Miracle-dz/UniFAR-FTUs huggingface dataset:
Twisting cubic rabbits

We solve an infinite family of twisted polynomial problems that are cubic generalizations of Hubbard's twisted rabbit problem. We show how the result of twisting by a power of a certain Dehn twist depends on the 9-adic expansion of the power. For the cubic rabbit with three post-critical points, we also give an algorithmic solution to the twisting problem for the full pure mapping class group.
Exact Solutions for Vector Phase-Matching Conditions in Nonlinear Uniaxial Crystals

The transcendental equations of vector phase matching are transformed into a fourth-order polynomial equation that admits an analytical solution. The real roots of this equation provide the optical axis orientations that are useful for efficient down-conversion in nonlinear uniaxial crystals. The production of entangled photon pairs is discussed in both collinear and non-collinear configurations of the spontaneous parametric down-conversion (SPDC) process. Degenerate and non-degenerate cases are also distinguished. As a practical example, SPDC processes of type-I and type-II are studied for beta-barium borate (BBO) crystals. The predictions are in very good agreement with experimental measurements already reported in the literature and include theoretical results of other authors as particular cases. Some properties that seem to be exclusive to BBO crystals are reported; the experimental verification of the latter would allow a better characterization of these crystals.
Color Dependence of the Transit Detectability of Young Active M Dwarfs

We investigate the planetary transit detectability in the presence of stellar rotational activity from light curves of young M dwarfs, and estimate the improvements in detection at near-infrared (NIR) wavelengths. By making maps of the transit signal detection efficiency over the orbital period and planetary radius with light curves of the members of four clusters observed by the K2 mission—Hyades, Praesepe, Pleiades, and Upper Scorpius—we evaluate the detectability of the rotation period and the modulation of the semi-amplitude. We find that the detection efficiency decreases remarkably, to about 20%, for rapid rotators with P rot ≤ 1 day, and that the lack of planets in Pleiades is likely due to the high fraction of rapidly rotating M dwarfs. We also evaluate the improvements in planet detection with NIR photometry via tests that use mock light curves, assuming that the signal amplitude of the stellar rotation decreases at NIR wavelengths. Our results suggest that NIR photometric monitoring will double the relative detection efficiency for transiting planetary candidates with P rot ≤ 1 day, and find planets around M dwarfs with approximately 100 Myr that are missing from previous transit surveys from space.
Generate novel and robust samples from data: accessible sharing without privacy concerns

Generating new samples from data sets can mitigate extra expensive operations, increased invasive procedures, and mitigate privacy issues. These novel samples that are statistically robust can be used as a temporary and intermediate replacement when privacy is a concern. This method can enable better data sharing practices without problems relating to identiﬁcation issues or biases that are ﬂaws for an adversarial attack.
Data Augmentation in Temporal and Polar Domains for Event-Based Learning

Event cameras are inherently suitable for spiking neural networks (SNNs) and have great potential in challenging scenes due to the advantages of bionics, asynchrony, high dynamic range, and no motion blur. However, novel data augmentations designed for event properties are required to process the unconventional output of these cameras in order to un-lock their potential. In this paper, we explore the extraordinary inﬂuence of brightness variations due to event properties. Along the way, two novel data augmentation methods, EventInvert and EventDrift (EventID), are proposed to simulate two basic transformations of this inﬂuence. Speciﬁcally, EventID inverts or drifts events in the stream through transformations in temporal and polar domains, thereby generating samples affected by brightness variances. Extensive experiments are carried out on the CIFAR10-DVS, N-Caltech101, and N-CARS datasets. It turns out that this simulation improves generalization by increasing the robustness of models against brightness variations. In addition, EventID is broadly effective, surpassing previous state-of-the-art performances. For example, the spiking neural network model with Even-tID achieves a state-of-the-art accuracy of 83.50% on the CIFAR10-DVS dataset.
A Dual-Arm Collaborative Framework for Dexterous Manipulation in Unstructured Environments with Contrastive Planning

Most object manipulation strategies for robots are based on the assumption that the object is rigid (i.e., with fixed geometry) and the goal's details have been fully specified (e.g., the exact target pose). However, there are many tasks that involve spatial relations in human environments where these conditions may be hard to satisfy, e.g., bending and placing a cable inside an unknown container. To develop advanced robotic manipulation capabilities in unstructured environments that avoid these assumptions, we propose a novel long-horizon framework that exploits contrastive planning in finding promising collaborative actions. Using simulation data collected by random actions, we learn an embedding model in a contrastive manner that encodes the spatio-temporal information from successful experiences, which facilitates the subgoal planning through clustering in the latent space. Based on the keypoint correspondence-based action parameterization, we design a leader-follower control scheme for the collaboration between dual arms. All models of our policy are automatically trained in simulation and can be directly transferred to real-world environments. To validate the proposed framework, we conduct a detailed experimental study on a complex scenario subject to environmental and reachability constraints in both simulation and real environments.
Magic angles in twisted bilayer graphene near commensuration: Towards a hypermagic regime

