Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Approach with Dynamic Multi-Swarm of UAVs for Forest Firefighting

In this paper, the Multi-Swarm Cooperative Information-driven search and Divide and Conquer mitigation control (MSCIDC) approach is proposed for faster detection and mitigation of forest fire by reducing the loss of biodiversity, nutrients, soil moisture, and other intangible benefits. A swarm is a cooperative group of Unmanned Aerial Vehicles (UAVs) that fly together to search and quench the fire effectively. The multi-swarm cooperative information-driven search uses a multi-level search comprising cooperative information-driven exploration and exploitation for quick/accurate detection of fire location. The search level is selected based on the thermal sensor information about the potential fire area. The dynamicity of swarms, aided by global regulative repulsion and merging between swarms, reduces the detection and mitigation time compared to the existing methods. The local attraction among the members of the swarm helps the non-detector members to reach the fire location faster, and divide-and-conquer mitigation control ensures a non-overlapping fire sector allocation for all members quenching the fire. The performance of MSCIDC has been compared with different multi-UAV methods using a simulated environment of pine forest. The performance clearly shows that MSCIDC mitigates fire much faster than the multi-UAV methods. The Monte-Carlo simulation results indicate that the proposed method reduces the average forest area burnt by $65\%$ and mission time by $60\%$ compared to the best result case of the multi-UAV approaches, guaranteeing a faster and successful mission.
Robust Few-shot Learning Without Using any Adversarial Samples

The high cost of acquiring and annotating samples has made the "few-shot" learning problem of prime importance. Existing works mainly focus on improving performance on clean data and overlook robustness concerns on the data perturbed with adversarial noise. Recently, a few efforts have been made to combine the few-shot problem with the robustness objective using sophisticated meta-learning techniques. These methods rely on the generation of adversarial samples in every episode of training, which further adds to the computational burden. To avoid such time-consuming and complicated procedures, we propose a simple but effective alternative that does not require any adversarial samples. Inspired by the cognitive decision-making process in humans, we enforce high-level feature matching between the base class data and their corresponding low-frequency samples in the pretraining stage via self distillation. The model is then fine-tuned on the samples of novel classes where we additionally improve the discriminability of low-frequency query set features via cosine similarity. On a one-shot setting of the CIFAR-FS dataset, our method yields a massive improvement of 60.55% and 62.05% in adversarial accuracy on the projected gradient descent (PGD) and state-of-the-art auto attack, respectively, with a minor drop in clean accuracy compared to the baseline. Moreover, our method only takes 1.69× of the standard training time while being ≈ 5× faster than thestate-of-the-art adversarial meta-learning methods. The code is available at https://github.com/vcl-iisc/robust-few-shot-learning.
Reciprocity, Homophily, and Social Network Effects in Pictorial Communication: A Case Study of Bitmoji Stickers

Pictorial emojis and stickers are commonly used in online social communications. We analyzed social communications using Bitmoji stickers, which are expressive pictorial stickers made from avatars resembling actual users. We collect a large-scale dataset of 3 billion Bitmoji stickers’ metadata, shared among 300 million Snapchat users. We find that individual Bitmoji sticker usage patterns can be characterized jointly on dimensions of reciprocity and selectivity. Generally speaking, users are either both reciprocal and selective about whom they use Bitmoji stickers with or neither reciprocal nor selective. We additionally demonstrate network homophily by showing that friends use Bitmoji stickers at similar rates. Finally, using a quasi-experimental approach, we show that receiving Bitmoji stickers from a friend encourages future Bitmoji sticker usage and overall Snapchat engagement. Our work carries implications for a better understanding of online pictorial communication behaviors.
Dual Generator Offline Reinforcement Learning

