alignment constraints into the model and deliberately underfitting during the maximization step. We evaluate our performance on the task of reconstructing Latin from a dataset of cognates across five Romance languages, achieving a notable reduction in edit distance from the target word forms compared to previous methods.
High-eccentricity Migration with Disk-induced Spin–Orbit Misalignment: A Preference for Perpendicular Hot Jupiters

High-eccentricity migration is a likely formation mechanism for many observed hot Jupiters, particularly those with a large misalignment between the stellar spin axis and orbital angular momentum axis of the planet. In one version of high-eccentricity migration, an inclined stellar companion excites von Zeipel–Lidov–Kozai (ZLK) eccentricity oscillations of a cold Jupiter, and tidal dissipation causes the planet’s orbit to shrink and circularize. Throughout this process, the stellar spin can evolve chaotically, resulting in highly misaligned hot Jupiters (HJs). Previous population studies of this migration mechanism have assumed that the stellar spin is aligned with the planetary orbital angular momentum when the companion begins to induce ZLK oscillations. However, in the presence of a binary companion, the star’s obliquity may be significantly excited during the dissipation of its protoplanetary disk. We calculate the stellar obliquities produced in the protoplanetary disk phase and use these to perform an updated population synthesis of ZLK-driven high-eccentricity migration with an F-type host star. We find that the resulting obliquity distribution of HJ systems is predominantly retrograde with a broad peak near 90°. The distribution we obtain has intriguing similarities to the recently observed preponderance of perpendicular planets close to their host stars.
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning

While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms to membership inference attacks. In such attacking systems, the adversary targets the set of collected input data on which the deep reinforcement learning algorithm has been trained. To address this gap, we propose an adversarial attack framework designed for testing the vulnerability of a state-of-the-art deep reinforcement learning algorithm to a membership inference attack. In particular, we design a series of experiments to investigate the impact of temporal correlation, which naturally exists in reinforcement learning training data, on the probability of information leakage. Moreover, we compare the performance of collective and individual membership attacks against the deep reinforcement learning algorithm. Experimental results show that the proposed adversarial attack framework is surprisingly effective at inferring data with an accuracy exceeding 84% in individual and 97% in collective modes in three different continuous control Mujoco tasks, which raises serious privacy concerns in this regard. Finally, we show that the learning state of the reinforcement learning algorithm influences the level of privacy breaches significantly.
On the chemodynamics of Wolf-Rayet ring nebulae : NGC 6888

A chemodynamical study of the W-R ring nebula NGC 6888 around the WN6 star WR 136 have been made. The observations combine, for the first time, high spatial and spectral resolution covering most of the optical range in different zones of the nebula. The spectra show an emission system with three different velocity components: (1) V(LSR) = -64 km/s, (2) V(LSR) = +78 km/s, and (3) V(LSR) = +18 km/s. Components (1) and (2) correspond to the blue and red-shifted parts of the shell, and (3) is probably associated to the ambient ionized gas
Constraining holographic cosmology using Planck data

Holographic cosmology offers a novel framework for describing the very early Universe in which cosmological predictions are expressed in terms of the observables of a three dimensional quantum field theory (QFT). This framework includes conventional slow-roll inflation, which is described in terms of a strongly coupled QFT, but it also allows for qualitatively new models for the very early Universe, where the dual QFT may be weakly coupled. The new models describe a universe which is non-geometric at early times. While standard slow-roll inflation leads to a (near-)power-law primordial power spectrum, perturbative superrenormalizable QFT's yield a new holographic spectral shape. Here, we compare the two predictions against cosmological observations. We use CosmoMC to determine the best fit parameters, and MultiNest for Bayesian Evidence, comparing the likelihoods. We find that the dual QFT should be non-perturbative at the very low multipoles (l ≥ 30$), while for higher multipoles (l ≤ 30$) the new holographic model, based on perturbative QFT, fits the data just as well as the standard power-law spectrum assumed in ΛCDM cosmology. This finding opens the door to applications of non-perturbative QFT techniques, such as lattice simulations, to observational cosmology on gigaparsec scales and beyond.
An algorithm to compute the Teichmueller polynomial from matrices

In their precedent work, the authors constructed closed oriented hyperbolic surfaces with pseudo-Anosov homeomorphisms from certain class of integral matrices. In this paper, we present a very simple algorithm to compute the Teichmueller polynomial corresponding to those surface homeomorphisms by first constructing an invariant track whose first homology group can be naturally identified with the first homology group of the surface, and computing its Alexander polynomial.
The universal $\ln^{2}s$ increase in total cross sections

While it has long been known that many models of high energy scattering give cross sections which rise as $\ln^{2}s$, the determination of the coefficient of this term is rarely given. We show that in gaussian and exponential eikonal models an exact expression for the cross section can be obtained, which demonstrates the $\ln^{2}s$ asyptoptic behaviour and determines its coefficient. The coefficient is universal, as found empirically, and the value of the constant obtained from the gaussian model is in good agreement with the empirical value.
Bouncing and emergent cosmologies from Arnowitt–Deser–Misner RG flows

Asymptotically safe gravity provides a framework for the description of gravity from the trans-Planckian regime to cosmological scales. According to this scenario, the cosmological constant and Newton’s coupling are functions of the energy scale whose evolution is dictated by the renormalization group (RG) equations. The formulation of the RG equations on foliated spacetimes, based on the Arnowitt–Deser–Misner (ADM) formalism, furnishes a natural way to construct the RG energy scale from the spectrum of the Laplacian operator on the spatial slices. Combining this idea with an RG improvement procedure, in this work we study quantum gravitational corrections to the Einstein–Hilbert action on Friedmann–Lemaître–Robertson–Walker backgrounds. The resulting quantum-corrected Friedmann equations can give rise to both bouncing cosmologies and emergent Universe solutions. Our bouncing models do not require the presence of exotic matter and emergent Universe solutions can be constructed for any allowed topology of the spatial slices.
Large-scale alpha^2-dynamo in low-mass stars and brown dwarfs

