Miracle-dz/UniFAR-FTUs huggingface dataset:
Searching the Search Space of Vision Transformer

Vision Transformer has shown great visual representation power in substantial vision tasks such as recognition and detection, and thus been attracting fast-growing efforts on manually designing more effective architectures. In this paper, we propose to use neural architecture search to automate this process, by searching not only the architecture but also the search space. The central idea is to gradually evolve different search dimensions guided by their E-T Error computed using a weight-sharing supernet. Moreover, we provide design guidelines of general vision transformers with extensive analysis according to the space searching process, which could promote the understanding of vision transformer. Remarkably, the searched models, named S3 (short for Searching the Search Space), from the searched space achieve superior performance to recently proposed models, such as Swin, DeiT and ViT, when evaluated on ImageNet. The effectiveness of S3 is also illustrated on object detection, semantic segmentation and visual question answering, demonstrating its generality to downstream vision and vision-language tasks. Code and models will be available at https://github.com/microsoft/Cream.
Environment Effects on X-Ray Absorption Spectra With Quantum Embedded Real-Time Time-Dependent Density Functional Theory Approaches

In this work we implement the real-time time-dependent block-orthogonalized Manby-Miller embedding (rt-BOMME) approach alongside our previously developed real-time frozen density embedding time-dependent density functional theory (rt-TDDFT-in-DFT FDE) code, and investigate these methods’ performance in reproducing X-ray absorption spectra (XAS) obtained with standard rt-TDDFT simulations, for model systems comprised of solvated fluoride and chloride ions ([X@ ( H 2 O ) 8 − , X = F, Cl). We observe that for ground-state quantities such as core orbital energies, the BOMME approach shows significantly better agreement with supermolecular results than FDE for the strongly interacting fluoride system, while for chloride the two embedding approaches show more similar results. For the excited states, we see that while FDE (constrained not to have the environment densities relaxed in the ground state) is in good agreement with the reference calculations for the region around the K and L1 edges, and is capable of reproducing the splitting of the 1s1 (n + 1)p1 final states (n + 1 being the lowest virtual p orbital of the halides), it by and large fails to properly reproduce the 1s1 (n + 2)p1 states and misses the electronic states arising from excitation to orbitals with important contributions from the solvent. The BOMME results, on the other hand, provide a faithful qualitative representation of the spectra in all energy regions considered, though its intrinsic approximation of employing a lower-accuracy exchange-correlation functional for the environment induces non-negligible shifts in peak positions for the excitations from the halide to the environment. Our results thus confirm that QM/QM embedding approaches are viable alternatives to standard real-time simulations of X-ray absorption spectra of species in complex or confined environments.
Pessimistic Model Selection for Offline Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) has demonstrated great potentials in solving sequential decision making problems in many applications. Despite its promising performance, practical gaps exist when deploying DRL in real-world scenarios. One main barrier is the over-fitting issue that leads to poor generalizability of the policy learned by DRL. In particular, for offline DRL with observational data, model selection is a challenging task as there is no ground truth available for performance demonstration, in contrast with the online setting with simulated environments. In this work, we propose a pessimistic model selection (PMS) approach for offline DRL with a theoretical guarantee, which features a provably effective framework for finding the best policy among a set of candidate models. Two refined approaches are also proposed to address the potential bias of DRL model in identifying the optimal policy. Numerical studies demonstrated the superior performance of our approach over existing methods.
EGFN: Efficient Geometry Feature Network for Fast Stereo 3D Object Detection

Fast stereo based 3D object detectors have made great progress in the sense of inference time recently. However, they lag far behind high-precision oriented methods in accuracy. We argue that the main reason is the missing or poor 3D geometry feature representation in fast stereo based methods. To solve this problem, we propose an efﬁ-cient geometry feature generation network (EGFN). The key of our EGFN is an efﬁcient and effective 3D geometry feature representation (EGFR) module. In the EGFR module, light-weight cost volume features are ﬁrstly generated, then are efﬁciently converted into 3D space, and ﬁnally multi-scale features enhancement in in both image and 3D spaces is conducted to obtain the 3D geometry features: enhanced light-weight voxel features. In addition, we introduce a novel multi-scale knowledge distillation strategy to guide multi-scale 3D geometry features learning. Experimental results on the public KITTI test set shows that the proposed EGFN outperforms YOLOStsereo3D, the advanced fast method, by 5.16% on mAP 3 d at the cost of merely additional 12 ms and hence achieves a better trade-off between accuracy and efﬁciency for stereo 3D object detection. Our code will be publicly available.
ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification

Contrastive learning has led to substantial improvements in the quality of learned embedding representations for tasks such as image classification. However, a key drawback of existing contrastive augmentation methods is that they may lead to the modification of the image content which can yield undesired alterations of its semantics. This can affect the performance of the model on downstream tasks. Hence, in this paper, we ask whether we can augment image data in contrastive learning such that the task-relevant semantic content of an image is preserved. For this purpose, we propose to leverage saliency-based explanation methods to create content-preserving masked augmentations for contrastive learning. Our novel explanation-driven supervised contrastive learning (ExCon) methodology critically serves the dual goals of encouraging nearby image embeddings to have similar content and explanation. To quantify the impact of ExCon, we conduct experiments on the CIFAR-100 and the Tiny ImageNet datasets. We demonstrate that ExCon outperforms vanilla supervised contrastive learning in terms of classification, explanation quality, adversarial robustness as well as probabilistic calibration in the context of distributional shift.
Asymptotic equidistribution for partition statistics and topological invariants

We provide a general framework for proving asymptotic equidistribution, convexity, and log concavity of coefficients of generating functions on arithmetic progressions. Our central tool is a variant of Wright's Circle Method proved by two of the authors with Bringmann and Ono, following work of Ngo and Rhoades. We offer a selection of different examples of such results, proving asymptotic equidistribution results for several partition statistics, modular sums of Betti numbers of two- and three-flag Hilbert schemes, and the number of cells of dimension a (mod b) of a certain scheme central in work of G\"ottsche.
Long-range and hierarchical language predictions in brains and algorithms

Deep learning has recently made remarkable progress in natural language processing. Yet, the resulting algorithms remain far from competing with the language abilities of the human brain. Predictive coding theory offers a potential explanation to this discrepancy: while deep language algorithms are optimized to predict adjacent words, the human brain would be tuned to make long-range and hierarchical predictions. To test this hypothesis, we analyze the fMRI brain signals of 304 subjects each listening to 70min of short stories. After confirming that the activations of deep language algorithms linearly map onto those of the brain, we show that enhancing these models with long-range forecast representations improves their brain-mapping. The results further reveal a hierarchy of predictions in the brain, whereby the fronto-parietal cortices forecast more abstract and more distant representations than the temporal cortices. Overall, this study strengthens predictive coding theory and suggests a critical role of long-range and hierarchical predictions in natural language processing.
Randomized Controlled Trials Under Influence: Covariate Factors and Graph-Based Network Interference

Randomized controlled trials are not only the golden standard in medicine and vaccine trials but have spread to many other disciplines like behavioral economics, making it an important interdisciplinary tool for scientists. When designing randomized controlled trials, how to assign participants to treatments becomes a key issue. In particular in the presence of covariate factors, the assignment can significantly influence statistical properties and thereby the quality of the trial. Another key issue is the widely popular assumption among experimenters that participants do not influence each other -- which is far from reality in a field study and can, if unaccounted for, deteriorate the quality of the trial. We address both issues in our work. After introducing randomized controlled trials bridging terms from different disciplines, we first address the issue of participant-treatment assignment in the presence of known covariate factors. Thereby, we review a recent assignment algorithm that achieves good worst-case variance bounds. Second, we address social spillover effects. Therefore, we build a comprehensive graph-based model of influence between participants, for which we design our own average treatment effect estimator $\hat \tau_{net}$. We discuss its bias and variance and reduce the problem of variance minimization to a certain instance of minimizing the norm of a matrix-vector product, which has been considered in literature before. Further, we discuss the role of disconnected components in the model's underlying graph.
Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting

Recent studies have shifted their focus towards formulating traffic forecasting as a spatio-temporal graph modeling problem. Typically, they constructed a static spatial graph at each time step and then connected each node with itself between adjacent time steps to create a spatio-temporal graph. However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks. Additionally, those models overlooked the dynamic spatio-temporal correlations among nodes by using the same adjacency matrix across different time steps. To address these limitations, we propose a novel approach called Spatio-Temporal Joint Graph Convolutional Networks (STJGCN) for accurate traffic forecasting on road networks over multiple future time steps. Specifically, our method encompasses the construction of both pre-defined and adaptive spatio-temporal joint graphs (STJGs) between any two time steps, which represent comprehensive and dynamic spatio-temporal correlations. We further introduce dilated causal spatio-temporal joint graph convolution layers on the STJG to capture spatio-temporal dependencies from distinct perspectives with multiple ranges. To aggregate information from different ranges, we propose a multi-range attention mechanism. Finally, we evaluate our approach on five public traffic datasets and experimental results demonstrate that STJGCN is not only computationally efficient but also outperforms 11 state-of-the-art baseline methods.
Groupoid actions on sets, duality and a morita context