In offline RL, constraining the learned policy to remain close to the data is essential to prevent the policy from outputting out-of-distribution (OOD) actions with erroneously overestimated values. In principle, generative adversarial networks (GAN) can provide an elegant solution to do so, with the discriminator directly providing a probability that quantifies distributional shift. However, in practice, GAN-based offline RL methods have not performed as well as alternative approaches, perhaps because the generator is trained to both fool the discriminator and maximize return -- two objectives that can be at odds with each other. In this paper, we show that the issue of conflicting objectives can be resolved by training two generators: one that maximizes return, with the other capturing the ``remainder'' of the data distribution in the offline dataset, such that the mixture of the two is close to the behavior policy. We show that not only does having two generators enable an effective GAN-based offline RL method, but also approximates a support constraint, where the policy does not need to match the entire data distribution, but only the slice of the data that leads to high long term performance. We name our method DASCO, for Dual-Generator Adversarial Support Constrained Offline RL. On benchmark tasks that require learning from sub-optimal data, DASCO significantly outperforms prior methods that enforce distribution constraint.
Benefits of Monotonicity in Safe Exploration with Gaussian Processes

We consider the problem of sequentially maximising an unknown function over a set of actions while ensuring that every sampled point has a function value below a given safety threshold. We model the function using kernel-based and Gaussian process methods, while differing from previous works in our assumption that the function is monotonically increasing with respect to a \emph{safety variable}. This assumption is motivated by various practical applications such as adaptive clinical trial design and robotics. Taking inspiration from the \textsc{\sffamily GP-UCB} and \textsc{\sffamily SafeOpt} algorithms, we propose an algorithm, monotone safe {\sffamily UCB} (\textsc{\sffamily M-SafeUCB}) for this task. We show that \textsc{\sffamily M-SafeUCB} enjoys theoretical guarantees in terms of safety, a suitably-defined regret notion, and approximately finding the entire safe boundary. In addition, we illustrate that the monotonicity assumption yields significant benefits in terms of the guarantees obtained, as well as algorithmic simplicity and efficiency. We support our theoretical findings by performing empirical evaluations on a variety of functions, including a simulated clinical trial experiment.
Adversarial Data Augmentation Using VAE-GAN for Disordered Speech Recognition

Automatic recognition of disordered speech remains a highly challenging task to date. The underlying neuro-motor conditions, often compounded with co-occurring physical disabilities, lead to the difficulty in collecting large quantities of impaired speech required for ASR system development. This paper presents novel variational auto-encoder generative adversarial network (VAE-GAN) based personalized disordered speech augmentation approaches that simultaneously learn to encode, generate and discriminate synthesized impaired speech. Separate latent features are derived to learn dysarthric speech characteristics and phoneme context representations. Self-supervised pre-trained Wav2vec 2.0 embedding features are also incorporated. Experiments conducted on the UASpeech corpus suggest the proposed adversarial data augmentation approach consistently outperformed the baseline speed perturbation and non-VAE GAN augmentation methods with trained hybrid TDNN and End-to-end Conformer systems. After LHUC speaker adaptation, the best system using VAE-GAN based augmentation produced an overall WER of 27.78% on the UASpeech test set of 16 dysarthric speakers, and the lowest published WER of 57.31% on the subset of speakers with "Very Low" intelligibility.
Characterizing Virtual Reality Software Testing

—Virtual Reality (VR) is an emerging technique that provides a unique real-time experience for users. VR technologies have provided revolutionary user experiences in various scenarios (e.g., training, education, product/architecture design, gaming, remote conference/tour, etc.). However, testing VR applications is challenging due to their nature which necessitates physical interactivity, and their reliance on hardware systems. Despite the recent advancements in VR technology and its usage scenarios, we still know little about VR application testing. To ﬁll up this knowledge gap, we performed an empirical study on 97 open-source VR applications including 28 industrial projects. Our analysis identiﬁed that 74.2% of the VR projects evaluated did not have any tests, and for the VR projects that did, the median functional-method to test-method ratio was low in comparison to other project categories. Moreover, we uncovered tool support issues concerning the measurement of VR code coverage, and the code coverage and assertion density results we were able to generate were also relatively low, as they respectively had averages of 15.63% and 17.69%. Finally, through manual analysis of 220 test cases from four VR applications and 281 test cases from four non-VR applications, we identiﬁed that VR applications require speciﬁc categories of test cases to ensure VR application quality attributes. We believe that our ﬁndings constitute a call to action for the VR development community to improve testing aspects and provide directions for software engineering researchers to develop advanced techniques for automatic test case generation and test quality analysis for VR applications.
Oracle Inequalities for Model Selection in Offline Reinforcement Learning