The Bistritzer-MacDonald continuum model (BM model) describes the low-energy moir\'e bands for twisted bilayer graphene (TBG) at small twist angles. We derive a generalized continuum model for TBG near any commensurate twist angle, which is characterized by complex interlayer hoppings at commensurate $AA$ stackings (rather than the real hoppings in the BM model), a real interlayer hopping at commensurate $AB/BA$ stackings, and a global energy shift. The complex phases of the $AA$ stacking hoppings and the twist angle together define a single angle parameter $\phi_0$. We compute the model parameters for the first six distinct commensurate TBG configurations, among which the $38.2^\circ$ configuration may be within experimentally observable energy scales. We identify the first magic angle for any $\phi_0$ at a condition similar to that of the BM model. At this angle, the lowest two moir\'e bands at charge neutrality become flat except near the $\boldsymbol\Gamma_M$ point and retain fragile topology but lose particle-hole symmetry. We further identify a hypermagic parameter regime centered at $\phi_0 = \pm\pi/2$ where many moir\'e bands around charge neutrality (often $8$ or more) become flat simultaneously. Many of these flat bands resemble those in the kagome lattice and $p_x$, $p_y$ 2-orbital honeycomb lattice tight-binding models.
InTEn-LOAM: Intensity and Temporal Enhanced LiDAR Odometry and Mapping

Traditional LiDAR odometry (LO) systems mainly leverage geometric information obtained from the traversed surroundings to register lazer scans and estimate LiDAR ego-motion, while they may be unreliable in dynamic or degraded environments. This paper proposes InTEn-LOAM, a low-drift and robust LiDAR odometry and mapping method that fully exploits implicit information of lazer sweeps (i.e., geometric, intensity and temporal characteristics). The specific content of this work includes method innovation and experimental verification. With respect to method innovation, we propose the cylindrical-image-based feature extraction scheme, which makes use of the characteristic of uniform spatial distribution of lazer points to boost the adaptive extraction of various types of features, i.e., ground, beam, facade and reflector. We propose a novel intensity-based point registration algorithm and incorporate it into the LiDAR odometry, enabling the LO system to jointly estimate the LiDAR ego-motion using both geometric and intensity feature points. To eliminate the interference of dynamic objects, we propose a temporal-based dynamic object removal approach to filter them out in the resulting points map. Moreover, the local map is organized and downsampled using a temporal-related voxel grid filter to maintain the similarity between the current scan and the static local map. With respect to experimental verification, extensive tests are conducted on both simulated and real-world datasets. The results show that the proposed method achieves similar or better accuracy with respect to the state-of-the-art in normal driving scenarios and outperforms geometric-based LO in unstructured environments.
The Dust Sublimation Region of the Type 1 AGN NGC 4151 at a Hundred Microarcsecond Scale as Resolved by the CHARA Array Interferometer

The nuclear region of Type 1 active galactic nuclei (AGNs) has only been partially resolved so far in the near-infrared (IR), where we expect to see the dust sublimation region and the nucleus directly without obscuration. Here, we present the near-IR interferometric observation of the brightest Type 1 AGN NGC 4151 at long baselines of ∼250 m using the CHARA Array, reaching structures at hundred microarcsecond scales. The squared visibilities decrease down to as low as ∼0.25, definitely showing that the structure is resolved. Furthermore, combining with the previous visibility measurements at shorter baselines but at different position angles, we show that the structure is elongated perpendicular to the polar axis of the nucleus, as defined by optical polarization and a linear radio jet. A thin-ring fit gives a minor/major axis ratio of ∼0.7 at a radius ∼0.5 mas (∼0.03 pc). This is consistent with the case where the sublimating dust grains are distributed preferentially in the equatorial plane in a ring-like geometry, viewed at an inclination angle of ∼40°. The recent mid-IR interferometric finding of polar-elongated geometry at a pc scale, together with a larger-scale polar outflow as spectrally resolved by the Hubble Space Telescope, would generally suggest a dusty, conical and hollow outflow being launched, presumably in the dust sublimation region. This might potentially lead to a polar-elongated morphology in the near-IR, as opposed to the results here. We discuss a possible scenario where an episodic, one-off anisotropic acceleration formed a polar-fast and equatorially slow velocity distribution, having led to an effectively flaring geometry as we observe.
Inference for the Binomial N Parameter: A Bayes Empirical Bayes Approach. Revision.

