Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Implementation of Minimax Adaptive Controller For Finite Set of Linear Systems

—This paper deals with a distributed implemen- tation of minimax adaptive control algorithm for networked dynamical systems modeled by a ﬁnite set of linear models. To hedge against the uncertainty arising out of ﬁnite number of possible dynamics in each node in the network, it collects only the historical data of its neighboring nodes to decide its control action along its edges. This makes our proposed distributed ap- proach scalable. Numerical simulations demonstrate that once each node has sufﬁciently estimated the uncertain parameters, the distributed minimax adaptive controller behaves like the optimal distributed H-inﬁnity controller in hindsight.
Design of Artificial Noise for Physical Layer Security in Visible Light Systems With Clipping

Though visible light communication (VLC) systems are contained in a given room, ensuring their security amongst users in a room is essential. In this paper, the design of artificial noise (AN) to enhance physical layer security in VLC systems is studied in the context of input signals with no explicit amplitude constraint (such as multicarrier systems). In such systems, clipping is needed to constrain the input signals within the limited linear ranges of the LEDs. However, this clipping process gives rise to non-linear clipping distortion, which must be incorporated into the AN design. To facilitate the solution of this problem, a sub-optimal design approach is presented using the Charnes-Cooper transformation and the convex-concave procedure (CCP). Numerical results show that the clipping distortion significantly reduces the secrecy level, and using AN is advantageous over the no-AN scheme in improving the secrecy performance.
Half-metallicity in aluminum-doped zigzag silicene nanoribbons

The spin-dependent electronic structures of aluminum-(Al) doped zigzag silicene nanoribbons (ZSiNRs) are investigated by first-principles calculations. When ZSiNRs are substitutionally doped by a single Al atom on different sites in every three primitive cells, they become half-metallic in some cases, a property that can be used in spintronic devices. More interestingly, spin-down electrons can be transported at the Fermi energy when the Al atom is placed on the sub-edge site. In contrast, spin-up electrons can be transported at the Fermi energy when the ZSiNRs are doped on sites near their centre. The magnetic moment on the edge is considerably suppressed if the Al atom is doped on edge or near-edge sites. Similar results are obtained for a phosphorus-(P) and boron-(B) doped ZSiNR. When two or more Si atoms are replaced by Al atoms, in general the half-metallic behaviour is replaced by a metallic, spin gapless semiconducting or semiconducting one. When a line of six Si atoms, along the ribbon's width, are replaced by Al atoms, the spin resolution of the band structure is suppressed and the system becomes nonmagnetic.
Healthcare serial killer or coincidence? Statistical issues in investigation of suspected medical misconduct

Justice systems are sometimes called upon to evaluate cases in which healthcare professionals are suspected of killing their patients illegally. These cases are difficult to evaluate because they involve at least two levels of uncertainty. Commonly in a murder case it is clear that a homicide has occurred, and investigators must resolve uncertainty about who is responsible. In the cases we examine here there is also uncertainty about whether homicide has occurred. Investigators need to consider whether the deaths that prompted the investigation could plausibly have occurred for reasons other than homicide, in addition to considering whether, if homicide was indeed the cause, the person under suspicion is responsible. In this report (commissioned by the Section on Forensic Statistics of the Royal Statistical Society, London) we provide advice and guidance on the investigation and evaluation of such cases. Our work was prompted by concerns about the statistical challenges such cases pose for the legal
A Novel Parallel Triangle Counting Algorithm with Reduced Communication

—Counting and ﬁnding triangles in graphs is often used in real-world analytics for characterizing the cohesiveness and identifying communities in graphs. In this paper, we present novel sequential and parallel triangle counting algorithms based on identifying horizontal-edges in a breadth-ﬁrst search (BFS) traversal of the graph. The BFS allows our algorithm to drastically reduce the number of edges examined for set intersections. Our new approach is the ﬁrst communication-optimal parallel algorithm that asymptotically reduces the communication on massive graphs such as from real social networks and synthetic graphs from the Graph500 Benchmark. In our estimate from massive-scale Graph500 graphs, our new algorithms reduces the communication by 21x on a scale 36 and by 176x on a scale 42. Because communication is known to be the dominant cost of parallel triangle counting, our new parallel algorithm, to our knowledge, is now the fastest method for counting triangles in large graphs.
To Improve Is to Change: Towards Improving Mood Prediction by Learning Changes in Emotion