In offline reinforcement learning (RL), a learner leverages prior logged data to learn a good policy without interacting with the environment. A major challenge in applying such methods in practice is the lack of both theoretically principled and practical tools for model selection and evaluation. To address this, we study the problem of model selection in offline RL with value function approximation. The learner is given a nested sequence of model classes to minimize squared Bellman error and must select among these to achieve a balance between approximation and estimation error of the classes. We propose the first model selection algorithm for offline RL that achieves minimax rate-optimal oracle inequalities up to logarithmic factors. The algorithm, ModBE, takes as input a collection of candidate model classes and a generic base offline RL algorithm. By successively eliminating model classes using a novel one-sided generalization test, ModBE returns a policy with regret scaling with the complexity of the minimally complete model class. In addition to its theoretical guarantees, it is conceptually simple and computationally efficient, amounting to solving a series of square loss regression problems and then comparing relative square loss between classes. We conclude with several numerical simulations showing it is capable of reliably selecting a good model class.
Spatiotemporal Calibration of 3-D Millimetre-Wavelength Radar-Camera Pairs

Autonomous vehicles (AVs) fuse data from multiple sensors and sensing modalities to impart a measure of robustness when operating in adverse conditions. Radars and cameras are popular choices for use in sensor fusion; although radar measurements are sparse in comparison to camera images, radar scans penetrate fog, rain, and snow. However, accurate sensor fusion depends upon knowledge of the spatial transform between the sensors and any temporal misalignment that exists in their measurement times. During the life cycle of an AV, these calibration parameters may change, so the ability to perform in-situ spatiotemporal calibration is essential to ensure reliable long-term operation. State-of-the-art 3D radar-camera spatiotemporal calibration algorithms require bespoke calibration targets that are not readily available in the field. In this paper, we describe an algorithm for targetless spatiotemporal calibration that does not require specialized infrastructure. Our approach leverages the ability of the radar unit to measure its own ego-velocity relative to a fixed, external reference frame. We analyze the identifiability of the spatiotemporal calibration problem and determine the motions necessary for calibration. Through a series of simulation studies, we characterize the sensitivity of our algorithm to measurement noise. Finally, we demonstrate accurate calibration for three real-world systems, including a handheld sensor rig and a vehicle-mounted sensor array. Our results show that we are able to match the performance of an existing, target-based method, while calibrating in arbitrary, infrastructure-free environments.
$X(3872)$, $X(4014)$, and their bottom partners at finite temperature

The properties of the $X(3872)$ and its spin partner, the $X(4014)$, are studied both in vacuum and at finite temperature. Using an effective hadron theory based on the hidden-gauge Lagrangian, the $X(3872)$ is dynamically generated from the $s$-wave rescattering of a pair of pseudoscalar and vector charm mesons. By incorporating the thermal spectral functions of open charm mesons, the calculation is extended to finite temperature. Similarly, the properties of the $X(4014)$ are obtained out of the scattering of charm vector mesons. By applying heavy-quark flavor symmetry, the properties of their bottom counterparts in the axial-vector and tensor channels are also predicted. All the dynamically generated states show a decreasing mass and acquire an increasing decay width with temperature, following the trend observed in their meson constituents. These results are relevant in relativistic heavy-ion collisions at high energies, in analyses of the collective medium formed after hadronization or in femtoscopic studies, and can be tested in lattice-QCD calculations exploring the melting of heavy mesons at finite temperature.
FedGen: Generalizable Federated Learning

