Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Effect of Multi-step Ascent in Sharpness-Aware Minimization

Recently, Sharpness-Aware Minimization (SAM) has shown state-of-the-art performance by seeking flat minima. To minimize the maximum loss within a neighborhood in the parameter space, SAM uses an ascent step, which perturbs the weights along the direction of gradient ascent with a given radius. While single-step or multi-step can be taken during ascent steps, previous studies have shown that multi-step ascent SAM rarely improves generalization performance. However, this phenomenon is particularly interesting because the multi-step ascent is expected to provide a better approximation of the maximum neighborhood loss. Therefore, in this paper, we analyze the effect of the number of ascent steps and investigate the difference between both single-step ascent SAM and multi-step ascent SAM. We identify the effect of the number of ascent on SAM optimization and reveal that single-step ascent SAM and multi-step ascent SAM exhibit distinct loss landscapes. Based on these observations, we finally suggest a simple modification that can mitigate the inefficiency of multi-step ascent SAM.
Slowly Rotating Black Holes in Einsteinian Quartic Gravities

We study slowly rotating black hole solutions in the six independent theories of Einstein Quartic Gravity (EQG) in four dimensions. Unlike in the static case for which all six theories yield the same solution, for rotating black holes we obtain distinct results for five out of the six theories. Working to leading order in the rotation parameter, we find that the equations characterizing these black holes can be reduced to second order for each theory, similar to what has already been done for Einstein Cubic Gravity. We construct approximate and numerical solutions to these equations, and study how physical properties of the solutions such as the angular velocity, photon sphere, black hole shadow, and innermost stable circular orbit are modified, working to leading order in the coupling constant.
The Influence of the Number of Different Stocks on the Levy–Levy–Solomon Model

The stock market model of Levy, Levy, Solomon is simulated for more than one stock to analyze the behavior for a large number of investors. Small markets can lead to realistic looking prices for one and more stocks. A large number of investors leads to a semi-regular fashion simulating one stock. For many stocks, three of the stocks are semi-regular and dominant, the rest is chaotic. Aside from that we changed the utility function and checked the results.
Shear-induced migration of a suspension under quasi-planar conﬁnement

The shear-induced migration of a non-Brownian suspension of spherical particles in a Newtonian ﬂuid is studied experimentally. The suspension is conﬁned in a thin rectangular channel that prevents complete particle overlap and subjected to an oscillatory pressure-driven ﬂow. Despite the signiﬁcant conﬁnement, the suspension exhibits many of the characteristic features of shear-induced migration in bulk suspensions. Furthermore, as a consequence of the channel geometry and the development and application of a single-camera particle tracking method, three-dimensional particle trajectories are obtained that allow us to directly associate particle contacts with the observed migration.
Weakly Supervised Temporal Convolutional Networks for Fine-Grained Surgical Activity Recognition

Automatic recognition of fine-grained surgical activities, called steps, is a challenging but crucial task for intelligent intra-operative computer assistance. The development of current vision-based activity recognition methods relies heavily on a high volume of manually annotated data. This data is difficult and time-consuming to generate and requires domain-specific knowledge. In this work, we propose to use coarser and easier-to-annotate activity labels, namely phases, as weak supervision to learn step recognition with fewer step annotated videos. We introduce a step-phase dependency loss to exploit the weak supervision signal. We then employ a Single-Stage Temporal Convolutional Network (SS-TCN) with a ResNet-50 backbone, trained in an end-to-end fashion from weakly annotated videos, for temporal activity segmentation and recognition. We extensively evaluate and show the effectiveness of the proposed method on a large video dataset consisting of 40 laparoscopic gastric bypass procedures and the public benchmark CATARACTS containing 50 cataract surgeries.
HST Imaging of Star-forming Clumps in Six GASP Ram-pressure-stripped Galaxies

