Miracle-dz/UniFAR-FTUs huggingface dataset:
Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models

Recently the prompt-tuning paradigm has attracted significant attention. By only tuning continuous prompts with a frozen pre-trained language model (PLM), prompt-tuning takes a step towards deploying a shared frozen PLM to serve numerous downstream tasks. Although prompt-tuning shows good performance on certain natural language understanding (NLU) tasks, its effectiveness on natural language generation (NLG) tasks is still under-explored. In this paper, we argue that one of the factors hindering the development of prompt-tuning on NLG tasks is the unfamiliar inputs (i.e., inputs are linguistically different from the pretraining corpus). For example, our preliminary exploration reveals a large performance gap between prompt-tuning and fine-tuning when unfamiliar inputs occur frequently in NLG tasks. This motivates us to propose input-tuning, which fine-tunes both the continuous prompts and the input representations, leading to a more effective way to adapt unfamiliar inputs to frozen PLMs. Our proposed input-tuning is conceptually simple and empirically powerful. Experimental results on seven NLG tasks demonstrate that input-tuning is significantly and consistently better than prompt-tuning. Furthermore, on three of these tasks, input-tuning can achieve a comparable or even better performance than fine-tuning.
On the pitfalls of entropy-based uncertainty for multi-class semi-supervised segmentation

Semi-supervised learning has emerged as an appealing strategy to train deep models with limited supervision. Most prior literature under this learning paradigm resorts to dual-based architectures, typically composed of a teacher-student duple. To drive the learning of the student, many of these models leverage the aleatoric uncertainty derived from the entropy of the predictions. While this has shown to work well in a binary scenario, we demonstrate in this work that this strategy leads to suboptimal results in a multi-class context, a more realistic and challenging setting. We argue, indeed, that these approaches underperform due to the erroneous uncertainty approximations in the presence of inter-class overlap. Furthermore, we propose an alternative solution to compute the uncertainty in a multi-class setting, based on divergence distances and which account for inter-class overlap. We evaluate the proposed solution on a challenging multi-class segmentation dataset and in two well-known uncertainty-based segmentation methods. The reported results demonstrate that by simply replacing the mechanism used to compute the uncertainty, our proposed solution brings substantial improvement on tested setups.
Multivariate Time Series Forecasting with Latent Graph Inference

This paper introduces a new approach for Multivariate Time Series forecasting that jointly infers and leverages relations among time series. Its modularity allows it to be integrated with current univariate methods. Our approach allows to trade-off accuracy and computational efficiency gradually via offering on one extreme inference of a potentially fully-connected graph or on another extreme a bipartite graph. In the potentially fully-connected case we consider all pair-wise interactions among time-series which yields the best forecasting accuracy. Conversely, the bipartite case leverages the dependency structure by inter-communicating the N time series through a small set of K auxiliary nodes that we introduce. This reduces the time and memory complexity w.r. t. previous graph inference methods from O(N^2) to O(NK) with a small trade-off in accuracy. We demonstrate the effectiveness of our model in a variety of datasets where both of its variants perform better or very competitively to previous graph inference methods in terms of forecasting accuracy and time efficiency.
Uncertify: Attacks Against Neural Network Certification

A key concept towards reliable, robust, and safe AI systems is the idea to implement fallback strategies when predictions of the AI cannot be trusted. Certifiers for neural networks have made great progress towards provable robustness guarantees against evasion attacks using adversarial examples. These methods guarantee for some predictions that a certain class of manipulations or attacks could not have changed the outcome. For the remaining predictions without guarantees, the method abstains from making a prediction and a fallback strategy needs to be invoked, which is typically more costly, less accurate, or even involves a human operator. While this is a key concept towards safe and secure AI, we show for the first time that this strategy comes with its own security risks, as such fallback strategies can be deliberately triggered by an adversary. In particular, we conduct the first systematic analysis of training-time attacks against certifiers in practical application pipelines, identifying new threat vectors that can be exploited to degrade the overall system. Using these insights, we design two backdoor attacks against network certifiers, which can drastically reduce certified robustness. For example, adding 1% poisoned data during training is sufficient to reduce certified robustness by up to 95 percentage points, effectively rendering the certifier useless. We analyze how such novel attacks can compromise the overall system’s integrity or availability. Our extensive experiments across multiple datasets, model architectures, and certifiers demonstrate the wide applicability of these attacks. A first investigation into potential defenses shows that current approaches are insufficient to mitigate the issue, highlighting the need for new, more specific solutions.
Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information

