Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Flow-based Programming Plugin for Brain Computer Interface in Computer-Aided Design

Over the last half century, the main application of Brain Computer Interfaces, BCIs has been controlling wheelchairs and neural prostheses or generating text or commands for people with restricted mobility. There has been very limited attention in the field to applications for computer aided design, despite the potential of BCIs to provide a new form of environmental interaction. In this paper we introduce the development and application of Neuron, a novel BCI tool that enables designers with little experience in neuroscience or computer programming to gain access to neurological data, along with established metrics relevant to design, create BCI interaction prototypes, both with digital onscreen objects and physical devices, and evaluate designs based on neurological information and record measurements for further analysis. After discussing the BCI tool development, the article presents its capabilities through two case studies, along with a brief evaluation of the tool performance and a discussion of implications, limitations, and future improvement.
Layer-wise Representation Fusion for Compositional Generalization

Existing neural models are demonstrated to struggle with compositional generalization (CG), i.e., the ability to systematically generalize to unseen compositions of seen components. A key reason for failure on CG is that the syntactic and semantic representations of sequences in both the uppermost layer of the encoder and decoder are entangled. However, previous work concentrates on separating the learning of syntax and semantics instead of exploring the reasons behind the representation entanglement (RE) problem to solve it. We explain why it exists by analyzing the representation evolving mechanism from the bottom to the top of the Transformer layers. We find that the ``shallow'' residual connections within each layer fail to fuse previous layers' information effectively, leading to information forgetting between layers and further the RE problems. Inspired by this, we propose LRF, a novel Layer-wise Representation Fusion framework for CG, which learns to fuse previous layers' information back into the encoding and decoding process effectively through introducing a fuse-attention module at each encoder and decoder layer. LRF achieves promising results on two realistic benchmarks, empirically demonstrating the effectiveness of our proposal. Codes are available at https://github.com/thinkaboutzero/LRF.
Personalized Privacy Amplification via Importance Sampling

We examine the privacy-enhancing properties of importance sampling. In importance sampling, selection probabilities are heterogeneous and each selected data point is weighted by the reciprocal of its selection probability. Due to the heterogeneity of importance sampling, we express our results within the framework of personalized differential privacy. We first consider the general case where an arbitrary personalized differentially private mechanism is subsampled with an arbitrary importance sampling distribution and show that the resulting mechanism also satisfies personalized differential privacy. This constitutes an extension of the established privacy amplification by subsampling result to importance sampling. Then, for any fixed mechanism, we derive the sampling distribution that achieves the optimal sampling rate subject to a worst-case privacy constraint. Empirically, we evaluate the privacy, efficiency, and accuracy of importance sampling on the example of k-means clustering.
Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the rapid amassing of spatial-temporal (ST) ocean data, many spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, including climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated but with unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models on ST ocean data. To the best of our knowledge, a comprehensive survey of existing studies remains missing in the literature, which hinders not only computer scientists from identifying the research issues in ocean data mining but also ocean scientists to apply advanced STDM techniques. In this paper, we provide a comprehensive survey of existing STDM studies for ocean science. Concretely, we first review the widely-used ST ocean datasets and highlight their unique characteristics. Then, typical ST ocean data quality enhancement techniques are explored. Next, we classify existing STDM studies in ocean science into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate on the techniques for these tasks. Finally, promising research opportunities are discussed. This survey can help scientists from both computer science and ocean science better understand the fundamental concepts, key techniques, and open challenges of STDM for ocean science.
Pandemic, Hybrid Teaching & Stress: Examining Indian Teachers’ Sociotechnical Support Practices in Low-income Schools

Support plays a vital role in the teaching profession. A good support system can empower teachers to regulate their emotions and effectively manage stress while working in isolation. The COVID-19 pandemic has ushered in a hybrid form of education, necessitating the acquisition of new skills by teachers and compelling them to adapt to remote teaching. This new development further amplifies the sense of isolation prevalent amongst the teaching community. Against this backdrop, our study investigates the availability of sociotechnical support infrastructures for teachers in low-income schools while also looking into the support practices embraced by this class of teachers following the pandemic. Through 28 qualitative interviews involving teachers, management and personnel from support organizations, we demonstrate how teachers have largely taken the initiative to establish their own informal support networks in the absence of formal support infrastructures. Smartphones have significantly augmented these support practices, serving as both a valuable source of support as well as a medium for facilitating support practices. However, in comparison to other forms of support received from these sources, the availability of emotion-focused support for teachers have proven to be inadequate, creating imbalances in their support seeking practices. Our paper provides different contextual ways to reduce these imbalances and improve the occupational well-being of teachers.
Feedforward neural networks with ReLU activation functions are linear splines