Although the terms mood and emotion are closely related and often used interchangeably, they are distinguished based on their duration, intensity and attribution. To date, hardly any computational models have (a) examined mood recognition, and (b) modelled the interplay between mood and emotional state in their analysis. In this paper, as a first step towards mood prediction, we propose a framework that utilises both dominant emotion (or mood) labels, and emotional change labels on the AFEW-VA database. Experiments evaluating unimodal (trained only using mood labels) and multimodal (trained with both mood and emotion change labels) convolutional neural networks confirm that incorporating emotional change information in the network training process can significantly improve the mood prediction performance, thus highlighting the importance of modelling emotion and mood simultaneously for improved performance in affective state recognition.
Distribution of Si, Fe, and Ni in the Intracluster Medium of the Coma Cluster

We studied the distributions of Si, Fe, and Ni in the intracluster medium (ICM) of the Coma cluster, one of the largest clusters in the nearby universe, using XMM-Newton data up to 0.5 r180 and Suzaku data of the central region up to 0.16 r180. Using the flux ratios of Ly alpha of H-like Si and 7.8 keV blend to K alpha of He-like Fe, the abundance ratios of Si to Fe and Ni to Fe of the ICM were derived using APEC model v2.0. 1. The Si/Fe ratio in the ICM of the Coma cluster shows no radial gradient. The emission weighted averages of the Si/Fe ratio in the ICM within 0.0--0.2 r180, 0.2--0.5 r180, and 0.0--0.5 r180 are 0.97 +- 0.11, 1.05 +- 0.36 and 0.99 +- 0.13, respectively, in solar units using the solar abundance of Lodders (2003). These values are close to those of smaller clusters and groups of galaxies. Using the Suzaku data of the central region, the derived Ni/Fe ratio of the ICM is 0.6--1.5 in solar units, according to the same solar abundance table. The systematic difference in the derived abundance ratios by different plasma codes are about 10%. Therefore, for the ICM in the Coma cluster, the abundance pattern of Si, Fe, and Ni is consistent with the same mixture of the yields of SN II and SN Ia in our Galaxy. Within 0.5 r180}, the cumulative iron-mass-to-light ratio increases with radius, and its radial profile is similar to those of relaxed smaller clusters with cD galaxies at their center. Considering the observed Si/Fe ratio, the cumulative metal-mass-to-light ratios at 0.5 r180 are compared with theoretical expectations.
Policy Gradient for Reinforcement Learning with General Utilities

In Reinforcement Learning (RL), the goal of agents is to discover an optimal policy that maximizes the expected cumulative rewards. This objective may also be viewed as finding a policy that optimizes a linear function of its state-action occupancy measure, hereafter referred as Linear RL. However, many supervised and unsupervised RL problems are not covered in the Linear RL framework, such as apprenticeship learning, pure exploration and variational intrinsic control, where the objectives are non-linear functions of the occupancy measures. RL with non-linear utilities looks unwieldy, as methods like Bellman equation, value iteration, policy gradient, dynamic programming that had tremendous success in Linear RL, fail to trivially generalize. In this paper, we derive the policy gradient theorem for RL with general utilities. The policy gradient theorem proves to be a cornerstone in Linear RL due to its elegance and ease of implementability. Our policy gradient theorem for RL with general utilities shares the same elegance and ease of implementability. Based on the policy gradient theorem derived, we also present a simple sample-based algorithm. We believe our results will be of interest to the community and offer inspiration to future works in this generalized setting.
Cross-sections of doubly curved sheets as confined elastica