Abstract Let and be groupoids. We present the notion of a -set and we prove a duality theorem in this context, which extends the duality theorem for graded algebras by groups. For A a unital -graded algebra and X a finite split -set, we show that there is an isomorphism between the category of the X-graded left A-modules and the category of the left -modules. As an application of this isomorphism, we construct a Morita context.
Cicatricial pemphigoid Brunsting–Perry variant masquerading as neutrophil‐mediated cicatricial alopecia

A 72‐year‐old male presented with scarring alopecia on the scalp vertex, multiple crusted plaques on the hairline, and a history of vesicular eruption on the face. The scalp showed crusted plaques with loss of follicular ostia. No follicular pustules or compound follicles were present. An initial transverse scalp biopsy showed perifollicular neutrophils, lymphocytes, and plasma cells along with dermal fibrosis. Focal epidermal/dermal and follicular/adventitial dermal clefts were apparent but were thought to be secondary to fibrosis, and the biopsy result was interpreted to represent a neutrophil‐mediated cicatricial alopecia. Concurrently, direct immunofluorescence (DIF) analysis showed linear junctional deposition of IgG and C3. A repeat scalp biopsy revealed more prominent epidermal/dermal clefts, fibrosis, mixed infiltrate with neutrophils, lymphocytes, histiocytes, and plasma cells, as well as prominent follicular/adventitial dermal clefts with perifollicular neutrophils. Given the combination of clefts, perijunctional neutrophils, and positive DIF findings, it became clear that this eruption represented the Brunsting–Perry variant of cicatricial pemphigoid. Here, we illustrated that a neutrophil‐rich form of cicatricial pemphigoid can masquerade as a neutrophil‐mediated scarring alopecia. In evaluating a specimen suspected to be a neutrophil‐mediated scarring alopecia, one should be alert to the presence of subepidermal and perifollicular clefting, and consider cicatricial pemphigoid.
Zero cycles, Mennicke symbols and $\mathrm{K}_1$-stability of certain real affine algebras

Let $R$ be a reduced real affine algebra of (Krull) dimension $d \ge 2$ such that either $R$ has no real maximal ideals, or the intersection of all real maximal ideals in $R$ has a height of at least one. In this article, we prove the following: (1) the $d$-th Euler class group $\text{E}^d(R)$, defined by Bhatwadekar-R. ~Sridharan, is canonically isomorphic to the Levine-Weibel Chow group of zero cycles $\text{CH}_0(\text{Spec}(R))$; (2) the universal Mennicke symbol $\text{MS}_{d+1}(R)$ is canonically isomorphic to the universal weak Mennicke symbol $\text{WMS}_{d+1}(R)$; and (3) additionally, if $R$ is a regular domain, then the Whitehead group $\mathrm{SK_1}(R)$ is canonically isomorphic to $\frac{\text{SL}_{d+1}(R)}{\text{E}_{d+1}(R)}$. As an application, we investigate some Eisenbud-Evans type theorems.
Cell-Free Massive MIMO Detection: A Distributed Expectation Propagation Approach

Cell-free massive MIMO is one of the core technologies for future wireless networks. It is expected to bring enormous benefits, including ultra-high reliability, data throughput, energy efficiency, and uniform coverage. As a radically distributed system, the performance of cell-free massive MIMO critically relies on efficient distributed processing algorithms. In this paper, we propose a distributed expectation propagation (EP) detector for cell-free massive MIMO, which consists of two modules: a nonlinear module at the central processing unit (CPU) and a linear module at each access point (AP). The turbo principle in iterative channel decoding is utilized to compute and pass the extrinsic information between the two modules. An analytical framework is provided to characterize the asymptotic performance of the proposed EP detector with a large number of antennas. Furthermore, a distributed iterative channel estimation and data detection (ICD) algorithm is developed to handle the practical setting with imperfect channel state information (CSI). Simulation results will show that the proposed method outperforms existing detectors for cell-free massive MIMO systems in terms of the bit-error rate and demonstrate that the developed theoretical analysis accurately predicts system performance. Finally, it is shown that with imperfect CSI, the proposed ICD algorithm improves the system performance significantly and enables non-orthogonal pilots to reduce the pilot overhead.
Braking and Body Angles Control of an Insect-Computer Hybrid Robot by Electrical Stimulation of Beetle Flight Muscle in Free Flight