Abstract : The problem of inference about the binomial N parameter is considered. Applications arise in situations where an unknown population size is to be estimated. Previous work has focused on point estimation, but many applications require interval estimation, prediction, and decision-making. A Bayes empirical Bayes approach is presented. This provides a simple and flexible way of specifying prior information, and also allows a convenient representation of vague prior knowledge. It yields solutions to the problems interval estimation, prediction, and decision-making, as well as that of point estimation. The Bayes estimator compares favorably with the best, previously proposed, point estimators in the literature. The Bayesian estimation interval which corresponds to a vague prior distribution also performs satisfactorily when used as a frequentist confidence interval.
A virtual reality-based method for examining audiovisual prosody perception

Purpose: Prosody plays a vital role in verbal communication. Acoustic cues of prosody, such as intonation patterns, intensity, and duration have been extensively examined. However, prosodic characteristics are not only perceived auditorily, but also visually, as head and facial movements accompany prosody production. The purpose of this technical report is to present a method for examining audiovisual prosody based on virtual reality. Methods: While controlled manipulations of acoustic cues are a valuable method for uncovering and quantifying prosody perception, such an approach for visual prosody is much more complicated. Here, we describe a novel approach based on video-realistic animations of a virtual human. Such a method has the advantage that – in parallel to acoustic manipulations – head and facial movements can be parametrized. Results: We show that animations based on a virtual human provide motion cues similar to those obtained from video recordings of a real talker. Parametrization of the motion cues allows fine-grained manipulations of the movements. Conclusions: The use of virtual reality promises to open up new avenues for examining multimodal effects of verbal communication. Specifically, we discuss the possible application of this technique in the framework of examining prosody perception in listeners with cochlear implants.
AnICA: analyzing inconsistencies in microarchitectural code analyzers

Microarchitectural code analyzers, i.e., tools that estimate the throughput of machine code basic blocks, are important utensils in the tool belt of performance engineers. Recent tools like llvm-mca, uiCA, and Ithemal use a variety of techniques and different models for their throughput predictions. When put to the test, it is common to see these state-of-the-art tools give very different results. These inconsistencies are either errors, or they point to different and rarely documented assumptions made by the tool designers. In this paper, we present AnICA, a tool taking inspiration from differential testing and abstract interpretation to systematically analyze inconsistencies among these code analyzers. Our evaluation shows that AnICA can summarize thousands of inconsistencies in a few dozen descriptions that directly lead to high-level insights into the different behavior of the tools. In several case studies, we further demonstrate how AnICA automatically finds and characterizes known and unknown bugs in llvm-mca, as well as a quirk in AMD's Zen microarchitectures.
Response of the Einstein Telescope to Doppler anisotropies

We study the response function of the Einstein Telescope to kinematic Doppler anisotropies, which represent one of the guaranteed properties of the stochastic gravitational wave background. If the frequency dependence of the stochastic background changes slope within the detector frequency band, the Doppler anisotropic contribution to the signal can not be factorized in a part depending on frequency, and a part depending on direction. For the first time, we study the detector response function to Doppler anisotropies without making any factorizable Ansatz. Moreover, we do not assume that kinematic effects are small, and we derive general formulas valid for any relative velocity among frames. We apply our findings to three well-motivated examples of background profiles: power-law, broken power-law, and models with a resonance motivated by primordial black hole scenarios. We derive the signal-to-noise ratio associated with an optimal estimator for the detection of non-factorizable kinematic anisotropies, and we study it for representative examples.
Understanding Stay-at-home Attitudes through Framing Analysis of Tweets

With the onset of the COVID-19 pandemic, a number of public policy measures have been developed to curb the spread of the virus. However, little is known about the attitudes towards stay-at-home orders expressed on social media despite the fact that social media are central platforms for expressing and debating personal attitudes. To address this gap, we analyze the prevalence and framing of attitudes towards stay-at-home policies, as expressed on Twitter in the early months of the pandemic. We focus on three aspects of tweets: whether they contain an attitude towards stay-at-home measures, whether the attitude was for or against, and the moral justification for the attitude, if any. We collect and annotate a dataset of stay-at-home tweets and create classifiers that enable large-scale analysis of the relationship between moral frames and stay-at-home attitudes and their temporal evolution. Our findings suggest that frames of care are correlated with a supportive stance, whereas freedom and oppression signify an attitude against stay-at-home directives. There was widespread support for stay-at-home orders in the early weeks of lockdowns, followed by increased resistance toward the end of May and the beginning of June 2020. The resistance was associated with moral judgment that mapped to political divisions.
A CMOL-Like Memristor-CMOS Neuromorphic Chip-Core Demonstrating Stochastic Binary STDP

