Miracle-dz/UniFAR-FTUs huggingface dataset:
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?

Modern deep neural networks for classification usually jointly learn a backbone for representation and a linear classifier to output the logit of each class. A recent study has shown a phenomenon called neural collapse that the within-class means of features and the classifier vectors converge to the vertices of a simplex equiangular tight frame (ETF) at the terminal phase of training on a balanced dataset. Since the ETF geometric structure maximally separates the pair-wise angles of all classes in the classifier, it is natural to raise the question, why do we spend an effort to learn a classifier when we know its optimal geometric structure? In this paper, we study the potential of learning a neural network for classification with the classifier randomly initialized as an ETF and fixed during training. Our analytical work based on the layer-peeled model indicates that the feature learning with a fixed ETF classifier naturally leads to the neural collapse state even when the dataset is imbalanced among classes. We further show that in this case the cross entropy (CE) loss is not necessary and can be replaced by a simple squared loss that shares the same global optimality but enjoys a better convergence property. Our experimental results show that our method is able to bring significant improvements with faster convergence on multiple imbalanced datasets.
Neural Implicit Surface Evolution

This work investigates the use of smooth neural networks for modeling dynamic variations of implicit surfaces under the level set equation (LSE). For this, it extends the representation of neural implicit surfaces to the space-time ℝ3 × ℝ, which opens up mechanisms for continuous geometric transformations. Examples include evolving an initial surface towards general vector fields, smoothing and sharpening using the mean curvature equation, and interpolations of initial conditions.The network training considers two constraints. A data term is responsible for fitting the initial condition to the corresponding time instant, usually ℝ3× {0}. Then, a LSE term forces the network to approximate the underlying geometric evolution given by the LSE, without any supervision. The network can also be initialized based on previously trained initial conditions, resulting in faster convergence compared to the standard approach.
Step out of KG: Knowledge Graph Completion via Knowledgeable Retrieval and Reading Comprehension

Knowledge graphs, as the cornerstone of many AI applications, usually face serious incompleteness problems. In recent years, there have been many efforts to study automatic knowledge graph completion (KGC), most of which use existing knowledge to infer new knowledge. However, in our experiments, we find that not all relations can be obtained by inference, which constrains the performance of existing models. To alleviate this problem, we propose a new model based on information retrieval and reading comprehension, namely IR4KGC. Specifically, we pre-train a knowledge-based information retrieval module that can retrieve documents related to the triples to be completed. Then, the retrieved documents are handed over to the reading comprehension module to generate the predicted answers. In experiments, we find that our model can well solve relations that cannot be inferred from existing knowledge, and achieve good results on KGC datasets.
Interactive Language: Talking to Robots in Real Time

We present a framework for building interactive, real-time, natural language-instructable robots in the real world, and we open source related assets (dataset, environment, benchmark, and policies). Trained with behavioral cloning on a dataset of hundreds of thousands of language-annotated trajectories, a produced policy can proficiently execute an order of magnitude more commands than previous works: specifically we estimate a 93.5% success rate on a set of 87,000 unique natural language strings specifying raw end-to-end visuo-linguo-motor skills in the real world. We find that the same policy is capable of being guided by a human via real-time language to address a wide range of precise long-horizon rearrangement goals, e.g."make a smiley face out of blocks". The dataset we release comprises nearly 600,000 language-labeled trajectories, an order of magnitude larger than prior available datasets. We hope the demonstrated results and associated assets enable further advancement of helpful, capable, natural-language-interactable robots. See videos at https://interactive-language.github. io.
BiaScope: Visual Unfairness Diagnosis for Graph Embeddings

The issue of bias (i.e., systematic unfairness) in machine learning models has recently attracted the attention of both researchers and practitioners. For the graph mining community in particular, an important goal toward algorithmic fairness is to detect and mitigate bias incorporated into graph embeddings since they are commonly used in human-centered applications, e.g., social-media recommendations. However, simple analytical methods for detecting bias typically involve aggregate statistics which do not reveal the sources of unfairness. Instead, visual methods can provide a holistic fairness characterization of graph embeddings and help uncover the causes of observed bias. In this work, we present BIASCOPE, an interactive visualization tool that supports end-to-end visual unfairness diagnosis for graph embeddings. The tool is the product of a design study in collaboration with domain experts. It allows the user to (i) visually compare two embeddings with respect to fairness, (ii) locate nodes or graph communities that are unfairly embedded, and (iii) understand the source of bias by interactively linking the relevant embedding subspace with the corresponding graph topology. Experts’ feedback confirms that our tool is effective at detecting and diagnosing unfairness. Thus, we envision our tool both as a companion for researchers in designing their algorithms as well as a guide for practitioners who use off-the-shelf graph embeddings.
S CALABLE AND P RIVACY - ENHANCED G RAPH G ENER - ATIVE M ODEL FOR G RAPH N EURAL N ETWORKS

