Miracle-dz/UniFAR-FTUs huggingface dataset:
Liability regimes in the age of AI: a use-case driven analysis of the burden of proof

New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better. In particular, data-driven learning approaches (i.e., Machine Learning (ML)) have been a true revolution in the advancement of multiple technologies in various application domains. But at the same time there is growing concern about certain intrinsic characteristics of these methodologies that carry potential risks to both safety and fundamental rights. Although there are mechanisms in the adoption process to minimize these risks (e.g., safety regulations), these do not exclude the possibility of harm occurring, and if this happens, victims should be able to seek compensation. Liability regimes will therefore play a key role in ensuring basic protection for victims using or interacting with these systems. However, the same characteristics that make AI systems inherently risky, such as lack of causality, opacity, unpredictability or their self and continuous learning capabilities, may lead to considerable difficulties when it comes to proving causation. This paper presents three case studies, as well as the methodology to reach them, that illustrate these difficulties. Specifically, we address the cases of cleaning robots, delivery drones and robots in education. The outcome of the proposed analysis suggests the need to revise liability regimes to alleviate the burden of proof on victims in cases involving AI technologies. This article appears in the AI & Society track.
Prospects for a Precise Equation of State Measurement from Advanced LIGO and Cosmic Explorer

Gravitational-wave observations of neutron star mergers can probe the nuclear equation of state by measuring the imprint of the neutron star’s tidal deformability on the signal. We investigate the ability of future gravitational-wave observations to produce a precise measurement of the equation of state from binary neutron star inspirals. Because measurability of the tidal effect depends on the equation of state, we explore several equations of state that span current observational constraints. We generate a population of binary neutron stars as seen by a simulated Advanced LIGO–Virgo network, as well as by a planned Cosmic Explorer observatory. We perform Bayesian inference to measure the parameters of each signal, and we combine measurements across each population to determine R 1.4, the radius of a 1.4 M ⊙ neutron star. We find that, with 321 signals, the LIGO–Virgo network is able to measure R 1.4 to better than 2% precision for all equations of state we consider; however, we also find that achieving this precision could take decades of observation, depending on the equation of state and the merger rate. On the other hand, we find that with one year of observation, Cosmic Explorer will measure R 1.4 to better than 0.6% precision. In both cases, we find that systematic biases, such as from an incorrect mass prior, can significantly impact measurement accuracy, and efforts will be required to mitigate these effects.
Kinks Multiply Mesons

In a (1+1)-dimensional scalar quantum field theory, we calculate the leading-order probability of meson multiplication, which is the inelastic scattering process: kink + meson $\rightarrow$ kink + 2 mesons. We also calculate the differential probability with respect to the final meson momenta and the probability that one or two of the final mesons recoils back towards the source. In the ultrarelativistic limit of the initial meson, the total probability tends to a constant, which we calculate analytically in the $\phi^4$ model. At this order the meson sector conserves energy on its own, while the incoming meson applies a positive pressure to the kink. This is in contrast with the situation in classical field theory, where Romanczukiewicz and collaborators have shown that, in the presence of a reflectionless kink, only meson fusion is allowed, resulting in a negative radiation pressure on the kink.
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

We propose an adaptive variance-reduction method, called AdaSpider, for minimization of $L$-smooth, non-convex functions with a finite-sum structure. In essence, AdaSpider combines an AdaGrad-inspired [Duchi et al., 2011, McMahan&Streeter, 2010], but a fairly distinct, adaptive step-size schedule with the recursive stochastic path integrated estimator proposed in [Fang et al., 2018]. To our knowledge, Adaspider is the first parameter-free non-convex variance-reduction method in the sense that it does not require the knowledge of problem-dependent parameters, such as smoothness constant $L$, target accuracy $\epsilon$ or any bound on gradient norms. In doing so, we are able to compute an $\epsilon$-stationary point with $\tilde{O}\left(n + \sqrt{n}/\epsilon^2\right)$ oracle-calls, which matches the respective lower bound up to logarithmic factors.
Open-Vocabulary Argument Role Prediction for Event Extraction

The argument role in event extraction refers to the relation between an event and an argument participating in it. Despite the great progress in event extraction, existing studies still depend on roles pre-defined by domain experts. These studies expose obvious weakness when extending to emerging event types or new domains without available roles. Therefore, more attention and effort needs to be devoted to automatically customizing argument roles. In this paper, we define this essential but under-explored task: open-vocabulary argument role prediction. The goal of this task is to infer a set of argument roles for a given event type. We propose a novel unsupervised framework, RolePred for this task. Specifically, we formulate the role prediction problem as an in-filling task and construct prompts for a pre-trained language model to generate candidate roles. By extracting and analyzing the candidate arguments, the event-specific roles are further merged and selected. To standardize the research of this task, we collect a new event extraction dataset from WikiPpedia including 142 customized argument roles with rich semantics. On this dataset, RolePred outperforms the existing methods by a large margin. Source code and dataset are available on our GitHub repository: https://github.com/yzjiao/RolePred
Seeing the Unseen: Errors and Bias in Visual Datasets

