Miracle-dz/UniFAR-FTUs huggingface dataset:
Fragmented Spatial Maps from Surprisal: State Abstraction and Efficient Planning

When animals explore spatial environments, their representations often fragment into multiple maps. What determines these map fragmentations, and can we predict where they will occur with simple principles? We pose the problem of fragmentation of an environment as one of (online) spatial clustering. Taking inspiration from the notion of a contiguous region in robotics, we develop a theory in which fragmentation decisions are driven by surprisal. When this criterion is implemented with boundary, grid, and place cells in various environments, it produces map fragmentations from the first exploration of each space. Augmented with a long-term spatial memory and a rule similar to the distance-dependent Chinese Restaurant Process for selecting among relevant memories, the theory predicts the reuse of map fragments in environments with repeating substructures. Our model provides a simple rule for generating spatial state abstractions and predicts map fragmentations observed in electrophysiological recordings. It further predicts that there should be “fragmentation decision” or “fracture” cells, which in multicompartment environments could be called “doorway” cells. Finally, we show that the resulting abstractions can lead to large (orders of magnitude) improvements in the ability to plan and navigate through complex environments.
The Impact of Secondary Schooling in Kenya: A Regression Discontinuity Analysis

ABSTRACT:I estimate the impacts of secondary school on human capital, occupational choice, and fertility for young adults in Kenya. Probability of admission to government secondary school rises sharply at a score close to the national mean on a standardized eighth grade examination, permitting me to estimate causal effects of schooling in a regression discontinuity framework. I combine administrative test score data with a survey of young adults to estimate these impacts. My results show that secondary schooling increases human capital. For men, I find a drop in low-skill self-employment; for women, I find a reduction in teen pregnancy.
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization

Federated learning (FL) is a useful tool in distributed machine learning that utilizes users’ local datasets in a privacy-preserving manner. When deploying FL in a constrained wireless environment; however, training models in a time-efficient manner can be a challenging task due to intermittent connectivity of devices, heterogeneous connection quality, and non-i.i. d. data. In this paper, we provide a novel convergence analysis of non-convex loss functions using FL on both i.i. d. and non-i.i. d. datasets with arbitrary device selection probabilities for each round. Then, using the derived convergence bound, we use stochastic optimization to develop a new client selection and power allocation algorithm that minimizes a function of the convergence bound and the average communication time under a transmit power constraint. We find an analytical solution to the minimization problem. One key feature of the algorithm is that knowledge of the channel statistics is not required and only the instantaneous channel state information needs to be known. Using the FEMNIST and CIFAR-10 datasets, we show through simulations that the communication time can be significantly decreased using our algorithm, compared to uniformly random participation.
Adversarial Jamming for a More Effective Constellation Attack

The common jamming mode in wireless commu-nication is band barrage jamming, which is controllable and difficult to resist. Although this method is simple to implement, it is obviously not the best jamming waveform. Therefore, based on the idea of adversarial examples, we propose the adversarial jamming waveform, which can independently optimize and find the best jamming waveform. We attack QAM with adversarial jamming and find that the optimal jamming waveform is equiva-lent to the amplitude and phase between the nearest constellation points. Furthermore, by verifying the jamming performance on a hardware platform, it is shown that our method significantly improves the bit error rate compared to other methods.
Cost-Effective Algorithms for Average-Case Interactive Graph Search

Interactive graph search (IGS) uses human intelligence to locate the target node in hierarchy, which can be applied for image classification, product categorization and searching a database. Specifically, IGS aims to categorize an object from a given category hierarchy via several rounds of interactive queries. In each round of query, the search algorithm picks a category and receives a boolean answer on whether the object is under the chosen category. The main efficiency goal asks for the minimum number of queries to identify the correct hierarchical category for the object. In this paper, we study the average-case interactive graph search (AIGS) problem that aims to minimize the expected number of queries when the objects follow a probability distribution. We propose a greedy search policy that splits the candidate categories as evenly as possible with respect to the probability weights, which offers an approximation guarantee of $O(\log n)$ for AIGS given the category hierarchy is a directed acyclic graph (DAG), where $n$ is the total number of categories. Meanwhile, if the input hierarchy is a tree, we show that a constant approximation factor of $(1+\sqrt{5})/2$ can be achieved. Furthermore, we present efficient implementations of the greedy policy, namely GreedyTree and GreedyDAG, that can quickly categorize the object in practice. Extensive experiments in real-world scenarios are carried out to demonstrate the superiority of our proposed methods.
European Strategy for Particle Physics -- Accelerator R&D Roadmap