The advent of nanoscale memristors raised hopes of being able to build CMOL (CMOS/nanowire/molecular) type ultra-dense in-memory-computing circuit architectures. In CMOL, nanoscale memristors would be fabricated at the intersection of nanowires. The CMOL concept can be exploited in neuromorphic hardware by fabricating lower density neurons on CMOS and placing massive analog synaptic connectivity with nanowire and nanoscale-memristor fabric post-fabricated on top. However, technical problems have hindered such developments for presently available reliable commercial monolithic CMOS-memristor technologies. On one hand, each memristor needs a MOS selector transistor in series to guarantee forming and programming operations in large arrays. This results in compound MOS-memristor synapses (called 1T1R) which are no longer synapses at the crossing of nanowires. On the other hand, memristors do not yet constitute highly reliable, stable analog memories for massive analog-weight synapses with gradual learning. Here we demonstrate a pseudo-CMOL monolithic chip core that circumvents the two technical problems mentioned above by: (a) exploiting a CMOL-like geometrical chip layout technique to improve density despite the 1T1R limitation, and (b) exploiting a binary weight stochastic Spike-Timing-Dependent-Plasticity (STDP) learning rule that takes advantage of the more reliable binary memory capability of the memristors used. Experimental results are provided for a spiking neural network (SNN) CMOL-core with 64 input neurons, 64 output neurons and 4096 1T1R synapses, fabricated in 130nm CMOS with 200nm-sized Ti/HfOx/TiN memristors on top. The CMOL-core uses query-driven event read-out, which allows for memristor variability insensitive computations. Experimental system-level demonstrations are provided for plain template matching tasks, as well as regularized stochastic binary STDP feature-extraction learning, obtaining perfect recognition in hardware for a 4-letter recognition experiment.
Bounded Model Checking for Unbounded Client Server Systems

Bounded model checking (BMC) is an efficient formal verification technique which allows for desired properties of a software system to be checked on bounded runs of an abstract model of the system. The properties are frequently described in some temporal logic and the system is modeled as a state transition system. In this paper we propose a novel counting logic, $\mathcal{L}_{C}$, to describe the temporal properties of client-server systems with an unbounded number of clients. We also propose two dimensional bounded model checking ($2D$-BMC) strategy that uses two distinguishable parameters, one for execution steps and another for the number of tokens in the net representing a client-server system, and these two evolve separately, which is different from the standard BMC techniques in the Petri Nets formalism. This $2D$-BMC strategy is implemented in a tool called DCModelChecker which leverages the $2D$-BMC technique with a state-of-the-art satisfiability modulo theories (SMT) solver Z3. The system is given as a Petri Net and properties specified using $\mathcal{L}_{C}$ are encoded into formulas that are checked by the solver. Our tool can also work on industrial benchmarks from the Model Checking Contest (MCC). We report on these experiments to illustrate the applicability of the $2D$-BMC strategy.
A deep Aurum reservoir: Stable compounds of two bulk-immiscible metals under pressure

The Earth’s crust is known to be depleted of gold, among other slightly heavy noble metals transported by magma from the Earth’s mantle to the crust. The bulk silicate Earth (BSE) model also suggests signiﬁcant depletion of Au in the silicate mantle itself, which cannot be explained by the amount of Au in the mantle’s magma. This implies that Au could remain in the lower mantle and form stable compounds, especially with iron, which is the predominant element within the core. While Fe does not form binary compounds or a bulk alloy with Au under ambient conditions, it may do so at the elevated pressures found in the Earth’s interior. Here, using density-functional methods, we investigated the possibility of identifying stable, binary Fe-Au compounds at pressures up to 210 GPa. We found three such Fe-Au compounds, which are stabilized by pressure and notable electron transfer, including an orthorhombic AuFe 4 phase that is ferromagnetic in nature with Au possessing a signiﬁcant magnetic moment. While our results suggest that thermal convection due to the conductivities and the heat ﬂux from the Fe-Au compounds could be an energy source to power the Earth’s geodynamo, they also point towards changes in Au’s chemical properties, as it can exist as either an anion or cation under pressure. In addition, the sound velocity and the density predicted for the various Fe-Au compounds suggest that they could shed light on the composition of the core-mantle boundary and the Earth’s core, while demonstrating how the presence of trace amounts of Au could inﬂuence agreement with seismic data.
Computational Sarcasm Analysis on Social Media: A Systematic Review

Sarcasm can be defined as saying or writing the opposite of what one truly wants to express, usually to insult, irritate, or amuse someone. Because of the obscure nature of sarcasm in textual data, detecting it is difficult and of great interest to the sentiment analysis research community. Though the research in sarcasm detection spans more than a decade, some significant advancements have been made recently, including employing unsupervised pre-trained transformers in multimodal environments and integrating context to identify sarcasm. In this study, we aim to provide a brief overview of recent advancements and trends in computational sarcasm research for the English language. We describe relevant datasets, methodologies, trends, issues, challenges, and tasks relating to sarcasm that are beyond detection. Our study provides well-summarized tables of sarcasm datasets, sarcastic features and their extraction methods, and performance analysis of various approaches which can help researchers in related domains understand current state-of-the-art practices in sarcasm detection.