As the field of Graph Neural Networks (GNN) continues to grow, it experiences a corresponding increase in the need for large, real-world datasets to train and test new GNN models on challenging, realistic problems. Unfortunately, such graph datasets are often generated from online, highly privacy-restricted ecosystems, which makes research and development on these datasets hard, if not impossible. This greatly reduces the amount of benchmark graphs available to researchers, causing the field to rely only on a handful of publicly-available datasets. To address this dilemma, we introduce a novel graph generative model, Computation Graph Transformer (CGT) that can learn and reproduce the distribution of real-world graphs in a privacy-enhanced way. Our proposed model (1) generates effective benchmark graphs on which GNNs show similar task performance as on the source graphs, (2) scales to process large-scale real-world graphs, (3) guarantees privacy for end-users. Extensive experiments across a vast body of graph generative models show that only our model can successfully generate privacy-controlled, synthetic substitutes of large-scale real-world graphs that can be effectively used to evaluate GNN models.
Better Smatch = Better Parser? AMR evaluation is not so simple anymore

Recently, astonishing advances have been observed in AMR parsing, as measured by the structural S MATCH metric. In fact, today’s systems achieve performance levels that seem to surpass estimates of human inter annotator agreement (IAA). Therefore, it is unclear how well S MATCH (still) relates to human estimates of parse quality, as in this situation potentially ﬁne-grained errors of similar weight may impact the AMR’s meaning to different degrees. We conduct an analysis of two popular and strong AMR parsers that – according to S MATCH – reach quality levels on par with human IAA, and assess how human quality rat-ings relate to S MATCH and other AMR metrics. Our main ﬁndings are: i) While high S MATCH scores indicate otherwise, we ﬁnd that AMR parsing is far from being solved : we frequently ﬁnd structurally small, but semanti-cally unacceptable errors that substantially distort sentence meaning. ii) Considering high-performance parsers, better S MATCH scores may not necessarily indicate consistently better parsing quality . To obtain a meaning-ful and comprehensive assessment of quality differences of parse(r)s, we recommend aug-menting evaluations with macro statistics, use of additional metrics, and more human analysis.
Singleton deformation of higher-spin theory and the phase structure of the three-dimensional O(N) vector model

We consider a singleton deformation of the AdS4 higher-spin theory dual to the three-dimensional O(N) vector model. The singleton couples to the higher-spin multiplet only through a marginal boundary interaction. We argue that the eect of such a deformation is to shift N ! N + 1 in both sides of the holographic correspondance and we show how the gap equations of the three-dimensional O(N) vector model arise from the higher-spin theory. The singleton deformation breaks higher-spin symmetry and gives rise to the well-known 1=N anomalous dimensions of the boundary theory.
Experimental and Computational Investigation of a Fractal Grid Wake

Fractal grids generate turbulence by exciting many length scales of different sizes simultaneously rather than using the nonlinear cascade mechanism to obtain multi-scale structures, as it is the case for regular grids. The interest in these grids has been further building up since the surprising findings stemming from the experimental and computational studies conducted on these grids. This work presents experimental wind tunnel and computational fluid dynamics (CFD) studies of the turbulent flow generated by a space-filling fractal square grid. The experimental work includes Particle Image Velocimetry (PIV) and hot-wire measurements. In addition, Delayed Detached Eddy Simulations (DDES) with a Spalart-Allmaras background turbulence model are conducted using the open-source package OpenFOAM. This is the first time DDES simulations are used to simulate and characterize the turbulent flow generated by a fractal grid. Finally, this article reports on the extensive statistical study and the direct comparison between the experimentally and numerically acquired time series to investigate and compare one-point- and two-point statistics. Our goal is to validate our computational results and provide enhanced insight into the complexity of the multi-scale generation of turbulence using a fractal grid with a low number of fractal iterations. In particular, we investigate the different turbulent structures and their complex interaction in the near-grid region or the production regime of the fractal grid flow.
Multiwavelength Observations of the Obscuring Wind in the Radio-quiet Quasar MR 2251-178

