Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust Backpropagation-Free Framework for Images

While current deep learning algorithms have been successful for a wide variety of artificial intelligence (AI) tasks, including those involving structured image data, they present deep neurophysiological conceptual issues due to their reliance on the gradients computed by backpropagation of errors (backprop) to obtain synaptic weight adjustments; hence are biologically implausible. We present a more biologically plausible approach, the error-kernel driven activation alignment (EKDAA) algorithm, to train convolution neural networks (CNNs) using locally derived error transmission kernels and error maps. We demonstrate the efficacy of EKDAA by performing the task of visual-recognition on the Fashion MNIST, CIFAR-10 and SVHN benchmarks as well as conducting blackbox robustness tests on adversarial examples derived from these datasets. Furthermore, we also present results for a CNN trained using a non-differentiable activation function. All recognition results nearly matches that of backprop and exhibit greater adversarial robustness compared to backprop.
SealID: Saimaa Ringed Seal Re-Identification Dataset

Wildlife camera traps and crowd-sourced image material provide novel possibilities to monitor endangered animal species. The massive data volumes call for automatic methods to solve various tasks related to population monitoring, such as the re-identification of individual animals. The Saimaa ringed seal (Pusa hispida saimensis) is an endangered subspecies only found in Lake Saimaa, Finland, and is one of the few existing freshwater seal species. Ringed seals have permanent pelage patterns that are unique to each individual and that can be used for the identification of individuals. A large variation in poses, further exacerbated by the deformable nature of seals, together with varying appearance and low contrast between the ring pattern and the rest of the pelage makes the Saimaa ringed seal re-identification task very challenging, providing a good benchmark by which to evaluate state-of-the-art re-identification methods. Therefore, we make our Saimaa ringed seal image (SealID) dataset (N = 57) publicly available for research purposes. In this paper, the dataset is described, the evaluation protocol for re-identification methods is proposed, and the results for two baseline methods—HotSpotter and NORPPA—are provided. The SealID dataset has been made publicly available.
Symmetry as a Representation of Intuitive Geometry?

Recognition of geometrical patterns seems to be an important aspect of human intelligence. Geometric pattern recognition is used in many intelligence tests, including Dehaene’s odd-one-out test of Core Geometry (CG)) based on intuitive geometrical concepts (Dehaene et al., 2006). Earlier work has developed a symmetry-based cognitive model of Dehaene’s test and demonstrated performance comparable to that of humans. In this work, we further investigate the role of symmetry in geometrical intuition and build a cognitive model for the 2-Alternative Forced Choice (2-AFC) variation of the CG test (Marupudi & Varma 2021). In contrast to Dehaene’s test, 2-AFC leaves almost no space for cognitive models based on generalization over multiple examples. Our symmetry-based model achieves an accuracy comparable to the human average on the 2-AFC test and appears to capture an essential part of intuitive geometry.
Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities

Search engines and recommendation systems attempt to continually improve the quality of the experience they afford to their users. Refining the ranker that produces the lists displayed in response to user requests is an important component of this process. A common practice is for the service providers to make changes (e.g. new ranking features, different ranking models) and A/B test them on a fraction of their users to establish the value of the change. An alternative approach estimates the effectiveness of the proposed changes offline, utilising previously collected clickthrough data on the old ranker to posit what the user behaviour on ranked lists produced by the new ranker would have been. A majority of offline evaluation approaches invoke the well studied inverse propensity weighting to adjust for biases inherent in logged data. In this paper, we propose the use of parametric estimates for these propensities. Specifically, by leveraging well known learning-to-rank methods as subroutines, we show how accurate offline evaluation can be achieved when the new rankings to be evaluated differ from the logged ones.
Inbetween: Visual Selection in Parametric Design

The act of selection plays a leading role in the design process and in the definition of personal style. This work introduces visual selection catalogs into parametric design environments. A two-fold contribution is presented: (i) guidelines for construction of a minimal-bias visual selection catalog from a parametric space, and (ii) Inbetween, a catalog for a parametric typeface that adheres to the guidelines, allows for font selection from a continuous design space, and enables the investigation of personal style. A user study conducted among graphic designers, revealed self-coherent characteristics in selection patterns, and a high correlation in selection patterns within tasks. These findings suggest that such patterns reflect personal user styles, formalizing the style selection process as traversals of decision trees. Together, our guidelines and catalog aid in making visual selection a key building block in the digital creation process and validate selection processes as a measure of personal style.
Wigner–Smith Time-Delay Matrix for Electromagnetics: Guiding and Periodic Systems With Evanescent Modes

