Miracle-dz/UniFAR-FTUs huggingface dataset:
A Priori User Acceptance and the Perceived Driving Pleasure in Semi-Autonomous and Autonomous Vehicles

The aim of this minor pilot study is, from a sociological user perspective, to explore a priori user acceptance and the perceived driving pleasure in semi-autonomous and autonomous vehicles. The methods used were 13 in-depth interviews while having participants watch video examples within four different scenarios. After each scenario, two different numerical rating scales were used. There was a tendency toward positive attitudes regarding semi-autonomous driving systems, especially the use of a parking assistant and while driving in city traffic congestion. However, there were also major concerns about trust, user interactions and legislation, as well as the use of technology when driving on highways. Future studies should use a more substantial theoretical framework and real-life tests for a better understanding of user acceptance of driving automation.
Towards Using Diachronic Distributed Word Representations as Models of Lexical Development

Recent work has shown that distributed word representations can encode abstract information from child-directed speech. In this paper, we use diachronic distributed word representations to perform temporal modeling and analysis of lexical development in children. Unlike all previous work, we use temporally sliced corpus to learn distributed word representations of child-speech and child-directed speech under a curriculum-learning setting. In our experiments, we perform a lexical categorization task to plot the semantic and syntactic knowledge acquisition trajectories in children. Next, we perform linear mixed-effects modeling over the diachronic representational changes to study the role of input word frequencies in the rate of word acquisition in children. We also perform a fine-grained analysis of lexical knowledge transfer from adults to children using Representational Similarity Analysis. Finally, we perform a qualitative analysis of the diachronic representations from our model, which reveals the grounding and word associations in the mental lexicon of children. Our experiments demonstrate the ease of usage and effectiveness of diachronic distributed word representations in modeling lexical development.
The Social Abduction of Science

The logic of abduction involves a collision between deduction and induction, where empirical surprises violate expectations and scientists innovate to resolve them. Here we reformulate abduction as a social process, occurring not only within individual scientists, but often through conversation between those who understand a particular scientific system and its anomalies—its insiders—and those exposed to alien and disruptive patterns, theories, and findings that could resolve them—its outsiders. These extended conversations between scientists, scholars, and disciplines with divergent backgrounds define a social logic of discovery by catalyzing social syllogisms that yield speculative hypotheses with outsized impact across science. We show how this approach theorizes a number of disparate findings from scientometrics and the science and science, tethering them to powerful interpersonal processes that link diversity with discovery. By reframing abductive reasoning as a social process, we attempt to broker a productive new relationship between science studies’ focus on scientific practice and the philosophy of science’s concern with propositions in scientific discourse, rendering claims-making through abduction a critical scientific practice, central to scientific change, and increasingly available to science studies through small and large-scale data on scientific communication and deliberation.
SWAT: Spatial Structure Within and Among Tokens

Modeling visual data as tokens (i.e., image patches) using attention mechanisms, feed-forward networks or convolutions has been highly effective in recent years. Such methods usually have a common pipeline: a tokenization method, followed by a set of layers/blocks for information mixing, both within and among tokens. When image patches are converted into tokens, they are often flattened, discarding the spatial structure within each patch. As a result, any processing that follows (eg: multi-head self-attention) may fail to recover and/or benefit from such information. In this paper, we argue that models can have significant gains when spatial structure is preserved during tokenization, and is explicitly used during the mixing stage. We propose two key contributions: (1) Structure-aware Tokenization and, (2) Structure-aware Mixing, both of which can be combined with existing models with minimal effort. We introduce a family of models (SWAT), showing improvements over the likes of DeiT, MLP-Mixer and Swin Transformer, across multiple benchmarks including ImageNet classification and ADE20K segmentation. Our code is available at github.com/kkahatapitiya/SWAT.
Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction

We present Gradient-SDF, a novel representation for 3D geometry that combines the advantages of implict and explicit representations. By storing at every voxel both the signed distance field as well as its gradient vector field, we enhance the capability of implicit representations with approaches originally formulated for explicit surfaces. As concrete examples, we show that (1) the Gradient-SDF allows us to perform direct SDF tracking from depth images, using efficient storage schemes like hash maps, and that (2) the Gradient-SDF representation enables us to perform photometric bundle adjustment directly in a voxel representation (without transforming into a point cloud or mesh), naturally a fully implicit optimization of geometry and camera poses and easy geometry upsampling. Experimental results confirm that this leads to significantly sharper reconstructions. Since the overall SDF voxel structure is still respected, the proposed Gradient-SDF is equally suited for (GPU) parallelization as related approaches.
Design and Experimental Characterization of a Vibration Energy Harvesting Device for Rotational Systems