Obscuring winds driven away from active supermassive black holes are rarely seen due to their transient nature. They have been observed with multiwavelength observations in a few Seyfert 1 galaxies and one broad absorption line radio-quiet quasar so far. An X-ray obscuration event in MR 2251-178 was caught in late-2020, which triggered multiwavelength (near-IR (NIR) to X-ray) observations targeting this radio-quiet quasar. In the X-ray band, the obscurer leads to a flux drop in the soft X-ray band from late-2020 to early-2021. X-ray obscuration events might have a quasi-period of two decades considering earlier events in 1980 and 1996. In the UV band, a forest of weak blueshifted absorption features emerged in the blue wing of Lyα λ1216 in late-2020. Our XMM-Newton, NuSTAR, and Hubble Space Telescope/COS observations are obtained simultaneously; hence, the transient X-ray obscuration event is expected to account for the UV outflow, although they are not necessarily caused by the same part of the wind. Both blueshifted and redshifted absorption features were found for He i λ10830, but no previous NIR spectra are available for comparison. The X-ray observational features of MR 2251-178 shared similarities with some other type 1 active galactic nuclei (AGNs) with obscuring wind. However, observational features in the UV to NIR bands are distinctly different from those seen in other AGN with obscuring winds. A general understanding of the observational variety and the nature of obscuring wind is still lacking.
Five Lectures on Cluster Theory

In this paper, we will present the author’s interpretation and embellishment of ﬁve lectures on cluster theory given by Kiyoshi Igusa during the Spring semester of 2022 at Brandeis University. They are meant to be used as an introduction to cluster theory from a representation-theoretic point of view.
SilverAlign: MT-Based Silver Data Algorithm For Evaluating Word Alignment

Word alignments are essential for a variety of NLP tasks. Therefore, choosing the best approaches for their creation is crucial. However, the scarce availability of gold evaluation data makes the choice difficult. We propose SilverAlign, a new method to automatically create silver data for the evaluation of word aligners by exploiting machine translation and minimal pairs. We show that performance on our silver data correlates well with gold benchmarks for 9 language pairs, making our approach a valid resource for evaluation of different domains and languages when gold data are not available. This addresses the important scenario of missing gold data alignments for low-resource languages.
Improved Data Augmentation for Translation Suggestion

Translation suggestion (TS) models are used to automatically provide alternative suggestions for incorrect spans in sentences generated by machine translation. This paper introduces the system used in our submission to the WMT’22 Translation Suggestion shared task. Our system is based on the ensemble of different translation architectures, including Transformer, SA-Transformer, and DynamicConv. We use three strategies to construct synthetic data from parallel corpora to compensate for the lack of supervised data. In addition, we introduce a multi-phase pre-training strategy, adding an additional pre-training phase with in-domain data. We rank second and third on the English-German and English-Chinese bidirectional tasks, respectively.
Implications of using the clinical target distribution as voxel-weights in radiation therapy optimization

Objective. Delineating and planning with respect to regions suspected to contain microscopic tumor cells is an inherently uncertain task in radiotherapy. The recently proposed clinical target distribution (CTD) is an alternative to the conventional clinical target volume (CTV), with initial promise. Previously, using the CTD in planning has primarily been evaluated in comparison to a conventionally defined CTV. We propose to compare the CTD approach against CTV margins of various sizes, dependent on the threshold at which the tumor infiltration probability is considered relevant. Approach. First, a theoretical framework is presented, concerned with optimizing the trade-off between the probability of sufficient target coverage and the penalties associated with high dose. From this framework we derive conventional CTV-based planning and contrast it with the CTD approach. The approaches are contextualized further by comparison with established methods for managing geometric uncertainties. Second, for both one- and three-dimensional phantoms, we compare a set of CTD plans created by varying the target objective function weight against a set of plans created by varying both the target weight and the CTV margin size. Main results. The results show that CTD-based planning gives slightly inefficient trade-offs between the evaluation criteria for a case in which near-minimum target dose is the highest priority. However, in a case when sparing a proximal organ at risk is critical, the CTD is better at maintaining sufficiently high dose toward the center of the target. Significance. We conclude that CTD-based planning is a computationally efficient method for planning with respect to delineation uncertainties, but that the inevitable effects on the dose distribution should not be disregarded.
Monte-Carlo Estimation of CoVaR

CoVaR is one of the most important measures of ﬁnancial systemic risks. It is deﬁned as the risk of a ﬁnancial portfolio conditional on another ﬁnancial portfolio being at risk. In this paper we ﬁrst develop a Monte-Carlo simulation-based batching estimator of CoVaR and study its consistency and asymptotic normality. We show that the optimal rate of convergence of the batching estimator is n − 1 / 3 , where n is the sample size. We then develop an importance-sampling inspired estimator under the delta-gamma approximations to the portfolio losses, and we show that the rate of convergence of the estimator is n − 1 / 2 . Numerical experiments support our theoretical ﬁndings and show that both estimators work well.
Modeling the impact of rainfall and temperature on sterile insect control strategies in a tropical environment

