Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer

Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some developing cities have few available data, which makes it infeasible to train a well-performed model. To address this challenge, cross-city knowledge transfer has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities. However, the spatio-temporal graphs among different cities show irregular structures and varied features, which limits the feasibility of existing Few-Shot Learning (FSL) methods. Therefore, we propose a model-agnostic few-shot learning framework for spatio-temporal graph called ST-GFSL. Specifically, to enhance feature extraction by transferring cross-city knowledge, ST-GFSL proposes to generate non-shared parameters based on node-level meta knowledge. The nodes in target city transfer the knowledge via parameter matching, retrieving from similar spatio-temporal characteristics. Furthermore, we propose to reconstruct the graph structure during meta-learning. The graph reconstruction loss is defined to guide structure-aware learning, avoiding structure deviation among different datasets. We conduct comprehensive experiments on four traffic speed prediction benchmarks and the results demonstrate the effectiveness of ST-GFSL compared with state-of-the-art methods.
On strict polynomial functors with bounded domain

We introduce a new functor category: the category $\mathcal{P}_{d,n}$ of strict polynomial functors with bounded by $n$ domain of degree $d$ over a field of characteristic $p>0$. It is equivalent to the category of finite dimensional modules over the Schur algebra $S(n,d)$, hence it allows one to apply the tools available in functor categories to representations of the algebraic group $\operatorname{GL}_n$. We investigate in detail the homological algebra in ${\cal P}_{d,n}$ for $d=p$ and establish equivalences between certain subcategories of ${\cal P}_{d,n}$'s which resemble the Spanier-Whitehead duality in stable homotopy theory.
The Derived Pure Spinor Formalism as an Equivalence of Categories

We construct a derived generalization of the pure spinor superfield formalism and prove that it exhibits an equivalence of dg-categories between multiplets for a supertranslation algebra and equivariant modules over its Chevalley-Eilenberg cochains. This equivalence is closely linked to Koszul duality for the supertranslation algebra. After introducing and describing the category of supermultiplets, we define the derived pure spinor construction explicitly as a dg-functor. We then show that the functor that takes the derived supertranslation invariants of any supermultiplet is a quasi-inverse to the pure spinor construction, using an explicit calculation. Finally, we illustrate our findings with examples and use insights from the derived formalism to answer some questions regarding the ordinary (underived) pure spinor superfield formalism.
Explaining Preferences with Shapley Values

While preference modelling is becoming one of the pillars of machine learning, the problem of preference explanation remains challenging and underexplored. In this paper, we propose \textsc{Pref-SHAP}, a Shapley value-based model explanation framework for pairwise comparison data. We derive the appropriate value functions for preference models and further extend the framework to model and explain \emph{context specific} information, such as the surface type in a tennis game. To demonstrate the utility of \textsc{Pref-SHAP}, we apply our method to a variety of synthetic and real-world datasets and show that richer and more insightful explanations can be obtained over the baseline.
Geometry of transcendental singularities of complex analytic functions and vector fields

We study transcendental meromorphic functions with essential singularities on Riemann surfaces. Every function $\Psi_X$ has associated a complex vector field $X$. In the converse direction, vector fields $X$ provide single valued or multivalued functions $\Psi_X$. Our goal is to understand the relationship between the analytical properties of $\Psi_X$, the singularities of its inverse $\Psi_X^{-1}$ and the geometric behavior of $X$. As first result, by examining the containment properties of the neighborhoods of the singularities of $\Psi_X^{-1}$, we characterize when a singularity of $\Psi_X^{-1}$ over a singular value $a$, is either algebraic, logarithmic or non logarithmic. Secondly, to make precise the cooperative aspects between analysis and geometry, we present the systematic study of two holomorphic families of transcendental functions with an essential singularity at infinity, as well as some sporadic examples. As third stage, we study the incompleteness of the trajectories of the associated vector field $X$ with essential singularities on a Riemann surface $M_{\mathfrak g}$ of genus ${\mathfrak g}$. As an application, we provide conditions under which there exists an infinite number of (real) incomplete trajectories of $X$ localized at the essential singularities. Furthermore, removing the incomplete trajectories decomposes the Riemann surface into real flow invariant canonical pieces.
Sharpness-Aware Training for Free