Fine-grained image classification is a challenging computer vision task where various species share similar visual appearances, resulting in misclassification if merely based on visual clues. Therefore, it is helpful to leverage additional information, e.g., the locations and dates for data shooting, which can be easily accessible but rarely exploited. In this paper, we first demonstrate that existing multimodal methods fuse multiple features only on a single dimension, which essentially has insufficient help in feature discrimination. To fully explore the potential of multimodal information, we propose a dynamic MLP on top of the image representation, which interacts with multimodal features at a higher and broader dimension. The dynamic MLP is an efficient structure parameterized by the learned embeddings of variable locations and dates. It can be regarded as an adaptive nonlinear projection for generating more discriminative image representations in visual tasks. To our best knowledge, it is the first attempt to explore the idea of dynamic networks to exploit multimodal information in fine-grained image classification tasks. Extensive experiments demonstrate the effectiveness of our method. The t-SNE algorithm visually indicates that our technique improves the recognizability of image representations that are visually similar but with different categories. Furthermore, among published works across multiple fine-grained datasets, dynamic MLP consistently achieves SOTA results11https://paperswithcode.com/dataset/inaturalist and takes third place in the iNaturalist challenge at FGVC822https://www.kaggle. com/c/inaturalist-2021/leaderboard. Code is available at httpsr//glthub.com/megvii-research/DynamicMLPForFinegrained.
Information projection approach to propensity score estimation for handling selection bias under missing at random

Propensity score weighting is widely used to improve the representativeness and correct the selection bias in the voluntary sample. The propensity score is often developed using a model for the sampling probability, which can be subject to model misspecification. In this paper, we consider an alternative approach of estimating the inverse of the propensity scores using the density ratio function satisfying the self-efficiency condition. The smoothed density ratio function is obtained by the solution to the information projection onto the space satisfying the moment conditions on the balancing scores. By including the covariates for the outcome regression models only in the density ratio model, we can achieve efficient propensity score estimation. Penalized regression is used to identify important covariates. We further extend the proposed approach to the multivariate missing case. Some limited simulation studies are presented to compare with the existing methods.
Evidence for X-Ray Emission in Excess to the Jet-afterglow Decay 3.5 yr after the Binary Neutron Star Merger GW 170817: A New Emission Component

For the first ∼3 yrs after the binary neutron star merger event GW 170817, the radio and X-ray radiation has been dominated by emission from a structured relativistic off-axis jet propagating into a low-density medium with n < 0.01 cm−3. We report on observational evidence for an excess of X-ray emission at δt > 900 days after the merger. With L x ≈ 5 × 1038 erg s−1 at 1234 days, the recently detected X-ray emission represents a ≥3. 2σ (Gaussian equivalent) deviation from the universal post-jet-break model that best fits the multiwavelength afterglow at earlier times. In the context of JetFit afterglow models, current data represent a departure with statistical significance ≥3. 1σ, depending on the fireball collimation, with the most realistic models showing excesses at the level of ≥3. 7σ. A lack of detectable 3 GHz radio emission suggests a harder broadband spectrum than the jet afterglow. These properties are consistent with the emergence of a new emission component such as synchrotron radiation from a mildly relativistic shock generated by the expanding merger ejecta, i.e., a kilonova afterglow. In this context, we present a set of ab initio numerical relativity binary neutron star (BNS) merger simulations that show that an X-ray excess supports the presence of a high-velocity tail in the merger ejecta, and argues against the prompt collapse of the merger remnant into a black hole. Radiation from accretion processes on the compact-object remnant represents a viable alternative. Neither a kilonova afterglow nor accretion-powered emission have been observed before, as detections of BNS mergers at this phase of evolution are unprecedented.
Handling Distribution Shifts on Graphs: An Invariance Perspective

There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so that research on out-of-distribution (OOD) generalization comes into the spotlight. Nonetheless, current endeavors mostly focus on Euclidean data, and its formulation for graph-structured data is not clear and remains under-explored, given two-fold fundamental challenges: 1) the inter-connection among nodes in one graph, which induces non-IID generation of data points even under the same environment, and 2) the structural information in the input graph, which is also informative for prediction. In this paper, we formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM), that facilitates graph neural networks to leverage invariance principles for prediction. EERM resorts to multiple context explorers (specified as graph structure editers in our case) that are adversarially trained to maximize the variance of risks from multiple virtual environments. Such a design enables the model to extrapolate from a single observed environment which is the common case for node-level prediction. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution and further demonstrate its power on various real-world datasets for handling distribution shifts from artificial spurious features, cross-domain transfers and dynamic graph evolution.
SwarmUS: An open hardware and software on-board platform for swarm robotics development