The sterile insect technique (SIT) is a biological control technique that can be used either to eliminate or decay a wild mosquito population under a given threshold to reduce the nuisance or the epidemiological risk. In this work, we propose a model using a differential system that takes into account the variations of rainfall and temperature over time and study their impacts on sterile males releases strategies. Our model is as simple as possible to avoid complexity while being able to capture the temporal variations of an Aedes albopictus mosquito population in a domain treated by SIT, located in R{\'e}union island. The main objective is to determine what period of the year is the most suitable to start a SIT control to minimize the duration of massive releases and the amount of sterile males to release, either to reduce the mosquito nuisance, or to reduce the epidemiological risk. Since sterilization is not 100% efficient, we also study the impact of different levels of residual fertility within the released sterile male population. Our study shows that rainfall plays a major role in the dynamics of the mosquito and the SIT control, that the best period to start a massive SIT treatment lasts from July to December, that residual fertility has to be as small as possible, and that increasing the size of the releases is not always necessarily interesting. We also highlight the importance of combining SIT with mechanical control, i.e. the removal of breeding sites, in particular when the initial mosquito population is large.
Pontryagin Duality for Modules over Compact Discrete Valuation Rings

We establish an analogue of Pontryagin duality for modules over compact discrete valuation rings $R$. Namely, we define the dual of a topological $R$ module to be its continuous $R$-module homomorphisms into $K/R$, the quotient module of the fraction field by its ring of integers. It is established that for locally compact $R$-modules the double dual map is an isomorphism and homeomorphism. Additionally, given a non-topological $R$-module a canonical topology is constructed, uniquely defined so that the double dual map will be injective and continuous. Finally, the functor assigning a module to itself equipped with canonical topology is shown to be fully faithful, allowing one to recontextualize the topological statements in purely algebraic forms.
Quantum dynamical maps and Markovianity

It is known that the time evolution of a subsystem from an initial state to two later times, t1,t2 (t2 > t1), are both completely positive (CP) but it is shown here that in the intermediate times between t1 and t2, in general, it need not be CP. This reveals the key to the Markov (if CP) and nonMarkov (if NCP) avataras of the intermediate dynamics. This is brought out based onA and B dynamical maps – without resorting to Master equation approach. The choice of tensor product form for the global initial state points towards the system-environment interaction dynamics as the sole cause for Markovianity/non-Markovianity. A succinct summary of the results is given in the form of a table.
Emergent scale-free networks

Many complex systems--from social and communication networks to biological networks and the Internet--are thought to exhibit scale-free structure. However, prevailing explanations rely on the constant addition of new nodes, an assumption that fails dramatically in some real-world settings. Here, we propose a model in which nodes are allowed to die, and their connections rearrange under a mixture of preferential and random attachment. With these simple dynamics, we show that networks self-organize towards scale-free structure, with a power-law exponent $\gamma = 1 + \frac{1}{p}$ that depends only on the proportion $p$ of preferential (rather than random) attachment. Applying our model to several real networks, we infer $p$ directly from data, and predict the relationship between network size and degree heterogeneity. Together, these results establish that realistic scale-free structure can emerge naturally in networks of constant size and density, with broad implications for the structure and function of complex systems.
The Mirror-Prox Sliding Method for Non-smooth decentralized saddle-point problems

The saddle-point optimization problems have a lot of practical applications. This paper focuses on such non-smooth problems in decentralized case. This work contains generalization of recently proposed sliding for centralized problem. Through specific penalization method and this sliding we obtain algorithm for non-smooth decentralized saddle-point problems. Note, the proposed method approaches lower bounds both for number of communication rounds and calls of (sub-)gradient per node.
Hate-CLIPper: Multimodal Hateful Meme Classification based on Cross-modal Interaction of CLIP Features

Hateful memes are a growing menace on social media. While the image and its corresponding text in a meme are related, they do not necessarily convey the same meaning when viewed individually. Hence, detecting hateful memes requires careful consideration of both visual and textual information. Multimodal pre-training can be beneficial for this task because it effectively captures the relationship between the image and the text by representing them in a similar feature space. Furthermore, it is essential to model the interactions between the image and text features through intermediate fusion. Most existing methods either employ multimodal pre-training or intermediate fusion, but not both. In this work, we propose the Hate-CLIPper architecture, which explicitly models the cross-modal interactions between the image and text representations obtained using Contrastive Language-Image Pre-training (CLIP) encoders via a feature interaction matrix (FIM). A simple classifier based on the FIM representation is able to achieve state-of-the-art performance on the Hateful Memes Challenge (HMC) dataset with an AUROC of 85.8, which even surpasses the human performance of 82.65. Experiments on other meme datasets such as Propaganda Memes and TamilMemes also demonstrate the generalizability of the proposed approach. Finally, we analyze the interpretability of the FIM representation and show that cross-modal interactions can indeed facilitate the learning of meaningful concepts. The code for this work is available at https://github.com/gokulkarthik/hateclipper