Miracle-dz/UniFAR-FTUs huggingface dataset:
On Robustness of Lane Detection Models to Physical-World Adversarial Attacks in Autonomous Driving

After the 2017 TuSimple Lane Detection Challenge, its evaluation based on accuracy and F1 score has become the de facto standard to measure the performance of lane detection methods. In this work, we conduct the first large-scale empirical study to evaluate the robustness of state-of-the-art lane detection methods under physical-world adversarial attacks in autonomous driving. We evaluate 4 major types of lane detection approaches with the conventional evaluation and end-to-end evaluation in autonomous driving scenarios and then discuss the security proprieties of each lane detection model. We demonstrate that the conventional evaluation fails to reflect the robustness in end-to-end autonomous driving scenarios. Our results show that the most robust model on the conventional metrics is the least robust in the end-to-end evaluation. Although the competition dataset and its metrics have played a substantial role in developing performant lane detection methods along with the rapid development of deep neural networks, the conventional evaluation is becoming obsolete and the gap between the metrics and practicality is critical. We hope that our study will help the community make further progress in building a more comprehensive framework to evaluate lane detection models.
Long-Short Transformer: Efficient Transformers for Language and Vision

Transformers have achieved success in both language and vision domains. However, it is prohibitively expensive to scale them to long sequences such as long documents or high-resolution images, because self-attention mechanism has quadratic time and memory complexities with respect to the input sequence length. In this paper, we propose Long-Short Transformer (Transformer-LS), an efficient self-attention mechanism for modeling long sequences with linear complexity for both language and vision tasks. It aggregates a novel long-range attention with dynamic projection to model distant correlations and a short-term attention to capture fine-grained local correlations. We propose a dual normalization strategy to account for the scale mismatch between the two attention mechanisms. Transformer-LS can be applied to both autoregressive and bidirectional models without additional complexity. Our method outperforms the state-of-the-art models on multiple tasks in language and vision domains, including the Long Range Arena benchmark, autoregressive language modeling, and ImageNet classification. For instance, Transformer-LS achieves 0.97 test BPC on enwik8 using half the number of parameters than previous method, while being faster and is able to handle 3x as long sequences compared to its full-attention version on the same hardware. On ImageNet, it can obtain the state-of-the-art results (e.g., a moderate size of 55.8M model solely trained on 224x224 ImageNet-1K can obtain Top-1 accuracy 84.1%), while being more scalable on high-resolution images. The source code and models are released at https://github.com/NVIDIA/transformer-ls .
Exploring the Milky Way Circumgalactic Medium in a Cosmological Context with a Semianalytic Model

We combine the Santa Cruz semianalytic model (SAM) for galaxy formation and evolution with the circumgalactic medium (CGM) model presented in Faerman et al. to explore the CGM properties of L * galaxies. We use the SAM to generate a sample of galaxies with halo masses similar to the Milky Way (MW) halo, M vir ≈ 1012 M ⊙, and find that the CGM mass and mean metallicity in the sample are correlated. We use the CGM masses and metallicities of the SAM galaxies as inputs for the FSM20 model and vary the amount of nonthermal support. The density profiles in our models can be approximated by power-law functions with slopes in the range of 0.75 < a n < 1.25, with higher nonthermal pressure resulting in flatter distributions. We explore how the gas pressure, dispersion measure, O VI–O VIII column densities, and cooling rates behave with the gas distribution and total mass. We show that for CGM masses below ∼3 × 1010 M ⊙ photoionization has a significant effect on the column densities of O VI and O VIII. The combination of different MW CGM observations favors models with similar fractions in thermal pressure, magnetic fields/cosmic rays, and turbulent support and with M CGM ∼ (3–10) × 1010 M ⊙. The MW O VI column requires t cool/t dyn ∼ 4, independent of the gas distribution. The AGN jet-driven heating rates in the SAM are enough to offset the CGM cooling, although exact balance is not required in star-forming galaxies. We provide predictions for the column densities of additional metal ions—N V, Ne VIII, and Mg X.
Study of ergodic divertor edge density regimes on the tokamaks Tore Supra and TEXTOR, and sensitivity of tunnel probe electron temperature measurements to a suprathermal electron component