Existing federated learning models that follow the standard risk minimization paradigm of machine learning often fail to generalize in the presence of spurious correlations in the training data. In many real-world distributed settings, spurious correlations exist due to biases and data sampling issues on distributed devices or clients that can erroneously influence models. Current generalization approaches are designed for centralized training and attempt to identify features that have an invariant causal relationship with the target, thereby reducing the effect of spurious features. However, such invariant risk minimization approaches rely on apriori knowledge of training data distributions which is hard to obtain in many applications. In this work, we present a generalizable federated learning framework called FedGen, which allows clients to identify and distinguish between spurious and invariant features in a collaborative manner without prior knowledge of training distributions. We evaluate our approach on real-world datasets from different domains and show that FedGen results in models that achieve significantly better generalization than current federated learning approaches.
MAST: Multiscale Audio Spectrogram Transformers

We present Multiscale Audio Spectrogram Transformer (MAST) for audio classification, which brings the concept of multiscale feature hierarchies to the Audio Spectrogram Transformer (AST) [1]. Given an input audio spectrogram, we first patchify and project it into an initial temporal resolution and embedding dimension, post which the multiple stages in MAST progressively expand the embedding dimension while reducing the temporal resolution of the input. We use a pyramid structure that allows early layers of MAST operating at a high temporal resolution but low embedding space to model simple low-level acoustic information and deeper temporally coarse layers to model high-level acoustic information with high-dimensional embeddings. We also extend our approach to present a new Self-Supervised Learning (SSL) method called SS-MAST, which calculates a symmetric contrastive loss between latent representations from a student and a teacher encoder, leveraging patch-drop, a novel audio augmentation approach that we introduce. In practice, MAST significantly outperforms AST by an average accuracy of 3.4% across 8 speech and non-speech tasks from the LAPE Benchmark [2], achieving state-of-the-art results on keyword spotting in Speech Commands. Additionally, our proposed SS-MAST achieves an absolute average improvement of 2.6% over the previously proposed SSAST [3]1.
SQUID: Faster Analytics via Sampled Quantiles Data-structure

Measurement is a fundamental enabler of network applications such as load balancing, attack detection and mitigation, and traffic engineering. A key building block in many critical measurement tasks is 𝑞 -MAX , where we wish to find the largest 𝑞 values in a number stream. A standard approach of maintaining a heap of the largest 𝑞 values ordered results in logarithmic runtime, which is too slow for large measurements. Modern approaches attain a constant runtime by removing small items in bulk and retaining the largest 𝑞 items at all times. Yet, these approaches are bottlenecked by an expensive quantile calculation method. We propose SQUID, a method that redesigns 𝑞 -MAX to allow the use of approximate quantiles , which we can compute efficiently, thereby accelerating the solution and, subsequently, many measurement tasks. We demonstrate the benefit of our approach by designing a novel weighted heavy hitters data structure that is faster and more accurate than the existing alternatives. Here, we combine our previous techniques with a lazy deletion of small entries, which expiates the maintenance process and increases the accuracy. We also demonstrate the applicability of our algorithmic approach in a general algorithmic scope by implementing the LRFU cache policy with a constant update time. Furthermore, we also show the practicality of SQUID for improving real-world networked systems, by implementing a P4 prototype of SQUID for in-network caching and demonstrating how SQUID enables a wide spectrum of score-based caching policy directly on a P4 switch. Our work includes an extensive evaluation that uses real packet and caching traces. We demonstrate speedups of up to 6.6x for the 𝑞 -MAX problem and 58% for its measurement applications. For weighted heavy hitters, our approach is up to 37.5% faster and requires up to 50% less space to achieve the same accuracy as state-of-the-art. In LRFU implementation, our approach is up to 33% faster than the best alternative. For programmable switch based caching, SQUID achieves similar hit ratio with the original CPU based LRFU and significantly outperforms 𝑞 -MAX. approaches. We show that our approximated policy achieves a comparable hit rate to LRFU. Its main benefit within our context is our ability to implement it as a RegisterAction on Tofino switches.
Crosslingual Generalization through Multitask Finetuning