From face recognition in smartphones to automatic routing on self-driving cars, machine vision algorithms lie in the core of these features. These systems solve image based tasks by identifying and understanding objects, subsequently making decisions from these information. However, errors in datasets are usually induced or even magnified in algorithms, at times resulting in issues such as recognising black people as gorillas and misrepresenting ethnicities in search results. This paper tracks the errors in datasets and their impacts, revealing that a flawed dataset could be a result of limited categories, incomprehensive sourcing and poor classification.
Proceedings of the NATO Advanced Study Institute on Physics and Applications of Quantum Wells and Superlattices Held in Erice, Italy on 21 April - 1 May 1987. Series B. Volume 170

Abstract : Contents: A Perspective in Quantum-Structure Development; Electronic States in Semiconductor Heterostructures; Molecular Beam Epitaxy of Artificially Layered III-V Semiconductors on an Atomic Scale; Molecular Beam Epitaxial Growth and Properties of Hg(-) based Microstructures; Strained Layer Superlattices; Electrical Transport in Microstructures; Physics of Resonant Tunneling in Semiconductors; Thermodynamic and Magneto-Optic Investigations of the Landau- level Density of States for 2D Electrons; High Field Magnetotransport: Lectures I and II: Analysis of Shubnikov-de Haas Oscillations and Parallel Field Magnetotransport; Physics and Applications of The Quantum Hall Effect; High Field Magnetotransport: Lecture III: The Fractional Quantum Hall Effect; Optical Properties of Quantum Wells; Raman Spectroscopy for the Study of Semiconductor Heterostructures and Superlattices; Far-Infrared Spectroscopy in Two-Dimensional Electronic Systems; Magneto-optical Properties of Heterojunctions, Quantum Wells, and Superlattices; Band-gap Engineering for New Photonic and Electronic Devices; Hot-Electron Spectroscopy and Transistor Design; Novel Tunneling Structures: Physics and Device Implications; and Opto-electronics in Semiconductor Quantum Wells Structures: Physics and Applications. Books; Symposia.
Analysis of luminosity measurements of the pre-white dwarf PG 1159-035

The study of the luminosity measurements of the pre-white dwarf PG 1159-035 has established the properties of the rich power spectrum of the detected radiation and, derived thereof, the physical properties of this celestial body. Those of the measurements which are available online are analysed in this work from a different perspective. After the measurements were band-passed, they were split into two parts (of comparable sizes), one yielding the training (learning) set (i.e., the database of embedding vectors and associated predictions), the other the test set. The optimal embedding dimension $m_0=10$ was obtained using Cao's method; this result was confirmed by an analysis of the correlation dimension. Subsequently, the extraction of the maximal Lyapunov exponent $\lambda$ was pursued for embedding dimensions $m$ between $3$ and $12$; results were obtained after removing the prominent undulations of the out-of-sample prediction-error arrays $S (k)$ by fitting a monotonic function to the data. The grand mean of the values, obtained for sufficient embedding dimensions ($10 \leq m \leq 12$), was: $\lambda = (9.2 \pm 1.0 ({\rm stat.}) \pm 2.7 ({\rm syst.})) \cdot 10^{-2}~\Delta \tau^{-1}$, where $\Delta \tau=10$ s is the sampling interval in the measurements. On the basis of this significantly non-zero result, it may be concluded that the physical processes, underlying the variation of the luminosity of PG 1159-035, are non-linear. The aforementioned result for $\lambda$ was obtained using the $L^\infty$-norm distance; a larger, yet not incompatible, result was extracted with the Euclidean ($L^2$-norm) distance.
Self Supervised Low Dose Computed Tomography Image Denoising Using Invertible Network Exploiting Inter Slice Congruence

The resurgence of deep neural networks has created an alternative pathway for low-dose computed tomography denoising by learning a nonlinear transformation function between low-dose CT (LDCT) and normal-dose CT (NDCT) image pairs. However, those paired LDCT and NDCT images are rarely available in the clinical environment, making deep neural network deployment infeasible. This study proposes a novel method for self-supervised low-dose CT denoising to alleviate the requirement of paired LDCT and NDCT images. Specifically, we have trained an invertible neural network to minimize the pixel-based mean square distance between a noisy slice and the average of its two immediate adjacent noisy slices. We have shown the aforementioned is similar to training a neural network to minimize the distance between clean NDCT and noisy LDCT image pairs. Again, during the reverse mapping of the invertible network, the output image is mapped to the original input image, similar to cycle consistency loss. Finally, the trained invertible network’s forward mapping is used for denoising LDCT images. Extensive experiments on two publicly available datasets showed that our method performs favourably against other existing unsupervised methods.
Inertial migration of a sphere in plane Couette flow