Controlled thermonuclear fusion offers one possible option to meet our future energy needs in a sustainable way. Magnetic confinement in a so-called 'tokamak'-machine is a possible approach towards the achievement of a burning plasma. An important issue in this tokamak research is the transition of the plasma edge to the inner wall. A first topic that is addressed in this thesis, is the ergodic divertor (ED) configuration. An ED achieves the transition between the confined plasma and the wall in a layer where the flux lines have been ergodized by a proper resonant magnetic perturbation. The connection between up- and downstream plasma parameters during ED operation in the tokamaks Tore Supra en TEXTOR has been investigated experimentally by means of Langmuir probes. As an important first step in the theoretical interpretation of those experiments, a Hamiltonian field line mapping code, which had been previously developed for the TEXTOR dynamic ergodic divertor, has been adapted to the geometry of the Tore Supra ED. Subsequently, this adapted code has been used to study some of the properties of the Tore Supra ED magnetic field line structure, as well as to make a qualitative comparison of the sensitivity of the TEXTOR and Tore Supra ergodic divertor magnetic topology to changes in the central density. A second topic of this thesis concerns certain interpretation issues regarding the current-voltage characteristics obtained by a newly developed type of Langmuir probe for the investigation of edge plasmas. More in particular, the sensitivity of the TP to a small population of nonthermal electrons has been investigated in addition to the influence of suprathermal electrons on the scaling and structure of the Debye and the magnetic sheath at the inside of the tunnel probe.
Periodicity and longtime diffusion for mean field systems in $\mathbb{R}^d$

We study in this paper the longtime behavior of some large but finite populations of interacting stochastic differential equations whose (infinite population) limit Fokker-Planck PDE admits a stable periodic solution. We show that the empirical measure for the population of size N stays close to the periodic solution, but with a random dephasing at the timescale Nt that converges weakly to a Brownian motion with constant drift.
On some fractal sets only containing irrationals

In this paper, we prove that many fractal sets generated by the associated dynamical systems only contain irrationals. We give two applications. Firstly, we explicitly construct some overlapping self-similar sets which only consist of irrationals. Secondly, we prove that some scaled univoque sets only contain irrationals.
Learning an Explicit Hyperparameter Prediction Policy Conditioned on Tasks

Meta learning has attracted much attention recently in machine learning community. Contrary to conventional machine learning aiming to learn inherent prediction rules to predict labels for new query data, meta learning aims to learn the learning methodology for machine learning from observed tasks, so as to generalize to new query tasks by leveraging the meta-learned learning methodology. In this study, we interpret such learning methodology as learning an explicit hyperparameter prediction policy shared by all training tasks. Specifically, this policy is represented as a parameterized function called meta-learner, mapping from a training/test task to its suitable hyperparameter setting, extracted from a pre-specified function set called meta learning machine. Such setting guarantees that the meta-learned learning methodology is able to flexibly fit diverse query tasks, instead of only obtaining fixed hyperparameters by many current meta learning methods, with less adaptability to query task’s variations. Such understanding of meta learning also makes it easily succeed from traditional learning theory for analyzing its generalization bounds with general losses/tasks/models. The theory naturally leads to some feasible controlling strategies for ameliorating the quality of the extracted meta-learner, verified to be able to finely ameliorate its generalization capability in some typical meta learning applications, including few-shot regression, few-shot classification and domain generalization.
The MAVERIC Survey: Simultaneous Chandra and VLA observations of the transitional millisecond pulsar candidate NGC 6652B