Real life implementations of distributed swarm robotics are rare. The standardization of a general purpose swarm robotics platform could greatly accelerate swarm robotics towards real life implementations. The SwarmUS platform is an open-source hardware and software on-board embedded system designed to be added onto existing robots while providing them with swarm features, thus proposing a new take on the platform standardization problem. These features include a distributed relative localization system based on Ultra-Wideband, a local communication system based on Wi-Fi and a distributed coordination system based on the Buzz programming language between robots connected within a SwarmUS platform. Additionally, a human-swarm interaction mobile application and an emulation of the platform in the Robot Operating System (ROS) is presented. Finally, an implementation of the system was realized and tested on two types of robots : a TurtleBot3 Burger and two Pioneer 2DX.
Properties of doubly heavy baryons in QCD

The study of the properties of doubly heavy baryons represents a promising area in particle physics. It can provide us with information about Cabibbo–Kobayashi–Maskawa (CKM) matrix elements and the low energy dynamics of QCD. They have a very rich phenomenology. The investigation of weak, electromagnetic, and strong decays has a vital importance for understanding the dynamics of doubly heavy baryons. The main ingredients of such studies are the spectroscopic parameters, the strong coupling constants, and the transition form factors. For calculations of these quantities, non-perturbative methods are needed. One of these methods is the QCD sum rules. In the present work, we review our studies on the properties of the doubly heavy baryons within the sum rules method, focusing mainly on the mass and strong coupling constants of the doubly heavy baryons. In addition, the radiative decays of doubly heavy baryons are estimated using the vector dominance model. We also make few remarks on the semileptonic decays of the doubly heavy baryons. ∗ taliev@metu.edu. tr
Acceleration of Federated Learning with Alleviated Forgetting in Local Training

Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy by independently training local models on each client and then aggregating parameters on a central server, thereby producing an effective global model. Although a variety of FL algorithms have been proposed, their training efficiency remains low when the data are not independently and identically distributed (non-i.i.d.) across different clients. We observe that the slow convergence rates of the existing methods are (at least partially) caused by the catastrophic forgetting issue during the local training stage on each individual client, which leads to a large increase in the loss function concerning the previous training data at the other clients. Here, we propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage by regularizing locally trained parameters with the loss on generated pseudo data, which encode the knowledge of previous training data learned by the global model. Our comprehensive experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep and the clients' data are extremely non-i.i. d. , but is also able to protect privacy better in classification problems and more robust against gradient inversion attacks. The code is available at: https://github.com/Zoesgithub/FedReg.
Melting of crystals of polarization vortices and chiral phase transitions in oxide superlattices

We study the equilibrium arrangements of polarization vortices in (PbTiO$_3$)$_n$/(SrTiO$_3$)$_n$ superlattices by means of second-principles simulations. We find that, at low temperatures, polarization vortices organize in a regular arrangement in which clockwise and counter-clockwise vortices alternate positions, leading to a crystal-like structure with well defined handedness. This chiral crystal melts at a critical temperature $T_\mathrm{M}$ into a chiral liquid, where long-range order is lost but handedness is preserved. At even higher temperatures, $T_\mathrm{C}$, a second phase transition occurs, at which the chiral liquid of polarization vortices loses its handedness. Both phase transitions can be readily identified by the adequate choices of order parameters.
Virtual vs. reality: external validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography

Research studies of artificial intelligence models in medical imaging have been hampered by poor generalization. This problem has been especially concerning over the last year with numerous applications of deep learning for COVID-19 diagnosis. Virtual imaging trials (VITs) could provide a solution for objective evaluation of these models. In this work utilizing the VITs, we created the CVIT-COVID dataset including 180 virtually imaged computed tomography (CT) images from simulated COVID-19 and normal phantom models under different COVID-19 morphology and imaging properties. We evaluated the performance of an open-source, deep-learning model from the University of Waterloo trained with multi-institutional data and an in-house model trained with the open clinical dataset called MosMed. We further validated the model's performance against open clinical data of 305 CT images to understand virtual vs. real clinical data performance. The open-source model was published with nearly perfect performance on the original Waterloo dataset but showed a consistent performance drop in external testing on another clinical dataset (AUC=0.77) and our simulated CVIT-COVID dataset (AUC=0.55). The in-house model achieved an AUC of 0.87 while testing on the internal test set (MosMed test set). However, performance dropped to an AUC of 0.65 and 0.69 when evaluated on clinical and our simulated CVIT-COVID dataset. The VIT framework offered control over imaging conditions, allowing us to show there was no change in performance as CT exposure was changed from 28.5 to 57 mAs. The VIT framework also provided voxel-level ground truth, revealing that performance of in-house model was much higher at AUC=0.87 for diffuse COVID-19 infection size <2.65% lung volume versus AUC=0.52 for focal disease with <2.65% volume. The virtual imaging framework enabled these uniquely rigorous analyses of model performance, which would be impracticable with real patients.
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion

The techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two promising approaches, this paper focuses on the utility of the Exponential Smoothing-Recurrent Neural Network (ES-RNN) in the pool of base learners for different ensembles. We compare against some state-of-the-art ensembling techniques and arithmetic model averaging as a benchmark. We experiment with the M4 forecasting dataset of 100,000 time-series, and the results show that the Feature-Based FORecast Model Averaging (FFORMA), on average, is the best technique for late data fusion with the ES-RNN. However, considering the M4’s Daily subset of data, stacking was the only successful ensemble at dealing with the case where all base learner performances were similar. Our experimental results indicate that we attain state-of-the-art forecasting results compared to Neural Basis Expansion Analysis (N-BEATS) as a benchmark. We conclude that model averaging is a more robust ensembling technique than model selection and stacking strategies. Further, the results show that gradient boosting is superior for implementing ensemble learning strategies.
WASP-35 and HAT-P-30/WASP-51: Reanalysis using TESS and Ground-based Transit Photometry

High-precision transit observations provide excellent opportunities for characterizing the physical properties of exoplanetary systems. These physical properties supply many pieces of information for unvealing the internal structure, external atmosphere, and dynamical history of the planets. We present revised properties of the transiting systems WASP-35 and HAT-P-30/WASP-51 through analyzing newly available TESS photometry and ground-based observations obtained at the 1 m telescope of the Yunnan Observatories as well as from the literature. The improved system parameters are consistent with previous results. Furthermore, we find that the transits of HAT-P-30b/WASP-51b show possible timing variation that cannot be explained by a decaying orbit due to tidal dissipation and the Rømer effect, while both apsidal precession and an additional perturbing body could reproduce this signal according to our comprehensive dynamical simulations. Because both systems are valuable targets that are suitable for transmission spectroscopy, we make some predictions for the atmospheric properties of WASP-35b and HAT-P-30b/WASP-51b based on the newly derived system parameters.
Constraining the shape of Milky Way satellites with distance gradients

We combine the Dark Energy Camera Legacy Survey (DECaLS) DR8 photometry with Gaia photometry to study the 3-D structure of Bootes I, Draco, Ursa Minor, Sextans and Sculptor dwarf galaxies using blue horizontal branch (BHB) stars as distance indicators. We construct a new colour-absolute magnitude of BHB stars that we use to measure the distance gradients within the body of the dwarf galaxies. We detect a statistically significant non-zero gradient only in Sextans and Sculptor. Through modeling of the gradient and 2-D density of the systems by triaxial Plummer models we find that the distance gradients in both dwarf galaxies are inconsistent with prolate shape, but compatible with oblate or triaxial shapes. In order to explain the observed gradients, oblate models of Sextans and Sculptor need to have a significant intrinsic ellipticity larger than 0.47 for Sextans and 0.46 for Sculptor. The flattened oblate shape may imply a significant anisotropy in velocity distribution in order to be consistent with the lack of significant velocity gradients in these systems.
Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms

Differential privacy provides a theoretical framework for processing a dataset about $n$ users, in a way that the output reveals a minimal information about any single user. Such notion of privacy is usually ensured by noise-adding mechanisms and amplified by several processes, including subsampling, shuffling, iteration, mixing and diffusion. In this work, we provide privacy amplification bounds for quantum and quantum-inspired algorithms. In particular, we show for the first time, that algorithms running on quantum encoding of a classical dataset or the outcomes of quantum-inspired classical sampling, amplify differential privacy. Moreover, we prove that a quantum version of differential privacy is amplified by the composition of quantum channels, provided that they satisfy some mixing conditions.
Competitive Exclusion in an Artificial Foraging Ecosystem

Artificial ecosystems provide an additional experimental tool to support laboratory work, field work, and theoretical development in competitive exclusion research. A novel application of a spatiotemporal agent based model is presented which simulates two foraging species of different intrinsic growth rates competing for the same replenishing resource in stable and seasonal environments. This experimental approach provides precise control over the parameters for the environment, the species, and individual movements. Detailed trajectories of these non-equilibrium populations and their characteristics are produced. Narrow zones of potential coexistence are identified within the environmental and intrinsic growth parameter spaces. An example of commensalism driven by the local spatial dynamics is identified. Results of these experiments are discussed in context of modern coexistence theory and research in movement-mediated community assembly. Constraints on possible origination scenarios are identified.