Abstract We study the inertial migration of a torque-free neutrally buoyant sphere in wall-bounded plane Couette flow over a wide range of channel Reynolds numbers $Re_c$ in the limit of small particle Reynolds number ($Re_p\ll 1$) and confinement ratio ($\lambda \ll 1$). Here, $Re_c = V_{wall}H/\nu$, where $H$ denotes the separation between the channel walls, $V_\text {wall}$ denotes the speed of the moving wall, and $\nu$ is the kinematic viscosity of the Newtonian suspending fluid. Also, $\lambda = a/H$, where $a$ is the sphere radius, with $Re_p=\lambda ^2 Re_c$. The channel centreline is found to be the only (stable) equilibrium below a critical $Re_c$ ($\approx 148$), consistent with the predictions of earlier small-$Re_c$ analyses. A supercritical pitchfork bifurcation at the critical $Re_c$ creates a pair of stable off-centre equilibria, located symmetrically with respect to the centreline, with the original centreline equilibrium becoming unstable simultaneously. The new equilibria migrate wall-ward with increasing $Re_c$. In contrast to the inference based on recent computations, the aforementioned bifurcation occurs for arbitrarily small $Re_p$ provided that $\lambda$ is sufficiently small. An analogous bifurcation occurs in the two-dimensional scenario, that is, for a circular cylinder suspended freely in plane Couette flow, with the critical $Re_c$ being approximately $110$.
On the continuum limit of epidemiological models on graphs: convergence and approximation results

We focus on an epidemiological model (the archetypical SIR system) defined on graphs and study the asymptotic behavior of the solutions as the number of vertices in the graph diverges. By relying on the theory of so called graphons we provide a characterization of the limit and establish convergence results. We also provide approximation results for both deterministic and random discretizations.
Search for microscopic evidence for molecular level negative thermal expansion in fullerenes

We report the high-resolution far infrared vibrational properties of C60 and endohedral Kr@C60 fullerene as a function of temperature. Anomalous softening of the F1u(1) mode (526 cm-1) is observed throughout the temperature range of investigation (300 - 10 K) suggesting that the fullerene cage may expand at low temperature in these molecular solids. To test this idea, we combine these results with temperature and pressure dependent Raman, infrared, and Kr extended x-ray absorption fine structure data from the literature to provide a comprehensive view of local cage size effects. The results are consistent with a recent molecular dynamics study [Kwon et al., Phys. Rev. Lett. 92, 15901 (2004)] that predicts negative thermal expansion in carbon fullerenes.
Analysis of Repeated-Measurements Data from Randomized Block Experiments

SUMMARY In this paper we consider the problem where repeated measurements are taken on each experimental unit of a randomized experimental design. If the design is a randomized complete block, then traditionally a split-block analysis is used. Here we considei extensions of the traditional split-block analysis where we allow for several orthogonal autocorrelated error components depending on the definitions of fixed and random effects. Our approach is based on time series models applied to separate orthogonal components of the data and it can also be used for more complex designs such as Latin squares.
NEUTRINO PHYSICS AND ASTROPHYSICS (Elementary Particles and Fields. Theory) Neutrino Opacity in Cold Neutron Matter

The weak dynamic form factors of cold neutron matter have been calculated within correlated basis function (CBF) theory using a realistic Hamiltonian. The results show that the effect of nucleon- nucleon correlations on the density and spin-density responses are different. The role of long-range correlations has been investigated comparing the CBF responses to those resulting from Landau theory of Fermi liquids. The neutrino mean free path has been obtained combining the two approaches. In this paper, we focus on the analysis of the density-density and spin-density dynamic form fac- tors (FF) constructed within the different approaches and apply them to the calculation of the elastic and quasielastic neutrino cross section and its mean free path. An approach which takes some care of the nucleon-nucleon interaction and of the consequent nucleon property modifications based on the corre- lated basis function (CBF) is suggested. This effect is sizable with respect to calculations performed within the Fermi gas (FG) approximation. Comparing the calculated FF with the corresponding results ob- tained within the Landau theory of Fermi liquids (LT), we find some difference between them. Therefore, we match these two approaches, LT and CBF, to cal- culate the elastic neutrino mean free path due to the neutral weak current.
Data Converter Design Space Exploration for IoT Applications: An Overview of Challenges and Future Directions