Multitask prompted finetuning (MTF) has been shown to help large language models generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused on English data and models. We apply MTF to the pretrained multilingual BLOOM and mT5 model families to produce finetuned variants called BLOOMZ and mT0. We find finetuning large multilingual language models on English tasks with English prompts allows for task genrealization to non-English languages that appear only in the pretraining corpus. Finetuning on multilingual tasks with English prompts further improves performance on English and non-English tasks leading to various state-of-the-art zero-shot results. We also investigate finetuning on multilingual tasks with prompts that have been machine-translated from English to match the language of each dataset. We find training on these machine-translated prompts leads to better performance on human-written prompts in the respective languages. Surprisingly, we find models are capable of zero-shot generalization to tasks in languages they have never intentionally seen. We conjecture that the models are learning higher-level capabilities that are both task- and language-agnostic. In addition, we introduce xP3, a composite of supervised datasets in 46 languages with English and machine-translated prompts. Our code, datasets and models are freely available at https://github.com/ bigscience-workshop/xmtf.
The Fifth Catalogue of Nearby Stars (CNS5)

Context. We present the compilation of the Fifth Catalogue of Nearby Stars (CNS5), based on astrometric and photometric data from Gaia EDR3 and HIPPARCOS and supplemented with parallaxes from ground-based astrometric surveys carried out in the infrared. Aims. The aim of the CNS5 is to provide the most complete sample of objects in the solar neighbourhood. For all known stars and brown dwarfs in the 25 pc sphere around the Sun, basic astrometric and photometric parameters are given. Furthermore, we provide the colour-magnitude diagram (CMD) and various luminosity functions of the stellar content in the solar neighbourhood, and characterise the completeness of the CNS5 catalogue. Methods. We compiled a sample of stars and brown dwarfs that are most likely located within 25 pc of the Sun, taking space-based parallaxes from Gaia EDR3 and HIPPARCOS as well as ground-based parallaxes from Best et al. (2021, AJ, 161, 42), Kirkpatrick et al. (2021, ApJS, 253, 7), and from the CNS4 into account. We developed a set of selection criteria to clean the sample from spurious sources. Furthermore, we show that effects of blending in the Gaia photometry, which mainly affect the faint and red sources in Gaia, can be mitigated to reliably place those objects in a CMD. We also assessed the completeness of the CNS5 using a Kolmogorov-Smirnov test and derive observational optical and mid-infrared (MIR) luminosity functions for the main-sequence stars and white dwarfs (WDs) in the solar neighbourhood. Results. The CNS5 contains 5931 objects, including 5230 stars (4946 main-sequence stars, 20 red giants and 264 white dwarfs) and 701 brown dwarfs. We find that the CNS5 catalogue is statistically complete down to 19.7 mag in the G-band and 11.8 mag in W1-band absolute magnitudes, corresponding to a spectral type of L8. The stellar number density in the solar neighbourhood is (7.99 ± 0.11) × 10−2 stars pc−3, and about 72% of stars in the solar neighbourhood are M dwarfs. Furthermore, we show that the WD sample in CNS5 is statistically complete within 25 pc. The derived number density of WDs is (4.03 ± 0.25) × 10−3 stars pc−3. The ratio between stars and brown dwarfs within 15 pc is 4.6 ± 0.4, whereas within 25 pc it is 7.5 ± 0.3. Thus, we estimate that about one third of brown dwarfs are still missing within 25 pc, particularly those with spectral types later than L8 and distances close to the 25 pc limit.