While engineers put lots of effort, resources, and time in building insect scale micro aerial vehicles (MAVs) that fly like insects, insects themselves are the real masters of flight. What if we would use living insect as platform for MAV instead? Here, we reported a flight control via electrical stimulation of a flight muscle of an insect-computer hybrid robot, which is the interface of a mountable wireless backpack controller and a living beetle. The beetle uses indirect flight muscles to drive wing flapping and three major direct flight muscles (basalar, subalar and third axilliary (3Ax) muscles) to control the kinematics of the wings for flight maneuver. While turning control was already achieved by stimulating basalar and 3Ax muscles, electrical stimulation of subalar muscles resulted in braking and elevation control in flight. We also demonstrated around 20 degrees of contralateral yaw and roll by stimulating individual subalar muscle. Stimulating both subalar muscles lead to an increase of 20 degrees in pitch and decelerate the flight by 1.5 m/s as well as an induce an elevation of 2 m/s.
CoNIC: Colon Nuclei Identification and Counting Challenge 2022

Nuclear segmentation, classification and quantification within Haematoxylin&Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology (CPath). However, automatic recognition of different nuclei is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intra-class variability. To help drive forward research and innovation for automatic nuclei recognition in CPath, we organise the Colon Nuclei Identification and Counting (CoNIC) Challenge. The challenge encourages researchers to develop algorithms that perform segmentation, classification and counting of nuclei within the current largest known publicly available nuclei-level dataset in CPath, containing around half a million labelled nuclei. Therefore, the CoNIC challenge utilises over 10 times the number of nuclei as the previous largest challenge dataset for nuclei recognition. It is important for algorithms to be robust to input variation if we wish to deploy them in a clinical setting. Therefore, as part of this challenge we will also test the sensitivity of each submitted algorithm to certain input variations.
Rank-Constrained Least-Squares: Prediction and Inference

In this work, we focus on the high-dimensional trace regression model with a low-rank coeﬃcient matrix. We establish a nearly optimal in-sample prediction risk bound for the rank-constrained least-squares estimator under no assumptions on the design matrix. Lying at the heart of the proof is a covering number bound for the family of projection operators corresponding to the subspaces spanned by the design. By leveraging this complexity result, we perform a power analysis for a permutation test on the existence of a low-rank signal under the high-dimensional trace regression model. We show that the permutation test based on the rank-constrained least-squares estimator achieves non-trivial power with no assumptions on the minimum (restricted) eigenvalue of the covariance matrix of the design. Finally, we use alternating minimization to approximately solve the rank-constrained least-squares problem to evaluate its empirical in-sample prediction risk and power of the resulting permutation test in our numerical study.
Nonminimal Lorentz invariance violation in light of the muon anomalous magnetic moment and long-baseline neutrino oscillation data

In light of the increasing hints of new physics at the muon g − 2 and neutrino oscillation experiments, we consider the recently observed tension in the long-baseline neutrino oscillation experiments as a potential indication of Lorentz invariance violation. For this purpose, the latest data from T2K and NO ν A is analysed in presence of non-minimal Lorentz invariance violation. Indeed, we ﬁnd that isotropic violation in dimensions D = 4, 5 and 6 can alleviate the tension in neutrino oscillation data by about 0.4–2.4 σ CL signiﬁcance, with the isotropic coeﬃcient γ (5) ττ = 3.58 × 10 − 32 GeV − 1 yielding the best ﬁt. At the same time, the anomalous muon g − 2 result can be reproduced with an additional non-isotropic violation of d zt = -1.7 × 10 − 25 . The analysis highlights the possibility of simultaneous relaxation of experimental tensions with Lorentz invariance violation of mixed nature.
GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection

Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems. However, current pre-training methods mainly focus on enhancing dialog understanding and generation tasks while neglecting the exploitation of dialog policy. In this paper, we propose GALAXY, a novel pre-trained dialog model that explicitly learns dialog policy from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised learning. Specifically, we introduce a dialog act prediction task for policy optimization during pre-training and employ a consistency regularization term to refine the learned representation with the help of unlabeled dialogs. We also implement a gating mechanism to weigh suitable unlabeled dialog samples. Empirical results show that GALAXY substantially improves the performance of task-oriented dialog systems, and achieves new state-of-the-art results on benchmark datasets: In-Car, MultiWOZ2.0 and MultiWOZ2.1, improving their end-to-end combined scores by 2.5, 5.3 and 5.5 points, respectively. We also show that GALAXY has a stronger few-shot ability than existing models under various low-resource settings. For reproducibility, we release the code and data at https://github.com/siat-nlp/GALAXY.