Transitional millisecond pulsars are millisecond pulsars that switch between a rotation-powered millisecond pulsar state and an accretion-powered X-ray binary state, and are thought to be an evolutionary stage between neutron star low-mass X-ray binaries and millisecond pulsars. So far, only three confirmed systems have been identified in addition to a handful of candidates. We present the results of a multiwavelength study of the low-mass X-ray binary NGC 6652B in the globular cluster NGC 6652, including simultaneous radio and X-ray observations taken by the Karl G. Jansky Very Large Array and the Chandra X-ray Observatory, and optical spectroscopy and photometry. This source is the second brightest X-ray source in NGC 6652 ($L_{\textrm {X}}\sim 1.8 \times 10^{34}{\, \mathrm{erg\, s}^{-1}}$) and is known to be variable. We observe several X-ray flares over the duration of our X-ray observations, in addition to persistent radio emission and occasional radio flares. Simultaneous radio and X-ray data show no clear evidence of anticorrelated variability. Optical spectra of NGC 6652B indicate variable, broad H α emission that transitions from double-peaked emission to absorption over a time-scale of hours. We consider a variety of possible explanations for the source behaviour, and conclude that based on the radio and X-ray luminosities, short time-scale variability and X-ray flaring, and optical spectra, NGC 6652B is best explained as a transitional millisecond pulsar candidate that displays prolonged X-ray flaring behaviour. However, this could only be confirmed with observations of a change to the rotation-powered millisecond pulsar state.
Dependent finitely homogneneous rosy structures

We study finitely homogeneous dependent rosy structures, adapting results of Cherlin, Harrington, and Lachlan proved for $\omega$-stable $\omega$-categorical structures. In particular, we prove that such structures have finite þ-rank and are coordinatized by a þ-rank 1 set. We show that they admit a distal, finitely axiomatizable, expansion. These results show that there are, up to inter-definability, at most countably many dependent rosy structures M which are homogeneous in a finite relational language.
Can diffusive and ballistic transport coexist in integrable quantum lattice models ?

We investigate the high-temperature dynamical conductivity σ(ω) in two one-dimensional integrable quantum lattice models: the anisotropic XXZ spin chain and the Hubbard chain. The emphasis is on the metallic regime of both models, where besides the ballistic component, the regular part of conductivity might reveal a diffusive-like transport. To resolve the low-frequency dynamics, we upgrade the microcanonical Lanczos method enabling studies of finite-size systems with up to L ≤ 32 sites for XXZ spin model with the frequency resolution δω ∼ 10−3J . Results for the XXZ chain reveal a fine structure of σ(ω) spectra, which originates from the discontinuous variation of the stiffness, previously found at commensurate values of the anisotropy parameter ∆. Still, we do not find a clear evidence for a diffusive component, at least not for commensurate values of ∆, particularly for ∆ = 0.5. Similar is the conclusion for the Hubbard model away from half-filling, where the spectra reveal more universal behavior.
Discrete-Valued Neural Communication

Deep learning has advanced from fully connected architectures to structured models organized into components, e.g., the transformer composed of positional elements, modular architectures divided into slots, and graph neural nets made up of nodes. In structured models, an interesting question is how to conduct dynamic and possibly sparse communication among the separate components. Here, we explore the hypothesis that restricting the transmitted information among components to discrete representations is a beneficial bottleneck. The motivating intuition is human language in which communication occurs through discrete symbols. Even though individuals have different understandings of what a"cat"is based on their specific experiences, the shared discrete token makes it possible for communication among individuals to be unimpeded by individual differences in internal representation. To discretize the values of concepts dynamically communicated among specialist components, we extend the quantization mechanism from the Vector-Quantized Variational Autoencoder to multi-headed discretization with shared codebooks and use it for discrete-valued neural communication (DVNC). Our experiments show that DVNC substantially improves systematic generalization in a variety of architectures -- transformers, modular architectures, and graph neural networks. We also show that the DVNC is robust to the choice of hyperparameters, making the method very useful in practice. Moreover, we establish a theoretical justification of our discretization process, proving that it has the ability to increase noise robustness and reduce the underlying dimensionality of the model.
The Hyperspherical Geometry of Community Detection: Modularity as a Distance