In this thesis the approximation properties of feedforward articial neural networks with one hidden layer and ReLU activation functions are examined. It is shown that functions of these kind are linear splines and the number of spline knots depend on the number of nodes in the network. In fact an upper bound can be derived for the number of knots. Furthermore, the positioning of the knots depend on the optimization of the adjustable parameters of the network. A numerical example is given where the network models are compared to linear interpolating splines with equidistant positioned knots.
Exact Diffusion Inversion via Bi-directional Integration Approximation

Recently, various methods have been proposed to address the inconsistency issue of DDIM inversion to enable image editing, such as EDICT [36] and Null-text inversion [22]. However, the above methods introduce considerable computational overhead. In this paper, we propose a new technique, named \emph{bi-directional integration approximation} (BDIA), to perform exact diffusion inversion with neglible computational overhead. Suppose we would like to estimate the next diffusion state $\boldsymbol{z}_{i-1}$ at timestep $t_i$ with the historical information $(i,\boldsymbol{z}_i)$ and $(i+1,\boldsymbol{z}_{i+1})$. We first obtain the estimated Gaussian noise $\hat{\boldsymbol{\epsilon}}(\boldsymbol{z}_i,i)$, and then apply the DDIM update procedure twice for approximating the ODE integration over the next time-slot $[t_i, t_{i-1}]$ in the forward manner and the previous time-slot $[t_i, t_{t+1}]$ in the backward manner. The DDIM step for the previous time-slot is used to refine the integration approximation made earlier when computing $\boldsymbol{z}_i$. A nice property of BDIA-DDIM is that the update expression for $\boldsymbol{z}_{i-1}$ is a linear combination of $(\boldsymbol{z}_{i+1}, \boldsymbol{z}_i, \hat{\boldsymbol{\epsilon}}(\boldsymbol{z}_i,i))$. This allows for exact backward computation of $\boldsymbol{z}_{i+1}$ given $(\boldsymbol{z}_i, \boldsymbol{z}_{i-1})$, thus leading to exact diffusion inversion. It is demonstrated with experiments that (round-trip) BDIA-DDIM is particularly effective for image editing. Our experiments further show that BDIA-DDIM produces markedly better image sampling qualities than DDIM for text-to-image generation. BDIA can also be applied to improve the performance of other ODE solvers in addition to DDIM. In our work, it is found that applying BDIA to the EDM sampling procedure produces consistently better performance over four pre-trained models.
Abductive Reasoning with the GPT-4 Language Model: Case studies from criminal investigation, medical practice, scientific research

This study evaluates the GPT-4 Large Language Model's abductive reasoning in complex fields like medical diagnostics, criminology, and cosmology. Using an interactive interview format, the AI assistant demonstrated reliability in generating and selecting hypotheses. It inferred plausible medical diagnoses based on patient data and provided potential causes and explanations in criminology and cosmology. The results highlight the potential of LLMs in complex problem-solving and the need for further research to maximize their practical applications.
Analysis of the rSVDdpd Algorithm: A Robust Singular Value Decomposition Method using Density Power Divergence

The traditional method of computing singular value decomposition (SVD) of a data matrix is based on a least squares principle, thus, is very sensitive to the presence of outliers. Hence the resulting inferences across different applications using the classical SVD are extremely degraded in the presence of data contamination (e.g., video surveillance background modelling tasks, etc.). A robust singular value decomposition method using the minimum density power divergence estimator (rSVDdpd) has been found to provide a satisfactory solution to this problem and works well in applications. For example, it provides a neat solution to the background modelling problem of video surveillance data in the presence of camera tampering. In this paper, we investigate the theoretical properties of the rSVDdpd estimator such as convergence, equivariance and consistency under reasonable assumptions. Since the dimension of the parameters, i.e., the number of singular values and the dimension of singular vectors can grow linearly with the size of the data, the usual M-estimation theory has to be suitably modified with concentration bounds to establish the asymptotic properties. We believe that we have been able to accomplish this satisfactorily in the present work. We also demonstrate the efficiency of rSVDdpd through extensive simulations.
2D, 2.5D, or 3D? An Exploratory Study on Multilayer Network Visualisations in Virtual Reality

Relational information between different types of entities is often modelled by a multilayer network (MLN) – a network with subnetworks represented by layers. The layers of an MLN can be arranged in different ways in a visual representation, however, the impact of the arrangement on the readability of the network is an open question. Therefore, we studied this impact for several commonly occurring tasks related to MLN analysis. Additionally, layer arrangements with a dimensionality beyond 2D, which are common in this scenario, motivate the use of stereoscopic displays. We ran a human subject study utilising a Virtual Reality headset to evaluate 2D, 2.5D, and 3D layer arrangements. The study employs six analysis tasks that cover the spectrum of an MLN task taxonomy, from path finding and pattern identification to comparisons between and across layers. We found no clear overall winner. However, we explore the task-to-arrangement space and derive empirical-based recommendations on the effective use of 2D, 2.5D, and 3D layer arrangements for MLNs.
Multi-Point Functional Central Limit Theorem for Wigner Matrices