We develop a model based on three dimensional mean-field magnetohydrodynamics computations for the generation of large scale magnetic fields in fully convective objects like low-mass stars, brown dwarfs and possibly gaseous planets. The dynamo process is of α 2 type and thus differs from the shell-dynamo at work in more massive stars. The α 2 dynamo is found to become supercritical for Coriolis numbers Ω* ≥ 1, i.e. Rossby numbers Ro ≤ 10. It generates a large-scale, non-axisymmetric, steady field that is symmetric with respect to the equatorial plane. Saturation of the α 2 -generated field at the equipartition field strength yields strengths of several kiloGauss, in agreement with observations of active M dwarfs, and provides a qualitative explanation for the observed activity saturation in late M stars. For brown dwarfs with a conductive core, as occurs at the center of the most massive and oldest of these objects, we have also studied an α 2 Ω dynamo, i.e. the effect of differential rotation. In this case the field is predominantly toroidal, axisymmetric, and oscillatory, like the solar magnetic field. The topology of the field in the fully convective objects exhibits a high order multipole character that differs from the aligned dipole field generated by the an dynamo. The strong reduction of the dipolar component due to the non-axisymmetry of the field should considerably reduce the Alfven radius and thus the efficiency of magnetic braking, providing an appealing explanation for the decreasing angular momentum loss rate observed in low-mass stars and brown dwarfs. This may have also implications for cataclysmic variables below the period gap. In spite of this large-scale field, the decreasing conductivity in the dominantly neutral atmosphere of these objects may prevent the current generation necessary to support a chromosphere and thus activity. An observational signature of the present model would be (i) asymmetry of the chromospheric activity, contrary to the spatially uniform activity expected from small-scale turbulent dynamo and (ii) the absence of cycles in uniformly rotating (fully convective) low-mass objects.
Gyrokinetic Continuum Simulation of Turbulence in Open-Field-Line Plasmas

The properties of the boundary plasma in a tokamak are now recognized to play a key role in determining the achievable fusion power and the lifetimes of plasma-facing components. Accurate quantitative modeling and improved qualitative understanding of the boundary plasma ultimately require five-dimensional gyrokinetic turbulence simulations, which have been successful in predicting turbulence and transport in the core. The additional challenges of boundary-plasma simulation necessitate the development of new gyrokinetic codes or major modifications to existing core gyrokinetic codes. In this thesis, we develop the first gyrokinetic continuum code capable of simulating plasma turbulence on open magnetic field lines, which is a key feature of a tokamak scrape-off layer. In contrast to prior attempts at this problem, we use an energy-conserving discontinuous Galerkin discretization in space. To model the interaction between the plasma and the wall, we design conducting-sheath boundary conditions that permit local currents into and out of the wall. We start by designing spatially one-dimensional kinetic models of parallel SOL dynamics and solve these systems using novel continuum algorithms. By generalizing these algorithms to higher dimensions and adding a model for collisions, we present results from the first gyrokinetic continuum simulations of turbulence on two types of open-field-line systems. The first simulation features uniform and straight field lines, such as found in some linear plasma devices. The second simulation is of a hypothetical model we developed of the NSTX scrape-off layer featuring helical field lines. These developments comprise a major step towards a gyrokinetic continuum code for quantitative predictions of turbulence and transport in the boundary plasma of magnetic fusion devices.
Measurement of Rb and BR(b->l nu X) at LEP Using Double-Tag Methods

We present a combined measurement of R b = Γ(Z → b¯b) / Γ(Z → hadrons) and the semileptonic branching ratio of b quarks in Z decays, Br(b → ℓν X), using double-tag methods. Two analyses are performed on one million hadronic Z decays collected in 1994 and 1995. The ﬁrst analysis exploits the capabilities of the silicon microvertex detector. The tagging of b-events is based on the large impact parameter of tracks from weak b-decays with respect to the e + e − collision point. In the second analysis, a high- p t lepton tag is used to enhance the b-component in the sample and its momentum spectrum is used to constrain the model dependent uncertainties in the semileptonic b-decay. The analyses are combined in order to provide precise determinations of R b and Br(b → ℓν X):
Scenarios for Ultrafast Gamma-Ray Variability in AGN

We analyze three scenarios to address the challenge of ultrafast gamma-ray variability reported from active galactic nuclei. We focus on the energy requirements imposed by these scenarios: (i) external cloud in the jet, (ii) relativistic blob propagating through the jet material, and (iii) production of high-energy gamma-rays in the magnetosphere gaps. We show that while the first two scenarios are not constrained by the flare luminosity, there is a robust upper limit on the luminosity of flares generated in the black hole magnetosphere. This limit depends weakly on the mass of the central black hole and is determined by the accretion disk magnetization, viewing angle, and the pair multiplicity. For the most favorable values of these parameters, the luminosity for 5-minute flares is limited by , which excludes a black hole magnetosphere origin of the flare detected from IC 310. In the scopes of scenarios (i) and (ii), the jet power, which is required to explain the IC 310 flare, exceeds the jet power estimated based on the radio data. To resolve this discrepancy in the framework of scenario (ii), it is sufficient to assume that the relativistic blobs are not distributed isotropically in the jet reference frame. A realization of scenario (i) demands that the jet power during the flare exceeds by a factor 102 the power of the radio jet relevant to a timescale of 108 years.