— Human lives are improving with the widespread use of cutting-edge digital technology like the Internet of Things (IoT). Recently, the pandemic has shown the demand for more digitally advanced IoT-based devices. International Data Corporation (IDC) forecasts that by 2025, there will be approximately 42 billion of these devices in use, capable of producing around 80 ZB (zettabytes) of data. So data acquisition, processing, communication, and visualization are necessary from a functional standpoint. Indicating sensors & data converters are the key components for IoT-based applications. The efficiency of such applications is truly measured in terms of latency, power, and resolution of data converters motivating designers to perform efficiently. Sensors capture and covert physical features from their chosen environment into detectable quantities. Data converter gives meaningful information and connects the real analog world to the digital component of the devices. The received data is interpreted and analyzed with the digital processing circuitry. Ultimately, it is used as information by a network of internet-connected smart devices. Because IoT technologies are adaptable to nearly any technology that may provide its operational activity and environmental conditions. But the challenges occur with power consumption as the complete IoT framework is battery operated and replacing a battery is a daunting task. So the goal of this chapter is to unveil the requirements to design energy-efficient data converters for IoT applications.
Testing highly nonlinear ﬁber for squeezed-light generation

Squeezed light, which is easily degraded by loss, could beneﬁt from generation directly in optical ﬁber. Furthermore, highly nonlinear ﬁber could offer more efﬁcient generation with lower pump power and shorter ﬁber lengths than standard single-mode ﬁber. We investigate non-polarization-maintaining highly nonlinear ﬁber (HNLF) for squeezed-light generation by characterizing possible sources of excess noise, including its zero-dispersion wavelength (ZDW) variation and polarization noise. We ﬁnd signif-icant ZDW variation and excess polarization noise. We believe the polarization noise is from non-linear polarization-mode dispersion. These ﬁndings are indicative of excess noise that would reduce and poten-tially completely overtake any squeezing generated, except possibly using intensity difference detection.
Dark Matter Substructure in Numerical Simulations: A Tale of Discreteness Noise, Runaway Instabilities, and Artificial Disruption

To gain understanding of the complicated, non-linear, and numerical processes associated with the tidal evolution of dark matter subhaloes in numerical simulation, we perform a large suite of idealized simulations that follow individual N-body subhaloes in a fixed, analytical host halo potential. By varying both physical and numerical parameters, we investigate under what conditions the subhaloes undergo disruption. We confirm the conclusions from our more analytical assessment in van den Bosch et al. that most disruption is numerical in origin; as long as a subhalo is resolved with sufficient mass and force resolution, a bound remnant survives. This implies that state-of-the-art cosmological simulations still suffer from significant overmerging. We demonstrate that this is mainly due to inadequate force softening, which causes excessive mass loss and artificial tidal disruption. In addition, we show that subhaloes in N-body simulations are susceptible to a runaway instability triggered by the amplification of discreteness noise in the presence of a tidal field. These two processes conspire to put serious limitations on the reliability of dark matter substructure in state-of-the-art cosmological simulations. We present two criteria that can be used to assess whether individual subhaloes in cosmological simulations are reliable or not, and advocate that subhaloes that satisfy either of these two criteria be discarded from further analysis. We discuss the potential implications of this work for several areas in astrophysics.
Inhibition of connexin hemichannels alleviates neuroinflammation and hyperexcitability in temporal lobe epilepsy

Significance Although symptomatic medications are available, many temporal lobe epilepsy (TLE) patients remain unresponsive to treatment and new drug targets are needed. Connexin-based gap junctions and hemichannels in brain glial cells are implicated in TLE, but blocking gap junctions can lead to undesirable side effects. Here, we studied the effect of a small organic molecule called D4, which selectively blocks connexin hemichannels, and tested its efficacy in treating TLE using a mouse model. In vivo application of D4 strongly suppresses TLE-induced hemichannel activity and neuroinflammation, curbs TLE seizures, and increases the survival rate. Thus, targeted blockade of brain connexin hemichannels with D4 is a potentially effective strategy to treat TLE symptoms and neuropathology.
Topological Landau Lattice

The concept of topological fermions, including Weyl and Dirac fermions, stems from the quantum Hall state induced by a magnetic field, but the definitions and classifications of topological fermions are formulated without using magnetic field. It is unclear whether and how the topological information of topological fermions can be probed once their eigen spectrum is completely rebuilt by a strong magnetic field. In this work, we provide an answer via mapping Landau levels (bands) of topological fermions in $d$ dimensions to the spectrum of a $(d-1)$-dimensional lattice model. The resultant "Landau lattice" may correspond to a topological insulator, and its topological property can be determined by real-space topological invariants. Accordingly, each zero-energy Landau level (band) inherits the topological stability from the corresponding topological boundary state of the Landau lattice. The theory is demonstrated in detail by transforming 2D Dirac fermions under magnetic fields to the Su-Schrieffer-Heeger models in class AIII, and 3D Weyl fermions to the Chern insulators in class A.