We introduce a metric space of clusterings, where clusterings are described by a binary vector indexed by the vertex-pairs. We extend this geometry to a hypersphere and prove that maximizing modularity is equivalent to minimizing the angular distance to some modularity vector over the set of clustering vectors. In that sense, modularity-based community detection methods can be seen as a subclass of a more general class of projection methods, which we define as the community detection methods that adhere to the following two-step procedure: first, mapping the network to a point on the hypersphere; second, projecting this point to the set of clustering vectors. We show that this class of projection methods contains many interesting community detection methods. Many of these new methods cannot be described in terms of null models and resolution parameters, as is customary for modularity-based methods. We provide a new characterization of such methods in terms of meridians and latitudes of the hypersphere. In addition, by relating the modularity resolution parameter to the latitude of the corresponding modularity vector, we obtain a new interpretation of the resolution limit that modularity maximization is known to suffer from.
Understanding Consumer Preferences for Explanations Generated by XAI Algorithms

Explaining firm decisions made by algorithms in customer-facing applications is increasingly required by regulators and expected by customers. While the emerging field of Explainable Artificial Intelligence (XAI) has mainly focused on developing algorithms that generate such explanations, there has not yet been sufficient consideration of customers' preferences for various types and formats of explanations. We discuss theoretically and study empirically people's preferences for explanations of algorithmic decisions. We focus on three main attributes that describe automatically-generated explanations from existing XAI algorithms (format, complexity, and specificity), and capture differences across contexts (online targeted advertising vs. loan applications) as well as heterogeneity in users' cognitive styles. Despite their popularity among academics, we find that counterfactual explanations are not popular among users, unless they follow a negative outcome (e.g., loan application was denied). We also find that users are willing to tolerate some complexity in explanations. Finally, our results suggest that preferences for specific (vs. more abstract) explanations are related to the level at which the decision is construed by the user, and to the deliberateness of the user's cognitive style.
The Hidden Convex Optimization Landscape of Two-Layer ReLU Neural Networks: an Exact Characterization of the Optimal Solutions

We prove that ﬁnding all globally optimal two-layer ReLU neural networks can be performed by solving a convex optimization program with cone constraints. Our analysis is novel, characterizes all optimal solutions, and does not leverage duality-based analysis which was recently used to lift neural network training into convex spaces. Given the set of solutions of our convex optimization program, we show how to construct exactly the entire set of optimal neural networks. We provide a detailed characterization of this optimal set and its invariant transformations. As additional consequences of our convex perspective, (i) we establish that Clarke stationary points found by stochastic gradient descent correspond to the global optimum of a subsampled convex problem (ii) we provide a polynomial-time algorithm for checking if a neural network is a global minimum of the training loss (iii) we provide an explicit construction of a continuous path between any neural network and the global minimum of its sublevel set and (iv) characterize the minimal size of the hidden layer so that the neural network optimization landscape has no spurious valleys. Overall, we provide a rich framework for studying the landscape of neural network training loss through convexity.
Contrastive Losses and Solution Caching for Predict-and-Optimize

Many decision-making processes involve solving a combinatorial optimization problem with uncertain input that can be estimated from historic data. Recently, problems in this class have been successfully addressed via end-to-end learning approaches, which rely on solving one optimization problem for each training instance at every epoch. In this context, we provide two distinct contributions. First, we use a Noise Contrastive approach to motivate a family of surrogate loss functions, based on viewing non-optimal solutions as negative examples. Second, we address a major bottleneck of all predict-and-optimize approaches, i.e. the need to frequently recompute optimal solutions at training time. This is done via a solver-agnostic solution caching scheme, and by replacing optimization calls with a lookup in the solution cache. The method is formally based on an inner approximation of the feasible space and, combined with a cache lookup strategy, provides a controllable trade-off between training time and accuracy of the loss approximation. We empirically show that even a very slow growth rate is enough to match the quality of state-of-the-art methods, at a fraction of the computational cost.
Research overview : Single molecule studies of polymer solutions and biological applications of π-conjugated polymers