Significance Understanding how a featureless planar solid film selects a complex morphology is an important fundamental challenge. In particular, it remains unclear how the selection of an overall three-dimensional shape constrains the behavior of smaller-scale wrinkles or folds. Here, we investigate a prototypical system consisting of a thin-membraned balloon that is partially filled with water and pressurized with air. We propose a quasi-two-dimensional model that captures an array of features seen in the experiments, from the shapes of individual folds to how the membrane morphology changes with pressure. Our results establish a route to analyzing complex film morphologies across scales.
Contribution of classical correlation and entanglement in the non-classical teleportation fidelity

It is well known that entanglement is the resource of quantum teleportation. Teleportation can be accomplished using classical correlation (CC) with a teleportation ﬁdelity (TF) upto 2 / 3. In the present work we have studied TF, entanglement and CC in the presence of decoherence. We have found that signiﬁcant increment of CC with respect to the strength of decoherence can lead TF in the non-classical region while entanglement is decreasing. We have also studied the protection of TF and entanglement using the technique of weak measurement and reverse weak measurement (WMRWM). Here we found that maximum protection of entanglement does not optimize the TF and CC. While optimization of TF indicates maximization of CC. Therefore, both entanglement and classical correlation of the shared state take part in the teleportation in a complex manner that needs to be explored in the future.
Reward Learning with Trees: Methods and Evaluation

Recent efforts to learn reward functions from human feedback have tended to use deep neural networks, whose lack of transparency hampers our ability to explain agent behaviour or verify alignment. We explore the merits of learning intrinsically interpretable tree models instead. We develop a recently proposed method for learning reward trees from preference labels, and show it to be broadly competitive with neural networks on challenging high-dimensional tasks, with good robustness to limited or corrupted data. Having found that reward tree learning can be done effectively in complex settings, we then consider why it should be used, demonstrating that the interpretable reward structure gives significant scope for traceability, verification and explanation.
Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks

Although large language models have achieved impressive zero-shot ability, the huge model size generally incurs high cost. Recently, semi-parametric language models, which augment a smaller language model with an external retriever, have demonstrated promising language modeling capabilities. However, it remains unclear whether such semi-parametric language models can perform competitively well as their fully-parametric counterparts on zero-shot generalization to downstream tasks. In this work, we introduce $\text{Zemi}$, a zero-shot semi-parametric language model. To our best knowledge, this is the first semi-parametric language model that can demonstrate strong zero-shot performance on a wide range of held-out unseen tasks. We train $\text{Zemi}$ with a novel semi-parametric multitask prompted training paradigm, which shows significant improvement compared with the parametric multitask training as proposed by T0. Specifically, we augment the multitask training and zero-shot evaluation with retrieval from a large-scale task-agnostic unlabeled corpus. In order to incorporate multiple potentially noisy retrieved augmentations, we further propose a novel $\text{augmentation fusion}$ module leveraging perceiver resampler and gated cross-attention. Notably, our proposed $\text{Zemi}_\text{LARGE}$ outperforms T0-3B by 16% on all seven evaluation tasks while being 3.9x smaller in model size.
Differentiable Parsing and Visual Grounding of Verbal Instructions for Object Placement

—Grounding spatial relations in natural language for object placing could have ambiguity and compositionality issues. To address the issues, we introduce P ARA G ON , a PARsing And visual GrOuNding framework for language-conditioned object placement. It parses language instructions into relations between objects and grounds those objects in visual scenes. A particle-based GNN then conducts relational reasoning between grounded objects for placement generation. P ARA G ON encodes all of those procedures into neural networks for end-to-end training, which avoids annotating parsing and object reference grounding labels. Our approach inherently integrates parsing-based methods into a probabilistic, data-driven framework. It is data-efﬁcient and generalizable for learning compositional instructions, robust to noisy language inputs, and adapts to the uncertainty of ambiguous instructions.
Composition of Differential Privacy & Privacy Amplification by Subsampling