The 2020 update of the European Strategy for Particle Physics emphasised the importance of an intensified and well-coordinated programme of accelerator R&D, supporting the design and delivery of future particle accelerators in a timely, affordable and sustainable way. This report sets out a roadmap for European accelerator R&D for the next five to ten years, covering five topical areas identified in the Strategy update. The R&D objectives include: improvement of the performance and cost-performance of magnet and radio-frequency acceleration systems; investigations of the potential of laser / plasma acceleration and energy-recovery linac techniques; and development of new concepts for muon beams and muon colliders. The goal of the roadmap is to document the collective view of the field on the next steps for the R&D programme, and to provide the evidence base to support subsequent decisions on prioritisation, resourcing and implementation.
Predicting and machine learning structural instabilities in 2D materials

We address the problem of predicting the zero temperature dynamical stability (DS) of a periodic crystal without computing its full phonon band structure. Using data for two-dimensional (2D) crystals, we first present statistical evidence that DS can be inferred with good reliability from the phonon frequencies at the center and boundary of the Brillouin zone (BZ). This analysis represents a validation of the DS test employed by the Computational 2D Materials Database (C2DB). For 137 dynamically unstable 2D crystals, we displace the atoms along an unstable mode and relax the structure. This procedure yields a dynamically stable crystal in 49 cases. The elementary properties of these new structures are characterised using the C2DB workflow, and it is found that their properties can differ significantly from those of the original unstable crystals, e.g. band gaps are opened by 0.3 eV on average. All the crystal structures and properties are available in the C2DB. Finally, we train a classification model on the DS data for 3295 2D materials in the C2DB using a representation encoding the electronic structure of the crystal. We obtain an excellent receiver operating characteristic (ROC) curve with an area under the curve (AUC) of 0.89, showing that the classification model can drastically reduce computational efforts in high-throughput studies. ar X iv :2 20 1. 08 09 1v 1 [ co nd -m at . m tr lsc i] 2 0 Ja n 20 22 Predicting and machine learning structural instabilities in 2D materials 2
LaMDA: Language Models for Dialog Applications

We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text. While model scaling alone can improve quality, it shows less improvements on safety and factual grounding. We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding. The first challenge, safety, involves ensuring that the model's responses are consistent with a set of human values, such as preventing harmful suggestions and unfair bias. We quantify safety using a metric based on an illustrative set of human values, and we find that filtering candidate responses using a LaMDA classifier fine-tuned with a small amount of crowdworker-annotated data offers a promising approach to improving model safety. The second challenge, factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator. We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible. Finally, we explore the use of LaMDA in the domains of education and content recommendations, and analyze their helpfulness and role consistency.
P-adic Simpson correpondence via prismatic crystals

Let $X$ be a proper smooth rigid analytic variety over a $p$-adic field $K$ with a good reduction $\mathfrak X$ over $\mathcal O_K$. In this paper, we construct a Simpson functor from the category of generalised representations on $X_{\rm proet}$ to the category of Higgs bundles on $X_{\mathbb C_p}$ with ${\rm Gal}(\bar K/K)$-actions using the methods in \cite{LZ} and \cite{DLLZ}. For the other direction, we construct an inverse Simpson functor from the category of Higgs bundles on $\mathfrak X$ with"arithmetic Sen operators"to the category of generalised representations on $X_{\rm proet}$ by using the prismatic theory developed in \cite{BS-a}, especially the category of Hodge--Tate crystals on $(\mathfrak X)_{\Prism}$. The main ingredient is the local computation of absolute prismatic cohomology, which is a generalisation of our previous work in \cite{MW-b}.
Numerical simulation of flow over flapping wings in tandem: Wingspan effects

We report direct numerical simulations of a pair of wings in horizontal tandem configuration, to analyze the effect of their aspect ratio on the flow and the aerodynamic performance of the system. The wings are immersed in a uniform free-stream at Reynolds number Re = 1000, and they undergo heaving and pitching oscillation with Strouhal number St = 0.7. The aspect ratios of forewing and hindwing vary between 2 and 4. The aerodynamic performance of the system is dictated by the interaction between the trailing edge vortex (TEV) shed by the forewing and the induced leading edge vortex formed on the hindwing. The aerodynamic performance of the forewing is similar to that of an isolated wing irrespective of the aspect ratio of the hindwing, with a small modulating effect produced by the forewing-hindwing interactions. On the other hand, the aerodynamic performance of the hindwing is clearly affected by the interaction with the forewing's TEV. Tandem configurations with a larger aspect ratio on the forewing than on the hindwing result in a quasi-two-dimensional flow structure on the latter. This yields an 8% increase in the time-averaged thrust coefficient of the hindwing, with no change in its propulsive efficiency.
A Survey of Disc Thickness and Viscosity in Circumbinary Accretion: Binary Evolution, Variability, and Disc Morphology