Modern deep neural networks (DNNs) have achieved state-of-the-art performances but are typically over-parameterized. The over-parameterization may result in undesirably large generalization error in the absence of other customized training strategies. Recently, a line of research under the name of Sharpness-Aware Minimization (SAM) has shown that minimizing a sharpness measure, which reflects the geometry of the loss landscape, can significantly reduce the generalization error. However, SAM-like methods incur a two-fold computational overhead of the given base optimizer (e.g. SGD) for approximating the sharpness measure. In this paper, we propose Sharpness-Aware Training for Free, or SAF, which mitigates the sharp landscape at almost zero additional computational cost over the base optimizer. Intuitively, SAF achieves this by avoiding sudden drops in the loss in the sharp local minima throughout the trajectory of the updates of the weights. Specifically, we suggest a novel trajectory loss, based on the KL-divergence between the outputs of DNNs with the current weights and past weights, as a replacement of the SAM's sharpness measure. This loss captures the rate of change of the training loss along the model's update trajectory. By minimizing it, SAF ensures the convergence to a flat minimum with improved generalization capabilities. Extensive empirical results show that SAF minimizes the sharpness in the same way that SAM does, yielding better results on the ImageNet dataset with essentially the same computational cost as the base optimizer.
Emergent organization of receptive fields in networks of excitatory and inhibitory neurons

: Local patterns of excitation and inhibition that can generate neural waves are studied as a computational mechanism underlying the organization of neuronal tunings. Sparse coding algorithms based on networks of excitatory and inhibitory neurons are proposed that exhibit topographic maps as the receptive ﬁelds are adapted to input stimuli. Motivated by a leaky integrate-and-ﬁre model of neural waves, we propose an activation model that is more typical of artiﬁcial neural networks. Computational experiments with the activation model using both natural images and natural language text are presented. In the case of images, familiar “pinwheel” patterns of oriented edge detectors emerge; in the case of text, the resulting topographic maps exhibit a 2-dimensional representation of granular word semantics. Experiments with a synthetic model of somatosensory input are used to investigate how the network dynamics may affect plasticity of neuronal maps under changes to the inputs.
Wideband multiple-input–multiple-output dielectric resonator antenna

A two-element multiple-input–multiple-output (MIMO) dielectric resonator antenna having wideband characteristics is presented. The wideband operation is achieved by using a mushroom shaped dielectric resonator excited by a conformal trapezoidal patch. In order to realise two-element MIMO configuration, two wideband radiators are arranged orthogonally which offers polarisation diversity. The measured bandwidth (VSWR ≤ 2) for Port1 is 61% (5.08–9.50 GHz), whereas for Port2 it is 65% (4.89–9.61 GHz). The isolation between the two ports is better than 20 dB for the desired frequency band. The antenna exhibits broadside radiation with cross polar level below 15 dB. The peak gain of antenna varies from 3.34 to 7.40 dBi at Port1 and from 2.34 to 7.9 dBi at Port2. Moreover, the various MIMO performance metrics including envelope correlation coefficient (ECC), diversity gain, channel capacity loss and total active reflection coefficient are investigated. The ECC is less than 0.01 and capacity loss is under 0.5 bps/Hz throughout the operating bandwidth. The results confirm that the antenna offers effective MIMO/diversity performance. The proposed antenna can be suitable for WLAN and upper ultra wideband frequency band applications.
Strain Modulation Effects on the Topological Properties of a Chiral p-Wave Superconductor

We present a study of strain modulation effects on electronic structures of a two-dimensional single-band chiral p-wave superconductor within the BCS mean-field scheme. We employ a lattice model and numerically solve the corresponding Bogolyubov-de Gennes equations. Assuming that strain modulation only modifies hopping amplitudes, we observe the emergence of local spontaneous supercurrents that are attributed to the non-trivial band topology of the system. We also report that strain modulation could induce the formation of topologically distinct domains within a single system, which is captured by spectral functions and local Chern markers.
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks

Graph Neural Networks (GNNs) have received extensive research attention for their promising performance in graph machine learning. Despite their extraordinary predictive accuracy, existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs, rendering these models vulnerable to graph structural attacks and with limited capacity in generalizing to graphs of varied homophily levels. Although many methods have been proposed to improve the robustness of GNN models, most of these techniques are restricted to the spatial domain and employ complicated defense mechanisms, such as learning new graph structures or calculating edge attentions. In this paper, we study the problem of designing simple and robust GNN models in the spectral domain. We propose EvenNet, a spectral GNN corresponding to an even-polynomial graph filter. Based on our theoretical analysis in both spatial and spectral domains, we demonstrate that EvenNet outperforms full-order models in generalizing across homophilic and heterophilic graphs, implying that ignoring odd-hop neighbors improves the robustness of GNNs. We conduct experiments on both synthetic and real-world datasets to demonstrate the effectiveness of EvenNet. Notably, EvenNet outperforms existing defense models against structural attacks without introducing additional computational costs and maintains competitiveness in traditional node classification tasks on homophilic and heterophilic graphs.
IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022