Consider the random variable $\mathrm{Tr}( f_1(W)A_1\dots f_k(W)A_k)$ where $W$ is an $N\times N$ Hermitian Wigner matrix, $k\in\mathbb{N}$, and choose (possibly $N$-dependent) regular functions $f_1,\dots, f_k$ as well as bounded deterministic matrices $A_1,\dots,A_k$. We give a functional central limit theorem showing that the fluctuations around the expectation are Gaussian. Moreover, we determine the limiting covariance structure and give explicit error bounds in terms of the scaling of $f_1,\dots,f_k$ and the number of traceless matrices among $A_1,\dots,A_k$, thus extending the results of [Cipolloni, Erd\H{o}s, Schr\"oder 2023] to products of arbitrary length $k\geq2$. As an application, we consider the fluctuation of $\mathrm{Tr}(\mathrm{e}^{\mathrm{i} tW}A_1\mathrm{e}^{-\mathrm{i} tW}A_2)$ around its thermal value $\mathrm{Tr}(A_1)\mathrm{Tr}(A_2)$ when $t$ is large and give an explicit formula for the variance.
On the Convergence of Bounded Agents

When has an agent converged? Standard models of the reinforcement learning problem give rise to a straightforward definition of convergence: An agent converges when its behavior or performance in each environment state stops changing. However, as we shift the focus of our learning problem from the environment's state to the agent's state, the concept of an agent's convergence becomes significantly less clear. In this paper, we propose two complementary accounts of agent convergence in a framing of the reinforcement learning problem that centers around bounded agents. The first view says that a bounded agent has converged when the minimal number of states needed to describe the agent's future behavior cannot decrease. The second view says that a bounded agent has converged just when the agent's performance only changes if the agent's internal state changes. We establish basic properties of these two definitions, show that they accommodate typical views of convergence in standard settings, and prove several facts about their nature and relationship. We take these perspectives, definitions, and analysis to bring clarity to a central idea of the field.
Properties of Discrete Sliced Wasserstein Losses

The Sliced Wasserstein (SW) distance has become a popular alternative to the Wasserstein distance for comparing probability measures. Widespread applications include image processing, domain adaptation and generative modelling, where it is common to optimise some parameters in order to minimise SW, which serves as a loss function between discrete probability measures (since measures admitting densities are numerically unattainable). All these optimisation problems bear the same sub-problem, which is minimising the Sliced Wasserstein energy. In this paper we study the properties of $\mathcal{E}: Y \longmapsto \mathrm{SW}_2^2(\gamma_Y, \gamma_Z)$, i.e. the SW distance between two uniform discrete measures with the same amount of points as a function of the support $Y \in \mathbb{R}^{n \times d}$ of one of the measures. We investigate the regularity and optimisation properties of this energy, as well as its Monte-Carlo approximation $\mathcal{E}_p$ (estimating the expectation in SW using only $p$ samples) and show convergence results on the critical points of $\mathcal{E}_p$ to those of $\mathcal{E}$, as well as an almost-sure uniform convergence. Finally, we show that in a certain sense, Stochastic Gradient Descent methods minimising $\mathcal{E}$ and $\mathcal{E}_p$ converge towards (Clarke) critical points of these energies.
Luminosity for laser-electron colliders

High intensity laser facilities are expanding their scope from laser and particle-acceleration test beds to user facilities and nuclear physics experiments. A basic goal is to confirm long-standing predictions of strong-field quantum electrodynamics, but with the advent of high-repetition rate laser experiments producing GeV-scale electrons and photons, novel searches for new high-energy particle physics also become possible. The common figure of merit for these facilities is the invariant $\chi\simeq 2\gamma_e|\vec E_{\rm laser}|/E_c$ describing the electric field strength in the electron rest frame relative to the ``critical'' field strength of quantum electrodynamics where the vacuum decays into electron-positron pairs. However, simply achieving large $\chi$ is insufficient; discovery or validation requires statistics to distinguish physics from fluctuations. The number of events delivered by the facility is therefore equally important. In high-energy physics, luminosity quantifies the rate at which colliders provide events and data. We adapt the definition of luminosity to high-intensity laser-electron collisions to quantify and thus optimize the rate at which laser facilities can deliver strong-field QED and potentially new physics events. Modeling the pulsed laser field and electron bunch, we find that luminosity is maximized for laser focal spot size equal or slightly larger than the diameter of the dense core of the electron bunch. Several examples show that luminosity can be maximized for parameters different from those maximizing the peak value of $\chi$ in the collision. The definition of luminosity for electron-laser collisions is straightforwardly extended to photon-laser collisions and lepton beam-beam collisions.
Learning Formal Specifications from Membership and Preference Queries