Much of the parameter space relevant to the evolution of astrophysical circumbinary accretion discs remains unexplored. We have carried out a suite of circumbinary disc simulations surveying both disc thickness and kinematic viscosity, using both constant-ν and constant-α prescriptions. We focus primarily on disc aspect ratios between 0.1 and 0.033, and on viscosities between ν = 0.0005 and ν = 0.008 (in units of binary semi-major axis and orbital frequency), and specialise to circular equal-mass binaries. Both factors strongly influence the evolution of the binary semi-major axis: at ν = 0.0005, inspirals occur at aspect ratios ≲ 0.059, while at ν = 0.004 inspirals occur only at aspect ratios ≲ 0.04. Inspirals occur largely because of the increasingly strong negative torque on the binary by streams of material which lag the binary, with negligible contributions from resonant torques excited in the circumbinary disc. We find that reductions in accretion rate occur when simulations are initialised too far from the eventual quasi-steady state driven by interaction with the binary, rather than being intrinsically linked to the disc aspect ratio. We find not only that the cavity size increases as viscosity is decreased, but that thinner circumbinary discs become more eccentric. Our results suggest that supermassive black hole binaries should be driven, more rapidly than previous estimates, from ∼parsec separations to distances where gravitational waves drive their inspiral, potentially reducing the number of binaries observable by pulsar timing arrays.
Serializable HTAP with Abort-/Wait-free Snapshot Read

Concurrency Control (CC) ensuring consistency of updated data is an essential element of OLTP systems. Recently, hybrid transactional/analytical processing (HTAP) systems developed for executing OLTP and OLAP have attracted much attention. The OLAP side CC domain has been isolated from OLTP's CC and in many cases has been achieved by snapshot isolation (SI) to establish HTAP systems. Although higher isolation level is ideal, considering OLAP read-only transactions in the context of OLTP scheduling achieving serializability forces aborts/waits and would be a potential performance problem. Furthermore, executing OLAP without affecting OLTP as much as possible is needed for HTAP systems. The aim of this study was serializability without additional aborts/waits. We propose read safe snapshot (RSS) using multiversion CC (MVCC) theory and introduce the RSS construction algorithm utilizing serializable snapshot isolation (SSI). For serializability of HTAP systems, our model makes use of multiversion and allows more schedules with read operations whose corresponding write operations do not participate in the dependency cycles. Furthermore, we implemented the algorithm practically in an open-source database system that offers SSI. Our algorithm was integrated into two types of architecture as HTAP systems called as unified (single-node) or decoupled (multinode) storage architecture. We evaluate the performance and abort rate of the single-node architecture where SSI is applicable. The multi-node architecture was investigated for examining the performance overhead applying our algorithm.
PDE-Based Optimal Strategy for Unconstrained Online Learning

Unconstrained Online Linear Optimization (OLO) is a practical problem setting to study the training of machine learning models. Existing works proposed a number of potential-based algorithms, but in general the design of these potential functions relies heavily on guessing. To streamline this workflow, we present a framework that generates new potential functions by solving a Partial Differential Equation (PDE). Specifically, when losses are 1-Lipschitz, our framework produces a novel algorithm with anytime regret bound $C\sqrt{T}+||u||\sqrt{2T}[\sqrt{\log(1+||u||/C)}+2]$, where $C$ is a user-specified constant and $u$ is any comparator unknown and unbounded a priori. Such a bound attains an optimal loss-regret trade-off without the impractical doubling trick. Moreover, a matching lower bound shows that the leading order term, including the constant multiplier $\sqrt{2}$, is tight. To our knowledge, the proposed algorithm is the first to achieve such optimalities.
Stitch it in Time: GAN-Based Facial Editing of Real Videos