Human intelligence has the remarkable ability to adapt to new tasks and environments quickly. Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions. To facilitate research in this direction, we propose IGLU: Interactive Grounded Language Understanding in a Collaborative Environment. The primary goal of the competition is to approach the problem of how to develop interactive embodied agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment. Understanding the complexity of the challenge, we split it into sub-tasks to make it feasible for participants. This research challenge is naturally related, but not limited, to two fields of study that are highly relevant to the NeurIPS community: Natural Language Understanding and Generation (NLU/G) and Reinforcement Learning (RL). Therefore, the suggested challenge can bring two communities together to approach one of the crucial challenges in AI. Another critical aspect of the challenge is the dedication to perform a human-in-the-loop evaluation as a final evaluation for the agents developed by contestants.
Double Deep Q Networks for Sensor Management in Space Situational Awareness

We present a novel Double Deep Q Network (DDQN) application to a sensor management problem in space situational awareness (SSA). Frequent launches of satellites into Earth orbit pose a significant sensor management challenge, whereby a limited number of sensors are required to detect and track an increasing number of objects. In this paper, we demonstrate the use of reinforcement learning to develop a sensor management policy for SSA. We simulate a controllable Earth-based telescope, which is trained to maximise the number of satellites tracked using an extended Kalman filter. The estimated state covariance matrices for satellites observed under the DDQN policy are greatly reduced compared to those generated by an alternate (random) policy. This work provides the basis for further advancements and motivates the use of reinforcement learning for SSA.
ANISE: Assembly-based Neural Implicit Surface rEconstruction

We present ANISE, a method that reconstructs a 3D shape from partial observations (images or sparse point clouds) using a part-aware neural implicit shape representation. The shape is formulated as an assembly of neural implicit functions, each representing a different part instance. In contrast to previous approaches, the prediction of this representation proceeds in a coarse-to-fine manner. Our model first reconstructs a structural arrangement of the shape in the form of geometric transformations of its part instances. Conditioned on them, the model predicts part latent codes encoding their surface geometry. Reconstructions can be obtained in two ways: (i) by directly decoding the part latent codes to part implicit functions, then combining them into the final shape; or (ii) by using part latents to retrieve similar part instances in a part database and assembling them in a single shape. We demonstrate that, when performing reconstruction by decoding part representations into implicit functions, our method achieves state-of-the-art part-aware reconstruction results from both images and sparse point clouds. When reconstructing shapes by assembling parts retrieved from a dataset, our approach significantly outperforms traditional shape retrieval methods even when significantly restricting the database size. We present our results in well-known sparse point cloud reconstruction and single-view reconstruction benchmarks.
Ranking Binary Unlabelled Necklaces in Polynomial Time

Unlabelled Necklaces are an equivalence class of cyclic words under both the rotation (cyclic shift) and the relabelling operations. The relabelling of a word is a bijective mapping from the alphabet to itself. The main result of the paper is the ﬁrst polynomial-time algorithm for ranking unlabelled necklaces of a binary alphabet. The time-complexity of the algorithm is O ( n 6 log 2 n ), where n is the length of the considered necklaces. The key part of the algorithm is to compute the rank of any word with respect to the set of unlabelled necklaces by ﬁnding three other ranks: the rank over all necklaces, the rank over symmetric unlabelled necklaces, and the rank over necklaces with an enclosing labelling. The last two concepts are introduced in this paper.
On EDA-Driven Learning for SAT Solving

We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively weak supervision, we propose applying the knowledge of the well-developed electronic design automation (EDA) field for SAT solving. Specifically, we first resort to logic synthesis algorithms to pre-process SAT instances into optimized and-inverter graphs (AIGs). By doing so, the distribution diversity among various SAT instances can be dramatically reduced, which facilitates improving the generalization capability of the learned model. Next, we regard the distribution of SAT solutions being a product of conditional Bernoulli distributions. Based on this observation, we approximate the SAT solving procedure with a conditional generative model, leveraging a novel directed acyclic graph neural network (DAGNN) with two polarity prototypes for conditional SAT modeling. To effectively train the generative model, with the help of logic simulation tools, we obtain the probabilities of nodes in the AIG being logic ‘1’ as rich supervision. We conduct comprehensive experiments on various SAT problems. Our results show that, DeepSAT achieves significant accuracy improvements over state-of-the-art learning-based SAT solutions, especially when generalized to SAT instances that are relatively large or with diverse distributions.
BASALISC: Flexible Asynchronous Hardware Accelerator for Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) allows for secure computation on encrypted data. It enables a variety of theoretical and practical applications, but is still several orders of magnitudes too slow to be practical. We present BASALISC, an architecture family of FHE hardware accelerators that aims to substantially accelerate FHE computations in the cloud. BASALISC implements Brakerski, Gentry, and Vaikuntanathan ’s (BGV) scheme and supports a range of parameter sets. In contrast to many prior studies, we directly support and implement BGV bootstrapping – the noise removal capability necessary to support arbitrary-depth computation. BASALISC exploits data representation in residue number systems and number-theoretic transforms to realize massive FHE parallelism. We propose a new generalized version of bootstrapping that can be implemented with optimized Montgomery multipliers that cost 46% less in silicon area and 40% less in power consumption versus traditional approaches. BASALISC is a Reduced Instruction Set Computing (RISC) architecture with a four-layer memory hierarchy, including a two-dimensional conflict-free inner memory layer that enables 32 Tb/s radix-256 number-theoretic transform (NTT) computations without pipeline stalls. Our conflict-resolution data permutation hardware is re-used to compute BGV automorphisms without additional hardware and without through-put penalty. BASALISC additionally includes a custom multiply-accumulate unit familiar in Digital Signal Processing (DSP) architectures, with which we accelerate tight BGV key switching loops. The BASALISC computation units and inner memory layers are designed in asynchronous logic, allowing them to run at different speeds to optimize each function. BASALISC is designed for Application-Specific Integrated Circuit (ASIC) implementation with
Group-invariant max filtering