This chapter is meant to be part of the book “Differential Privacy for Artificial Intelligence Applications. ” We give an introduction to the most important property of differential privacy – composition: running multiple independent analyses on the data of a set of people will still be differentially private as long as each of the analyses is private on its own – as well as the related topic of privacy amplification by subsampling. This chapter introduces the basic concepts and gives proofs of the key results needed to apply these tools in practice. ∗Google Research . steinke@google.com 1 ar X iv :2 21 0. 00 59 7v 2 [ cs . C R ] 4 O ct 2 02 2
Characterization of effects of transfer learning across domains and languages

With ever-expanding datasets of domains, tasks and languages, transfer learning (TL) from pre-trained neural language models has emerged as a powerful technique over the years. Many pieces of research have shown the effectiveness of transfer learning across different domains and tasks. However, there remains uncertainty around when a transfer will lead to positive or negative impacts on performance of the model. To understand the uncertainty, we investigate how TL affects the performance of popular pre-trained models like BERT, RoBERTa and XLNet over three natural language processing (NLP) tasks. We believe this work will inform about speciﬁcs on when and what to transfer related to domain, multi-lingual dataset and various NLP tasks.
How Relevant is Selective Memory Population in Lifelong Language Learning?

Lifelong language learning seeks to have models continuously learn multiple tasks in a sequential order without suffering from catastrophic forgetting. State-of-the-art approaches rely on sparse experience replay as the primary approach to prevent forgetting. Experience replay usually adopts sampling methods for the memory population; however, the effect of the chosen sampling strategy on model performance has not yet been studied. In this paper, we investigate how relevant the selective memory population is in the lifelong learning process of text classification and question-answering tasks. We found that methods that randomly store a uniform number of samples from the entire data stream lead to high performances, especially for low memory size, which is consistent with computer vision studies.
Periodic orbits in evolutionary game dynamics: An information-theoretic perspective

Even though existence of non-convergent evolution of the states of populations in ecological and evolutionary contexts is an undeniable fact, insightful game-theoretic interpretations of such outcomes are scarce in the literature of evolutionary game theory. Here we tap into the information-theoretic concept of relative entropy in order to construct a game-theoretic interpretation for periodic orbits in a wide class of evolutionary game dynamics. Eﬀectively, we present a consistent generalization of the evolutionarily stable strategy—the cornerstone of the evolutionary game theory—and aptly term the generalized concept: information stable orbit. The information stable orbit captures the essence of the evolutionarily stable strategy in that it compares the total payoﬀ obtained against an evolving mutant with the total payoﬀ that the mutant gets while playing against itself. Further-more, we discuss the connection of the information stable orbit with the dynamical stability of the corresponding periodic orbit.
Disentangling the high- and low-cutoff scales via the trilinear Higgs couplings in the type-I two-Higgs-doublet model

The type-I two-Higgs-doublet model in the inverted Higgs scenario can retain the theoretical stability all the way up to the Planck scale. The Planck-cutoff scale, $\Lambda_{\rm cut}^{\rm Planck}$, directly impacts the mass spectra such that all the extra Higgs boson masses should be light below about 160 GeV. However, the observation of the light masses of new Higgs bosons does not indicate the high cutoff scale because a low cutoff scale can also accommodate the light masses. Over the viable parameter points that satisfy the theoretical requirements and the experimental constraints, we show that the trilinear Higgs couplings for low $\Lambda_{\rm cut}$ are entirely different from those for the Planck-cutoff scale. The most sensitive coupling to the cutoff scale is from the $h$-$h$-$h$ vertex, where $h$ is the lighter CP-even Higgs boson at a mass below 125 GeV. Among the multi-Higgs productions mediated by Higgs bosons, the gluon fusion processes of $gg \to h h $ and $gg \to AA$ are insensitive to the cutoff scale, yielding a small variation of $\mathcal{O}(1)\,{\rm fb}$ according to $\Lambda_{\rm cut}$. The smoking-gun signature is from the triple Higgs production of $q\bar{q}' \to W^* \to H^\pm hh$, which solely depends on the $h$-$h$-$h$ vertex. The cross section for $\Lambda_{\rm cut}=1\,{\rm TeV}$ is about $10^3$ times larger than that for the Planck-cutoff scale. Since the decay modes of $H^\pm \to W^* h/W^* A$ and $h/A \to bb$ are dominant, the process yields the $6b+\ell\nu$ final state, which enjoys an almost background-free environment. Consequently, the precision measurement of $pp \to H^\pm hh$ can probe the cutoff scale of the model.
Feature Embedding by Template Matching as a ResNet Block