Exploiting broad- and narrowband images of the Hubble Space Telescope from the near-UV to I-band rest frame, we study the star-forming clumps of six galaxies of the GASP sample undergoing strong ram pressure stripping. Clumps are detected in Hα and near-UV, tracing star formation on different timescales. We consider clumps located in galaxy disks and stripped tails and formed in stripped gas but still close to the disk, called extraplanar. We detect 2406 Hα-selected clumps (1708 in disks, 375 in extraplanar regions, and 323 in tails) and 3745 UV-selected clumps (2021 disk, 825 extraplanar, and 899 tail clumps). Only ∼15% of star-forming clumps are spatially resolved, meaning that most are smaller than ∼140 pc. We study the luminosity and size distribution functions (LDFs and SDFs, respectively) and the luminosity–size relation. The average LDF slope is 1.79 ± 0.09, while the average SDF slope is 3.1 ± 0.5. The results suggest that the star formation is turbulence-driven and scale-free, as in main-sequence galaxies. All of the clumps, whether they are in the disks or tails, have an enhanced Hα luminosity at a given size, compared to the clumps in main-sequence galaxies. Indeed, their Hα luminosity is closer to that of clumps in starburst galaxies, indicating that ram pressure is able to enhance the luminosity. No striking differences are found among disk and tail clumps, suggesting that the different environments in which they are embedded play a minor role in influencing the star formation.
Ba-enhanced Dwarf and Subgiant Stars in the LAMOST Galactic Surveys

Ba-enhanced stars are interesting probes of stellar astrophysics and Galactic formation history. In this work, we investigate the chemistry and kinematics of a large sample of Ba-enhanced ([Ba/Fe]>1.0) dwarf and subgiant stars with 5000 < T eff < 6700 K from LAMOST. We find that both stellar internal evolution processes and external mass exchange due to binary evolution are responsible for the origins of the Ba enhancement of our sample stars. About one third of them exhibit C and N enhancement and an ultraviolet brightness excess, indicating they are products of binary evolution. The remaining Ba-enhanced stars with normal C and N abundances are mostly warm stars with T eff > 6000 K. They are likely the consequences of stellar internal element transport processes, but they show very different element patterns to hotter Am/Fm stars. Our results reveal a substantially lack of high-[α/Fe], Ba-enhanced stars in the [Fe/H]–[α/Fe] plane, which we dub as a “high-[α/Fe] desert”. We suggest it is due to a lower efficiency for producing Ba-enhanced stars by low-mass asymptotic giant branch progenitors in binary systems. Our results call for detailed modeling of these Ba-enhanced stellar peculiarities, in the context of both stellar internal element transport and external mass accretion.
Managing multi-facet bias in collaborative filtering recommender systems

Due to the extensive growth of information available online, recommender systems play a more significant role in serving people's interests. Traditional recommender systems mostly use an accuracy-focused approach to produce recommendations. Today's research suggests that this single-dimension approach can lead the system to be biased against a series of items with certain attributes. Biased recommendations across groups of items can endanger the interests of item providers along with causing user dissatisfaction with the system. This study aims to manage a new type of intersectional bias regarding the geographical origin and popularity of items in the output of state-of-the-art collaborative filtering recommender algorithms. We introduce an algorithm called MFAIR, a multi-facet post-processing bias mitigation algorithm to alleviate these biases. Extensive experiments on two real-world datasets of movies and books, enriched with the items' continents of production, show that the proposed algorithm strikes a reasonable balance between accuracy and both types of the mentioned biases. According to the results, our proposed approach outperforms a well-known competitor with no or only a slight loss of efficiency.
The optical behaviour of BL Lacertae at its maximum brightness levels: a blend of geometry and energetics