Given a real inner product space $V$ and a group $G$ of linear isometries, we construct a family of $G$-invariant real-valued functions on $V$ that we call max filters. In the case where $V=\mathbb{R}^d$ and $G$ is finite, a suitable max filter bank separates orbits, and is even bilipschitz in the quotient metric. In the case where $V=L^2(\mathbb{R}^d)$ and $G$ is the group of translation operators, a max filter exhibits stability to diffeomorphic distortion like that of the scattering transform introduced by Mallat. We establish that max filters are well suited for various classification tasks, both in theory and in practice.
Quark: Controllable Text Generation with Reinforced Unlearning

Large-scale language models often learn behaviors that are misaligned with user expectations. Generated text may contain offensive or toxic language, contain significant repetition, or be of a different sentiment than desired by the user. We consider the task of unlearning these misalignments by fine-tuning the language model on signals of what not to do. We introduce Quantized Reward Konditioning (Quark), an algorithm for optimizing a reward function that quantifies an (un)wanted property, while not straying too far from the original model. Quark alternates between (i) collecting samples with the current language model, (ii) sorting them into quantiles based on reward, with each quantile identified by a reward token prepended to the language model's input, and (iii) using a standard language modeling loss on samples from each quantile conditioned on its reward token, while remaining nearby the original language model via a KL-divergence penalty. By conditioning on a high-reward token at generation time, the model generates text that exhibits less of the unwanted property. For unlearning toxicity, negative sentiment, and repetition, our experiments show that Quark outperforms both strong baselines and state-of-the-art reinforcement learning methods like PPO (Schulman et al. 2017), while relying only on standard language modeling primitives.
Guided Exploration of Data Summaries

Data summarization is the process of producing interpretable and representative subsets of an input dataset. It is usually performed following a one-shot process with the purpose of finding the best summary. A useful summary contains k individually uniform sets that are collectively diverse to be representative. Uniformity addresses interpretability and diversity addresses representativity. Finding such as summary is a difficult task when data is highly diverse and large. We examine the applicability of Exploratory Data Analysis (EDA) to data summarization and formalize Eda4Sum, the problem of guided exploration of data summaries that seeks to sequentially produce connected summaries with the goal of maximizing their cumulative utility. Eda4Sum generalizes one-shot summarization. We propose to solve it with one of two approaches: (i) Top1Sum that chooses the most useful summary at each step; (ii) RLSum that trains a policy with Deep Reinforcement Learning that rewards an agent for finding a diverse and new collection of uniform sets at each step. We compare these approaches with one-shot summarization and top-performing EDA solutions. We run extensive experiments on three large datasets. Our results demonstrate the superiority of our approaches for summarizing very large data, and the need to provide guidance to domain experts.
Denial-of-Service Attack on Object Detection Model Using Universal Adversarial Perturbation

Adversarial attacks against deep learning-based object detectors have been studied extensively in the past few years. The proposed attacks aimed solely at compromising the models’ integrity (i.e., trustworthiness of the model’s prediction), while adversarial attacks targeting the models’ availability, a critical aspect in safety-critical domains such as autonomous driving, have not been explored by the machine learning research community. In this paper, we propose NMS-Sponge , a novel approach that negatively affects the decision latency of YOLO, a state-of-the-art object detector, and compromises the model’s availability by applying a universal adversarial perturbation (UAP). In our experiments, we demonstrate that the proposed UAP is able to increase the processing time of individual frames by adding “phantom" objects while preserving the detection of the original objects.