Convolution blocks serve as local feature extractors and are the key to success of the neural networks. To make local semantic feature embedding rather explicit, we reformulate convolution blocks as feature selection according to the best matching kernel. In this manner, we show that typical ResNet blocks indeed perform local feature embedding via template matching once batch normalization (BN) followed by a rectified linear unit (ReLU) is interpreted as arg-max optimizer. Following this perspective, we tailor a residual block that explicitly forces semantically meaningful local feature embedding through using label information. Specifically, we assign a feature vector to each local region according to the classes that the corresponding region matches. We evaluate our method on three popular benchmark datasets with several architectures for image classification and consistently show that our approach substantially improves the performance of the baseline architectures.
GSC 4560–02157: a new long-period eclipsing cataclysmic variable star

We study the newly discovered variable star GSC 4560–02157. CCD photometry was performed in 2013–2014 at the Tien Shan Astronomical Observatory, and a spectrum was obtained with the 6-m telescope of the Special Astrophysical Observatory in June, 2014. GSC 4560–02157 is demonstrated to be an eclipsing variable star (P = 0.265359d). All its flat-bottom primary minima are approximately at the same brightness level, but the star's out-of-eclipse brightness and brightness at secondary minima vary considerably (by up to 0.6m) from cycle to cycle. Moreover, there are short-term (time scale of 0.03–0.04 days) small-amplitude brightness variations out of eclipse. This behavior suggests a cataclysmic nature for the star, which is confirmed with a spectrum taken on 2014 June 5. The spectrum shows numerous emissions of the hydrogen Balmer series, HeI and HeII.
Extension of the Kramers-Kronig method for polarized infrared reflectance spectra from the face of low-symmetry crystals

An extension of the Kramers-Kronig method for treatment of polarized infrared reflectance spectra from the face of low-symmetry crystals, where directions of principal dielectric axes depend on frequency, is proposed. It is shown, how to obtain the frequency dependencies of the complex reflectivity tensor components, using three reflectance spectra measured for different directions of linear polarization of the incident wave, when reflected wave is immediately sent to the detector. The problem is formulated in a form of the system of integral equations, and effective numerical technique is found for solving it. The question of the further recovery of the complex dielectric tensor on the base of the reflectivity tensor, is discussed. The case of the monoclinic crystals is considered in details. An example of the application of the extended Kramers-Kronig method to the reflectance spectra from the (ac)-face of the single-crystal bismuth oxide with the monoclinic lattice is given.
Acoustic Localization and Communication Using a MEMS Microphone for Low-cost and Low-power Bio-inspired Underwater Robots

Having accurate localization capabilities is one of the fundamental requirements of autonomous robots. For underwater vehicles, the choices for effective localization are limited due to limitations of GPS use in water and poor environ-mental visibility that makes camera-based methods ineffective. Popular inertial navigation methods for underwater localization using Doppler-velocity log sensors, sonar, high-end inertial navigation systems, or acoustic positioning systems require bulky expensive hardware which are incompatible with low-cost, bio-inspired underwater robots. In this paper, we introduce an approach for underwater robot localization inspired by GPS methods known as acoustic pseudoranging. Our method allows us to potentially localize multiple bio-inspired robots equipped with commonly available micro electro-mechanical systems microphones. This is achieved through estimating the time difference of arrival of acoustic signals sent simultaneously through four speakers with a known constellation geometry. We also leverage the same acoustic framework to perform one-way communication with the robot to execute some primitive motions. To our knowledge, this is the first application of the approach for the on-board localization of small bio-inspired robots in water. Hardware schematics and the accompanying code are released to aid further development in the field33https://github.com/rpl-cmu/underwater-acoustic-pseudoranging.