In 2021 BL Lacertae underwent an extraordinary activity phase, which was intensively followed by the Whole Earth Blazar Telescope (WEBT) Collaboration. We present the WEBT optical data in the BVRI bands acquired at 36 observatories around the world. In mid-2021 the source showed its historical maximum, with R = 11.14. The light curves display many episodes of intraday variability, whose amplitude increases with source brightness, in agreement with a geometrical interpretation of the long-term flux behaviour. This is also supported by the long-term spectral variability, with an almost achromatic trend with brightness. In contrast, short-term variations are found to be strongly chromatic and are ascribed to energetic processes in the jet. We also analyse the optical polarimetric behaviour, finding evidence of a strong correlation between the intrinsic fast variations in flux density and those in polarization degree, with a time delay of about 13 h. This suggests a common physical origin. The overall behaviour of the source can be interpreted as the result of two mechanisms: variability on time-scales greater than several days is likely produced by orientation effects, while either shock waves propagating in the jet, or magnetic reconnection, possibly induced by kink instabilities in the jet, can explain variability on shorter time-scales. The latter scenario could also account for the appearance of quasi-periodic oscillations, with periods from a few days to a few hours, during outbursts, when the jet is more closely aligned with our line of sight and the time-scales are shortened by relativistic effects.
Neural Algorithmic Reasoning with Causal Regularisation

Recent work on neural algorithmic reasoning has investigated the reasoning capabilities of neural networks, effectively demonstrating they can learn to execute classical algorithms on unseen data coming from the train distribution. However, the performance of existing neural reasoners significantly degrades on out-of-distribution (OOD) test data, where inputs have larger sizes. In this work, we make an important observation: there are many different inputs for which an algorithm will perform certain intermediate computations identically. This insight allows us to develop data augmentation procedures that, given an algorithm's intermediate trajectory, produce inputs for which the target algorithm would have exactly the same next trajectory step. We ensure invariance in the next-step prediction across such inputs, by employing a self-supervised objective derived by our observation, formalised in a causal graph. We prove that the resulting method, which we call Hint-ReLIC, improves the OOD generalisation capabilities of the reasoner. We evaluate our method on the CLRS algorithmic reasoning benchmark, where we show up to 3$\times$ improvements on the OOD test data.
A Unifying Perspective on Multi-Calibration: Unleashing Game Dynamics for Multi-Objective Learning

We provide a unifying framework for the design and analysis of multi-calibrated and moment-multi-calibrated predictors. Placing the multi-calibration problem in the general setting of multi-objective learning —where learning guarantees must hold simultaneously over a set of distributions and loss functions—we exploit connections to game dynamics to obtain state-of-the-art guarantees for a diverse set of multi-calibration learning problems. In addition to shedding light on existing multi-calibration guarantees, and greatly simplifying their analysis, our approach yields a 1 /ε 2 improvement in the number of oracle calls compared to the state-of-the-art algorithm of Jung et al. [19] for learning deterministic moment-calibrated predictors and an exponential improvement in k compared to the state-of-the-art algorithm of Gopalan et al. [14] for learning a k -class multi-calibrated predictor. Beyond multi-calibration, we use these game dynamics to address existing and emerging considerations in the study of group fairness and multi-distribution learning.
Electrical and Polarimetric Radar Observations of a Multicell Storm in TELEX

Abstract On 28–29 June 2004 a multicellular thunderstorm west of Oklahoma City, Oklahoma, was probed as part of the Thunderstorm Electrification and Lightning Experiment field program. This study makes use of radar observations from the Norman, Oklahoma, polarimetric Weather Surveillance Radar-1988 Doppler, three-dimensional lightning mapping data from the Oklahoma Lightning Mapping Array (LMA), and balloon-borne vector electric field meter (EFM) measurements. The storm had a low flash rate (30 flashes in 40 min). Four charge regions were inferred from a combination of LMA and EFM data. Lower positive charge near 4 km and midlevel negative charge from 4.5 to 6 km MSL (from 0° to −6.5°C) were generated in and adjacent to a vigorous updraft pulse. Further midlevel negative charge from 4.5 to 6 km MSL and upper positive charge from 6 to 8 km (from −6.5° to −19°C) were generated later in quantity sufficient to initiate lightning as the updraft decayed. A negative screening layer was present near the storm top.
HierCat: Hierarchical Query Categorization from Weakly Supervised Data at Facebook Marketplace