This paper presents a new vibration based electromagnetic power generator to transfer energy from stationary to rotating equipment, which can be a new attempt to substitute slip ring in rotational systems. The natural frequencies and modes are simulated in order to have a maximum and steady power output from the device. Parameters such as piezoelectric disk location and relative motion direction of the magnet are theoretically and experimentally analyzed. The results show that the position that is close to the fixed end of the cantilever and the relative motion along the long side gives higher power output. Moreover, the capability of the energy harvester to extract power from lower energy environment is experimentally validated. The voltage and power output are measured at different excitation frequencies.
Spin and Orbital Spectroscopy in the Absence of Coulomb Blockade in Lead Telluride Nanowire Quantum Dots

We investigate quantum dots in semiconductor PbTe nanowire devices. Due to the accessibility of ambipolar transport in PbTe, quantum dots can be occupied both with electrons and holes. Owing to a very large dielectric constant in PbTe of order 1000, we do not observe Coulomb blockade which typically obfuscates the orbital and spin spectra. We extract large and highly anisotropic effective Landé g-factors, in the range 20-44. The absence of Coulomb blockade allows direct readout, at zero source-drain bias, of spin-orbit hybridization energies of up to 600 \muμeV. These spin properties make PbTe nanowires, the recently synthesized members of group IV-VI materials family, attractive as a materials platform for quantum technology, such as spin and topological qubits.
Natural resources, nationalism, and nationalization

Synopsis This paper traces the rise of South Africa’s mining industry in the context of the different state forms that oversaw its development. It commences with the mining-led industrial revolution through four periods of conflict, each of which contributed to a deepening of the industrial base, with constant battles between domestic and foreign capital and labour, and the rise of both Afrikaner and African nationalism. The role of state-owned enterprises has been crucial in shaping this dynamic so that at its peak the public sector and leading mining houses constituted two massive centres of power. This was followed by a thirty-year period of neo-liberal experimentation. The current debate on the ‘developmental state’ is an attempt to reconstruct the state as a counterweight to capital. It is argued that while the country has moved toward inclusive political and economic institutions, these are both limited by deep ‘extractive’ characteristics. This transformation failure provides a fertile ground for populism that presents one of the biggest threats to inclusive growth. Cited examples point to the many ways that states have sought to deal with the bonanza that conversion of mineral non-resources into resources brings. Such greenfield resources on state land, or in areas where the state holds monopoly rights, do not present ownership issues. It is suggested that this is the route that South Africa should follow, as the alternative will be conflictual.
Application of deep learning to camera trap data for ecologists in planning / engineering – Can captivity imagery train a model which generalises to the wild?

Understanding the abundance of a species is the first step towards understanding both its long-term sustainability and the impact that we may be having upon it. Ecologists use camera traps to remotely survey for the presence of specific animal species. Previous studies have shown that deep learning models can be trained to automatically detect and classify animals within camera trap imagery with high levels of confidence. However, the ability to train these models is reliant upon having enough high-quality training data. What happens when the animal is rare or the data sets are non-existent? This research proposes an approach of using images of rare animals in captivity (we focus on the Scottish wildcat) to generate the training dataset. We explore the challenges associated with generalising a model trained on captivity data when applied to data collected in the wild. The research is contextualised by the needs of ecologists in planning / engineering. Following precedents from other research, this project establishes an ensemble system of object detection, image segmentation and image classification models which are then tested using different image manipulation and class structuring techniques to encourage model generalisation. The research concludes, in the context of Scottish wildcat, that models trained on captivity imagery cannot be generalised to wild camera trap imagery using existing techniques. However, final model performances based on a two-class model (Wildcat vs Not Wildcat) achieved an overall accuracy score of 81.6% and Wildcat accuracy score of 54.8% on a test set in which only 1% of images contained a wildcat. This suggests using captivity images is feasible with further research. This is the first research which attempts to generate a training set based on captivity data and the first to explore the development of such models in the context of ecologists in planning / engineering.
Nucleon isovector form factors from physical-mass 2+1-flavor dynamical domain-wall QCD

The current status is reported of joint lattice numerical calculations by LHP and RBC collaborations of isovector nucleon form factors using 2+1-flavor physical-mass domain-wall fermions ensemble at a lattice cutoff $a^{-1}$ of 1.730(4) GeV and spatial volume of $(La=\mbox{\rm 5.471(13)fm})^3$ generated jointly by RBC and UKQCD collaborations.
DeepWiVe: Deep-Learning-Aided Wireless Video Transmission