Active learning is a well-studied approach to learning formal specifications, such as automata. In this work, we extend active specification learning by proposing a novel framework that strategically requests a combination of membership labels and pair-wise preferences, a popular alternative to membership labels. The combination of pair-wise preferences and membership labels allows for a more flexible approach to active specification learning, which previously relied on membership labels only. We instantiate our framework in two different domains, demonstrating the generality of our approach. Our results suggest that learning from both modalities allows us to robustly and conveniently identify specifications via membership and preferences.
Absolutist AI

This paper argues that training AI systems with absolute constraints -- which forbid certain acts irrespective of the amount of value they might produce -- may make considerable progress on many AI safety problems in principle. First, it provides a guardrail for avoiding the very worst outcomes of misalignment. Second, it could prevent AIs from causing catastrophes for the sake of very valuable consequences, such as replacing humans with a much larger number of beings living at a higher welfare level. Third, it makes systems more corrigible, allowing creators to make corrective interventions in them, such as altering their objective functions or shutting them down. And fourth, it helps systems explore their environment more safely by prohibiting them from exploring especially dangerous acts. I offer a decision-theoretic formalization of an absolute constraints, improving on existing models in the literature, and use this model to prove some results about the training and behavior of absolutist AIs. I conclude by showing that, although absolutist AIs will not maximize expected value, they will not be susceptible to behave irrationally, and they will not (contra coherence arguments) face environmental pressure to become expected-value maximizers.
Mass and decay of the $s\bar{s}$ member of the $1^{3}F_{4}$ meson nonet

The mass and decay of the $s\bar{s}$ member of the $1^{3}F_{4}$ meson nonet are investigated in the framework of the Regge phenomenology and the $^{3}P_{0}$ model. We propose, based on the results, that the assignment of the $s\bar{s}$ member of the $1^{3}F_{4}$ meson nonet will require additional testing in the future. Our results also provide information for future studies of the $1^{3}F_{4}$ meson nonet.
Dark-energy properties of a spacetime-symmetry breaking cosmological solution -- late-time behaviour and phantom crossing

We investigate the late-time cosmological dynamics in a simple case of explicit spacetime-symmetry breaking. By expanding in a small symmetry-breaking coefficient we are able to write the Friedmann equations as $\Lambda$CDM + dynamical dark energy, which we show contains logarithmic dependence of the scale factor. We find that the dark energy equation of state displays divergences and phantom behaviour for certain values of the symmetry-breaking coefficient, where the Null Energy Condition is also broken. We also discuss the adiabatic sound speed of dark energy and compare the model to current constraints using the Chevallier-Polarski-Linder parametrisation.
Multi-band Metasurface-Driven Surface-Enhanced Infrared Absorption Spectroscopy for Improved Characterization of in-Situ Electrochemical Reactions

Surface-enhanced spectroscopy techniques are the method-of-choice to characterize adsorbed intermediates occurring during electrochemical reactions, which are crucial in realizing a green and sustainable future. Characterizing species with low coverage or short lifetimes has so far been limited by low signal enhancement. Recently, single-band metasurface-driven surface-enhanced infrared absorption spectroscopy (SEIRAS) has been pioneered as a promising technology to monitor a single vibrational mode during electrochemical CO oxidation. However, electrochemical reactions are complex, and their understanding requires the simultaneous monitoring of multiple adsorbed species in situ, hampering the adoption of nanostructured electrodes in spectro-electrochemistry. Here, we develop a multi-band nanophotonic-electrochemical platform that simultaneously monitors in situ multiple adsorbed species emerging during cyclic voltammetry scans by leveraging the high resolution offered by the reproducible nanostructuring of the working electrode. Specifically, we studied the electrochemical reduction of CO2 on a Pt surface and used two separately tuned metasurface arrays to monitor two adsorption configurations of CO with vibrational bands at ∼2030 and ∼1840 cm–1. Our platform provides a ∼40-fold enhancement in the detection of characteristic absorption signals compared to conventional broadband electrochemically roughened platinum films. A straightforward methodology is outlined starting with baselining our system in a CO-saturated environment and clearly detecting both configurations of adsorption. In contrast, during the electrochemical reduction of CO2 on platinum in K2CO3, CO adsorbed in a bridged configuration could not be detected. We anticipate that our technology will guide researchers in developing similar sensing platforms to simultaneously detect multiple challenging intermediates, with low surface coverage or short lifetimes.