Query categorization at customer-to-customer e-commerce platforms like Facebook Marketplace is challenging due to the vagueness of search intent, noise in real-world data, and imbalanced training data across languages. Its deployment also needs to consider challenges in scalability and downstream integration in order to translate modeling advances into better search result relevance. In this paper we present HierCat, the query categorization system at Facebook Marketplace. HierCat addresses these challenges by leveraging multi-task pre-training of dual-encoder architectures with a hierarchical inference step to effectively learn from weakly supervised training data mined from searcher engagement. We show that HierCat not only outperforms popular methods in offline experiments, but also leads to 1.4% improvement in NDCG and 4.3% increase in searcher engagement at Facebook Marketplace Search in two weeks of online A/B testing.
Use of immersive virtual reality-based experiments to study tactical decision-making during emergency evacuation

Humans make their evacuation decisions first at strategic/tactical levels, deciding their exit and route choice and then at operational level, navigating to a way-point, avoiding collisions. What influences an individuals at tactical level is of importance, for modelers to design a high fidelity simulation or for safety engineers to create efficient designs/codes. Does an unlit exit sign dissuades individual(s) to avoid a particular exit/route and vice versa? What effect does the crowd's choices have on individual's decision making? To answer these questions, we studied the effect of exit signage (unlit/lit), different proportions of crowd movement towards the exits, and the combined (reinforcing/conflicting) effect of the sign and the crowd treatment on reaction times and exit choices of participants in an immersive virtual reality(VR) evacuation experiment. We found that there is tolerance for queuing when different sources of information, exit signage and crowd movement reinforced one another. The effect of unlit exit signage on dissuading individuals from using a particular exit/route was significant. The virtual crowd was ineffective at encouraging utilization of a particular exit/route but had a slight repulsive effect. Additionally, we found some similarities between previous studies based on screen-based evacuation experiments and our VR-based experiment.
LSFD for Rician-Faded Cell-Free mMIMO Systems with Channel Aging and Hardware Impairments

We study the impact of channel aging on the uplink of a cell-free massive multiple-input multiple-output system with hardware impairments. We consider a dynamic analog-to-digital converter architecture at the access points (APs), and low-resolution digital-to-analog converters at the user equipments (UEs). We derive a closed-form spectral efficiency expression by considering i) practical spatially-correlated Rician channels; ii) hardware impairments at the APs and the UEs; iii) channel aging; and iv) large-scale fading decoding (LSFD). We show that LSFD can effectively mitigate the detrimental effects of i) channel aging for both low and high UE velocities; and ii) inter-user interference for low-velocity UEs but not for high-velocity UEs.
Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation

Skip connections and normalisation layers form two standard architectural components that are ubiquitous for the training of Deep Neural Networks (DNNs), but whose precise roles are poorly understood. Recent approaches such as Deep Kernel Shaping have made progress towards reducing our reliance on them, using insights from wide NN kernel theory to improve signal propagation in vanilla DNNs (which we define as networks without skips or normalisation). However, these approaches are incompatible with the self-attention layers present in transformers, whose kernels are intrinsically more complicated to analyse and control. And so the question remains: is it possible to train deep vanilla transformers? We answer this question in the affirmative by designing several approaches that use combinations of parameter initialisations, bias matrices and location-dependent rescaling to achieve faithful signal propagation in vanilla transformers. Our methods address various intricacies specific to signal propagation in transformers, including the interaction with positional encoding and causal masking. In experiments on WikiText-103 and C4, our approaches enable deep transformers without normalisation to train at speeds matching their standard counterparts, and deep vanilla transformers to reach the same performance as standard ones after about 5 times more iterations.
Hadamard Layer to Improve Semantic Segmentation

The Hadamard Layer, a simple and computationally efficient way to improve results in semantic segmentation tasks, is presented. This layer has no free parameters that require to be trained. Therefore it does not increase the number of model parameters, and the extra computational cost is marginal. Experimental results show that the new Hadamard layer substantially improves the performance of the investigated models (variants of the Pix2Pix model). The performance’s improvement can be explained by the Hadamard layer forcing the network to produce an internal encoding of the classes so that all bins are active. Therefore, the network computation is more distributed. In a sort that the Hadamard layer requires that to change the predicted class, it is necessary to modify 2k−1 bins, assuming k bins in the encoding. A specific loss function allows a stable and fast training convergence.
Minimal modelling approach to describe turbulent rocket roll dynamics in a vertical wind tunnel