We present DeepWiVe, the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combining video compression, channel coding, and modulation steps into a single neural transform. Our DNN decoder predicts residuals without distortion feedback, which improves the video quality by accounting for occlusion/disocclusion and camera movements. We simultaneously train different bandwidth allocation networks for the frames to allow variable bandwidth transmission. Then, we train a bandwidth allocation network using reinforcement learning (RL) that optimizes the allocation of limited available channel bandwidth among video frames to maximize the overall visual quality. Our results show that DeepWiVe can overcome the cliff-effect, which is prevalent in conventional separation-based digital communication schemes, and achieve graceful degradation with the mismatch between the estimated and actual channel qualities. DeepWiVe outperforms H.264 video compression followed by low-density parity check (LDPC) codes in all channel conditions by up to 0.0485 in terms of the multi-scale structural similarity index measure (MS-SSIM), and H.265+ LDPC by up to 0.0069 on average. We also illustrate the importance of optimizing bandwidth allocation in JSCC video transmission by showing that our optimal bandwidth allocation policy is superior to uniform allocation as well as a heuristic policy benchmark.
Asymptotic Average Mutual Information Over Finite Input Mixture Gamma Distributed Channels

This letter establishes a unified analytical framework to study the asymptotic average mutual information (AMI) of mixture gamma (MG) distributed fading channels driven by finite input signals in the high signal-to-noise ratio (SNR) regime. It is found that the AMI converges to some constant as the average SNR increases and its rate of convergence (ROC) is determined by the coding gain and diversity order. Moreover, the derived results are used to investigate the asymptotic optimal power allocation policy of a bank of parallel fading channels having finite inputs. It is suggested that in the high SNR region, the sub-channel with a lower coding gain or diversity order should be allocated with more power. Finally, numerical results are provided to collaborate the theoretical analyses.
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning

—This paper studies a distributed policy gradient in collaborative multi-agent reinforcement learning (MARL), where agents over a communication network aim to ﬁnd the optimal policy to maximize the average of all agents’ local returns. Due to the non-concave performance function of policy gradient, the existing distributed stochastic optimization methods for convex problems cannot be directly used for policy gradient in MARL. This paper proposes a distributed policy gradient with variance reduction and gradient tracking to address the high variances of policy gradient, and utilizes importance weight to solve the distribution shift problem in the sampling process. We then provide an upper bound on the mean-squared stationary gap, which depends on the number of iterations, the mini-batch size, the epoch size, the problem parameters, and the network topology. We further establish the sample and communication complexity to obtain an (cid:15) -approximate stationary point. Numerical experiments are performed to validate the effectiveness of the proposed algorithm.
JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints

Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software. CAD designers build up these assemblies by aligning individual parts to one another using constraints called joints. In this paper we introduce JoinABLe, a learning-based method that assembles parts together to form joints. JoinABLe uses the weak supervision available in standard parametric CAD files without the help of object class labels or human guidance. Our results show that by making network predictions over a graph representation of solid models we can outperform multiple baseline methods with an accuracy (79.53%) that approaches human performance (80%). Finally, to support future research we release the Fusion 360 Gallery assembly dataset, containing assemblies with rich information on joints, contact surfaces, holes, and the underlying assembly graph structure.
A Simple and Tight Greedy OCRS

In recent years, Contention Resolution Schemes (CRSs), introduced by Chekuri, Vondr´ak, and Zenklusen, have emerged as a general framework for obtaining feasible solutions to combinatorial optimization problems with constraints. The idea is to ﬁrst solve a continuous relaxation and then round the fractional solution. When one does not have any control on the order of rounding, Online Contention Resolution Schemes (OCRSs) can be used instead, and have been successfully applied in settings such as prophet inequalities and stochastic probing. Intuitively, a greedy OCRS has to decide which elements to include in the integral solution before the online process starts. In this work, we give simple 1 /e - selectable greedy OCRSs for rank-1 matroids, partition matroids and transversal matroids. We also show that our greedy OCRSs are optimal, even for the simple single-item case.
Distribution of first-reaction times with target regions on boundaries of shell-like domains

We study the probability density function (PDF) of the first-reaction times between a diffusive ligand and a membrane-bound, immobile imperfect target region in a restricted ‘onion-shell’ geometry bounded by two nested membranes of arbitrary shapes. For such a setting, encountered in diverse molecular signal transduction pathways or in the narrow escape problem with additional steric constraints, we derive an exact spectral form of the PDF, as well as present its approximate form calculated by help of the so-called self-consistent approximation. For a particular case when the nested domains are concentric spheres, we get a fully explicit form of the approximated PDF, assess the accuracy of this approximation, and discuss various facets of the obtained distributions. Our results can be straightforwardly applied to describe the PDF of the terminal reaction event in multi-stage signal transduction processes.
Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation

Consistency learning using input image, feature, or network perturbations has shown remarkable results in semi-supervised semantic segmentation, but this approach can be seriously affected by inaccurate predictions of unlabelled training images. There are two consequences of these inaccurate predictions: 1) the training based on the “strict” cross-entropy (CE) loss can easily overfit prediction mistakes, leading to confirmation bias; and 2) the perturbations applied to these inaccurate predictions will use potentially erroneous predictions as training signals, degrading consistency learning. In this paper, we address the prediction accuracy problem of consistency learning methods with novel extensions of the mean-teacher (MT) model, which include a new auxiliary teacher, and the replacement of MT's mean square error (MSE) by a stricter confidence-weighted cross-entropy (Conf-CE) loss. The accurate prediction by this model allows us to use a challenging combination of network, input data and feature perturbations to improve the consistency learning generalisation, where the feature perturbations consist of a new adversarial perturbation. Results on public benchmarks show that our approach achieves remarkable improvements over the previous SOTA methods in the field.11Supported by Australian Research Council through grants DP180103232 and FT190100525. Our code is available at https://github.com/yyliu01/PS-MT.
Inflated Eccentric Migration of Evolving Gas Giants I – Accelerated Formation and Destruction of Hot and Warm Jupiters

Hot and warm Jupiters (HJs and WJs, correspondingly) are gas giants orbiting their host stars at very short orbital periods (P HJ < 10 days; 10 < P WJ < 200 days). HJs and a significant fraction of WJs are thought to have migrated from initially farther-out birth locations. While such migration processes have been extensively studied, the thermal evolution of gas giants and its coupling with migration processes are usually overlooked. In particular, gas giants end their core accretion phase with large radii, then contract slowly to their final radii. Moreover, intensive heating can slow the contraction at various evolutionary stages. The initial large inflated radii lead to faster tidal migration, due to the strong dependence of tides on the radius. Here, we explore this accelerated migration channel, which we term inflated eccentric migration, using a semi-analytical, self-consistent model of the thermal–dynamical evolution of the migrating gas giants, later validated by our numerical model (see the companion paper, paper II). We demonstrate our model for specific examples and carry out a population synthesis study. Our results provide a general picture of the properties of the formed HJs and WJs via inflated migration, and their dependence on the initial parameters/distributions. We show that the tidal migration of gas giants could occur much more rapidly then previously thought, and could lead to the accelerated destruction and formation of HJs and an enhanced formation rate for WJs. Accounting for the coupled thermal–dynamical evolution is therefore critical to understanding the formation of HJs/WJs, and the evolution and final properties of the population, and it plays a key role in their migration processes.
Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs

Do language models have beliefs about the world? Dennett (1995) famously argues that even thermostats have beliefs, on the view that a belief is simply an informational state decoupled from any motivational state. In this paper, we discuss approaches to detecting when models have beliefs about the world, and we improve on methods for updating model beliefs to be more truthful, with a focus on methods based on learned optimizers or hypernetworks. Our main contributions include: (1) new metrics for evaluating belief-updating methods that focus on the logical consistency of beliefs, (2) a training objective for Sequential, Local, and Generalizing model updates (SLAG) that improves the performance of learned optimizers, and (3) the introduction of the belief graph, which is a new form of interface with language models that shows the interdependencies between model beliefs. Our experiments suggest that models possess belief-like qualities to only a limited extent, but update methods can both fix incorrect model beliefs and greatly improve their consistency. Although off-the-shelf optimizers are surprisingly strong belief-updating baselines, our learned optimizers can outperform them in more difficult settings than have been considered in past work. Code is available at https://github.com/peterbhase/SLAG-Belief-Updating
Spherically symmetric black holes in the limiting curvature theory of gravity

In this paper we describe a model of a four-dimensional spherically symmetric black hole in a limiting curvature theory of gravity. In this theory the Einstein-Hilbert action is modified by adding to the action terms providing inequality constraints on chosen curvature invariants. We demonstrated that in such a model, instead of formation of the curvature singularity, the spacetime remains regular in the black hole interior. For black holes with gravitational radius much larger than the radius $\ell$ of the critical curvature the obtained solutions describe a space exponentially expanding in one direction and periodically oscillating in the other two (spherical) directions.