In this project, we investigated the self-assembly process of a class of bio-hybrid synthetic oligopeptides with πconjugated cores flanked by sequence-defined amino acids into fiber-like materials. We induced self-assembly of biohybrid oligopeptides by increasing the concentration of the monomers. Using a combination of particle tracking microrheology, confocal fluorescence microscopy, optical spectroscopy, and electron microscopy, we studied the gelation process of π-conjugated oligopeptides during the self-assembly process. Our results show that π-conjugated oligopeptides self-assemble into fiber-like structures not only at low pH [1], but also at neutral pH, even in the absence of electrostatic screening of charged residues [2]. A critical fiber formation concentration cfiber and a critical gel concentration cgel are determined for fiber-forming π-conjugated oligopeptides so that at relatively low concentrations, oligopeptide monomers will self-assemble into fibers and at higher concentrations, they will form physical crosslink and form gels with 3-D percolated structures. We also measured the linear viscoelastic modulus of the forming gels across a wide range of peptide concentrations. These results suggest that the underlying chemical structure of the synthetic π-conjugated cores greatly influence the self-assembly process, such that oligopeptides appended to π-conjugated cores with greater torsional flexibility tend to form more robust fibers upon increasing peptide concentration compared to oligopeptides with sterically constrained cores.
What makes an urban bird ? 1 2

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Oral hygiene habits and status of orthodontic patients attending the University of Pretoria, Oral and Dental Hospital

Author affiliations: 1. Noluthando L Buthelezi: , DipOH, AdvDipOH (Ortho), PGCHE, BEd Hons (TEPD), Department of Orthodontics, School of Dentistry, University of Pretoria, Pretoria, South Africa. ORCID Number: 0000-0002-3900-7707 2. Thomas K Madiba: B.Dent Ther, BDS, DHSM, MChD (Community Dentistry), Department of Community Dentistry, School of Dentistry, University of Pretoria, Pretoria, South Africa. ORCID Number: 0000-0002-0171-0595 Corresponding author: Thomas K Madiba Department of Community Dentistry, School of Dentistry, University of Pretoria, Pretoria, South Africa. P.O. Box 21497, Valhalla, 0137. Email: thommy.madiba@gmail.com Author contributions: 1. Noluthando L Buthelezi: Principal author 50% 2. Thomas K Madiba: Second author 50% RESEARCH 130 >
Analysis of Selected Metals in Edible Fish and Bottom Sediment from Lake Hawassa , Ethiopia

Sediment and fish (nile Tilapia, Oreochromis niloticus) were collected from six sites of Lake Hawassa namely; Tikur Wuha, Amora Gedel, Dorie Bafana, Central part of the lake, the opposite side of Amora Gedel and Referral Hospital sites. Concentration of heavy metals was analyzed quantitatively using Flame Atomic Absorption Spectrophotometer and Chemometric methods. The maximum and minimum mean levels (mg/kg dry mass) in different lake sediment sites were Cu (51.38-17.64), Mn (348.51-140.63), Cd (12.8764403), Pb (30.05-6.72), Ni (26.73-8.43 ), Zn (367.84-147.49), Cr (14.15-5.34), Co (27.199.59 ), Mg (3859.86-1872.43), Na (4913.72-2903.043), Ca (5526.74 3608.71), K (4229.69-2255.57) and that of fish muscle were Cu (13.833-5.666), Mn (11.972-9.722), Zn (21.110-12.055), Ca (574-482), Mg (512-437), Na (771-681), K (903-785). The result shows that the metal concentration in sediment samples is in order of Ca > Na > K > Mg > Zn > Mn > Cu > Pb > Co > Ni > Cr > Cd. The detectable metals in Tilapia fish muscle were in order of K > Na > Ca > Mg > Zn > Mn > Cu. In all the samples, concentrations of metals in sediment samples were higher than those of fish samples. © 2015 Elixir All rights reserved. ARTICLE INF O Article h istory: Received: 5 March 2015; Received in revised form: 16 May 2015; Accepted: 16 May 2015;