The roll dynamics of a 5-kg, 1.3 -m high sounding rocket are analysed in a vertical wind tunnel. Significant turbulence in the tunnel makes the system identification of the effective inertia, damping, and asymmetry with respect to roll challenging. A novel method is developed, which decouples the disturbance from the rocket frame's intrinsic roll dynamics and allows accurate prediction of roll rate and angle. The parameter identification method is integral-based, and treats wind disturbances as equivalent to a movement in the actuator fins. The method is robust, requires minimal computation, and gave realistic disturbance distributions reflecting the randomness of the turbulent wind flow. Two models, one with constant damping and one with fin angle-dependant damping were considered. The mean absolute roll rate of the rocket frame observed in experiments was 16.4°/s and both models predicted the roll rate with a mean absolute error less than 0.10°/s with a standard deviation less than 0.08°/s. The roll angle (measured by an encoder), was tracked by the model with a mean absolute error less than 0.20° and a standard deviation less than 0.15°. These results prove the concept of this minimal modelling approach which will be extended to varying wind speed, and pitch, and yaw dynamics in the future.
Coplanar Circumbinary Planets Can Be Unstable to Large Tilt Oscillations in the Presence of an Inner Polar Planet

Mutually misaligned circumbinary planets may form in a warped or broken gas disk or from later planet–planet interactions. With numerical simulations and analytic estimates we explore the dynamics of two circumbinary planets with a large mutual inclination. A coplanar inner planet causes prograde apsidal precession of the binary and the stationary inclination for the outer planet is higher for larger outer planet orbital radius. In this case a coplanar outer planet always remains coplanar. On the other hand, a polar inner planet causes retrograde apsidal precession of the binary orbit and the stationary inclination is smaller for larger outer planet orbital radius. For a range of outer planet semimajor axes, an initially coplanar orbit is librating meaning that the outer planet undergoes large tilt oscillations. Circumbinary planets that are highly inclined to the binary are difficult to detect—it is unlikely for a planet to have an inclination below the transit detection limit in the presence of a polar inner planet. These results suggest that there could be a population of circumbinary planets that are undergoing large tilt oscillations.
The excedance quotient of the Bruhat order, Quasisymmetric Varieties and Temperley-Lieb algebras

Let $R_n=\mathbb{Q}[x_1,x_2,\ldots,x_n]$ be the ring of polynomial in $n$ variables and consider the ideal $\langle \mathrm{QSym}_{n}^{+}\rangle\subseteq R_n$ generated by quasisymmetric polynomials without constant term. It was shown by J.~C. ~Aval, F. ~Bergeron and N. ~Bergeron that $\dim\big(R_n\big/\langle \mathrm{QSym}_{n}^{+} \rangle\big)=C_n$ the $n$th Catalan number. In the present work, we explain this phenomenon by defining a set of permutations $\mathrm{QSV}_{n}$ with the following properties: first, $\mathrm{QSV}_{n}$ is a basis of the Temperley--Lieb algebra $\mathsf{TL}_{n}(2)$, and second, when considering $\mathrm{QSV}_{n}$ as a collection of points in $\mathbb{Q}^{n}$, the top-degree homogeneous component of the vanishing ideal $\mathbf{I}(\mathrm{QSV}_{n})$ is $\langle \mathrm{QSym}_{n}^{+}\rangle$. Our construction has a few byproducts which are independently noteworthy. We define an equivalence relation $\sim$ on the symmetric group $S_{n}$ using weak excedances and show that its equivalence classes are naturally indexed by noncrossing partitions. Each equivalence class is an interval in the Bruhat order between an element of $\mathrm{QSV}_{n}$ and a $321$-avoiding permutation. Furthermore, the Bruhat order induces a well-defined order on $S_{n}\big/\! \! \sim$. Finally, we show that any section of the quotient $S_{n}\big/\! \! \sim$ gives an (often novel) basis for $\mathsf{TL}_{n}(2)$.