The ability of Generative Adversarial Networks to encode rich semantics within their latent space has been widely adopted for facial image editing. However, replicating their success with videos has proven challenging. Applying StyleGAN editing to real videos introduces two main challenges: (i) StyleGAN operates over aligned crops. When editing videos, these crops need to be pasted back into the frame, resulting in a spatial inconsistency. (ii) Videos introduce a fundamental barrier to overcome — temporal coherency. To address the first challenge, we propose a novel stitching-tuning procedure. The generator is carefully tuned to overcome the spatial artifacts at crop borders, resulting in smooth transitions even when difficult backgrounds are involved. Turning to temporal coherence, we propose that this challenge is largely artificial. The source video is already temporally coherent, and deviations arise in part due to careless treatment of individual components in the editing pipeline. We leverage the natural alignment of StyleGAN and the tendency of neural networks to learn low-frequency functions, and demonstrate that they provide a strongly consistent prior. These components are combined in an end-to-end framework for semantic editing of facial videos. We compare our pipeline to the current state-of-the-art and demonstrate significant improvements. Our method produces meaningful manipulations and maintains greater spatial and temporal consistency, even on challenging talking head videos which current methods struggle with. Our code and videos are available at https://stitch-time.github. io/.
Insights from a computational analysis of the SARS‐CoV‐2 Omicron variant: Host–pathogen interaction, pathogenicity, and possible drug therapeutics

Prominently accountable for the upsurge of COVID‐19 cases as the world attempts to recover from the previous two waves, Omicron has further threatened the conventional therapeutic approaches. The lack of extensive research regarding Omicron has raised the need to establish correlations to understand this variant by structural comparisons. Here, we evaluate, correlate, and compare its genomic sequences through an immunoinformatic approach to understand its epidemiological characteristics and responses to existing drugs.
Linguistically Driven Multi-Task Pre-Training for Low-Resource Neural Machine Translation

In the present study, we propose novel sequence-to-sequence pre-training objectives for low-resource machine translation (NMT): Japanese-specific sequence to sequence (JASS) for language pairs involving Japanese as the source or target language, and English-specific sequence to sequence (ENSS) for language pairs involving English. JASS focuses on masking and reordering Japanese linguistic units known as bunsetsu, whereas ENSS is proposed based on phrase structure masking and reordering tasks. Experiments on ASPEC Japanese–English & Japanese–Chinese, Wikipedia Japanese–Chinese, News English–Korean corpora demonstrate that JASS and ENSS outperform MASS and other existing language-agnostic pre-training methods by up to +2.9 BLEU points for the Japanese–English tasks, up to +7.0 BLEU points for the Japanese–Chinese tasks and up to +1.3 BLEU points for English–Korean tasks. Empirical analysis, which focuses on the relationship between individual parts in JASS and ENSS, reveals the complementary nature of the subtasks of JASS and ENSS. Adequacy evaluation using LASER, human evaluation, and case studies reveals that our proposed methods significantly outperform pre-training methods without injected linguistic knowledge and they have a larger positive impact on the adequacy as compared to the fluency.
Avoiding Barren Plateaus Using Classical Shadows

Variational quantum algorithms are promising algorithms for achieving quantum advantage on near-term devices. The quantum hardware is used to implement a variational wave function and measure observables, whereas the classical computer is used to store and update the variational parameters. The optimization landscape of expressive variational ansätze is however dominated by large regions in parameter space, known as barren plateaus, with vanishing gradients, which prevents eﬃcient optimization. In this work we propose a general algorithm to avoid barren plateaus in the initialization and throughout the optimization. To this end we deﬁne a notion of weak barren plateaus (WBPs) based on the entropies of local reduced density matrices. The presence of WBPs can be eﬃciently quantiﬁed using recently introduced shadow tomography of the quantum state with a classical computer. We demonstrate that avoidance of WBPs suﬃces to ensure sizable gradients in the initialization. In addition, we demonstrate that decreasing the gradient step size, guided by the entropies allows WBPs to be avoided during the optimization process. This paves the way for eﬃcient barren plateau-free optimization on near-term devices. (LW). LW encounters a WBP for both learning rates that we consider (green star). In contrast, SA avoids the WBP for both learning rates. Good performance and further convergence in the local minimum is only achieved through a smaller learning rate of η = 0.01. We use a system size of N = 10 and a random circuit from Eq. (1) with circuit depth p = 100. Data is averaged over 100 random instances.
Topological frequency conversion in Weyl semimetals

We theoretically predict a new working principle for optical amplification, based on Weyl semimetals: when a Weyl semimetal is suitably irradiated at two frequencies, electrons close to the Weyl points convert energy between the frequencies through the mechanism of topological frequency conversion from [Martin et al, PRX 7 041008 (2017)]. Each electron converts energy at a quantized rate given by an integer multiple of Planck's constant multiplied by the product of the two frequencies. In simulations, we show that optimal, but feasible band structures can support topological frequency conversion in the"THz gap"at intensities down to $ 2{\rm W}/{\rm mm^2}$; the gain from the effect can exceed the dissipative loss when the frequencies are larger than the relaxation time of the system. Topological frequency conversion provides a new paradigm for optical amplification, and further extends Weyl semimetals' promise for technological applications.