The Wigner–Smith (WS) time-delay matrix relates an electromagnetic system’s scattering matrix and its frequency derivative. Previous work showed that the entries of WS time-delay matrices of systems excited by propagating waves consist of volume integrals of energy-like field quantities. This article introduces a generalized WS relationship that applies to systems excited by mixtures of propagating and evanescent fields. Just like its predecessor, the generalized WS relationship allows for the identification of so-called WS modes that interact with the system with well-defined time delays. Furthermore, a technique is developed to compute the WS time-delay matrix of a composite system from the WS time-delay matrices of its subsystems. Numerical examples demonstrate the usefulness of the generalized WS method when characterizing time delays experienced by fields interacting with guiding and periodic structures that have ports supporting evanescent modes.
Blockchain for the Cybersecurity of Smart City Applications

Cybersecurity is an inherent characteristic that should be addressed before the large deployment of smart city applications. Recently, Blockchain appears as a promising technology to provide several cybersecurity aspects of smart city applications. This paper provides a comprehensive review of the existing blockchain-based solutions for the cybersecurity of the main smart city applications, namely smart healthcare, smart transportation, smart agriculture, supply chain management, smart grid, and smart homes. We describe the existing solutions and we discuss their merits and limits. Moreover, we define the security requirements of each smart city application and we give a mapping of the studied solutions to these defined requirements. Additionally, future directions are given. We believe that the present survey is a good starting point for every researcher in the fields of cybersecurity, blockchain, and smart cities.
Charge Dynamics Electron Microscopy: Nanoscale Imaging of Femtosecond Plasma Dynamics

Understanding and actively controlling the spatiotemporal dynamics of nonequilibrium electron clouds is fundamental for the design of light and electron sources, high-power electronic devices, and plasma-based applications. However, electron clouds evolve in a complex collective fashion on the nanometer and femtosecond scales, producing electromagnetic screening that renders them inaccessible to existing optical probes. Here, we solve the long-standing challenge of characterizing the evolution of electron clouds generated upon irradiation of metallic structures using an ultrafast transmission electron microscope to record the charged plasma dynamics. Our approach to charge dynamics electron microscopy (CDEM) is based on the simultaneous detection of electron-beam acceleration and broadening with nanometer/femtosecond resolution. By combining experimental results with comprehensive microscopic theory, we provide a deep understanding of this highly out-of-equilibrium regime, including previously inaccessible intricate microscopic mechanisms of electron emission, screening by the metal, and collective cloud dynamics. Beyond the present specific demonstration, the here-introduced CDEM technique grants us access to a wide range of nonequilibrium electrodynamic phenomena involving the ultrafast evolution of bound and free charges on the nanoscale.
Targeting dePARylation selectively suppresses DNA repair–defective and PARP inhibitor–resistant malignancies

A novel PARG inhibitor traps DNA repair factors at DNA lesions and selectively kills tumor cells with DNA repair defects. While poly(ADP-ribosyl)ation (PARylation) plays an important role in DNA repair, the role of dePARylation in DNA repair remains elusive. Here, we report that a novel small molecule identified from the NCI database, COH34, specifically inhibits poly(ADP-ribose) glycohydrolase (PARG), the major dePARylation enzyme, with nanomolar potency in vitro and in vivo. COH34 binds to the catalytic domain of PARG, thereby prolonging PARylation at DNA lesions and trapping DNA repair factors. This compound induces lethality in cancer cells with DNA repair defects and exhibits antitumor activity in xenograft mouse cancer models. Moreover, COH34 can sensitize tumor cells with DNA repair defects to other DNA-damaging agents, such as topoisomerase I inhibitors and DNA-alkylating agents, which are widely used in cancer chemotherapy. Notably, COH34 also efficiently kills PARP inhibitor–resistant cancer cells. Together, our study reveals the molecular mechanism of PARG in DNA repair and provides an effective strategy for future cancer therapies.
Learning to Control under Time-Varying Environment

This paper investigates the problem of regret minimization in linear time-varying (LTV) dynamical systems. Due to the simultaneous presence of uncertainty and non-stationarity, designing online control algorithms for unknown LTV systems remains a challenging task. At a cost of NP-hard offline planning, prior works have introduced online convex optimization algorithms, although they suffer from nonparametric rate of regret. In this paper, we propose the first computationally tractable online algorithm with regret guarantees that avoids offline planning over the state linear feedback policies. Our algorithm is based on the optimism in the face of uncertainty (OFU) principle in which we optimistically select the best model in a high confidence region. Our algorithm is then more explorative when compared to previous approaches. To overcome non-stationarity, we propose either a restarting strategy (R-OFU) or a sliding window (SW-OFU) strategy. With proper configuration, our algorithm is attains sublinear regret $O(T^{2/3})$. These algorithms utilize data from the current phase for tracking variations on the system dynamics. We corroborate our theoretical findings with numerical experiments, which highlight the effectiveness of our methods. To the best of our knowledge, our study establishes the first model-based online algorithm with regret guarantees under LTV dynamical systems.
Active Bayesian Causal Inference

Causal discovery and causal reasoning are classically treated as separate and consecutive tasks: one first infers the causal graph, and then uses it to estimate causal effects of interventions. However, such a two-stage approach is uneconomical, especially in terms of actively collected interventional data, since the causal query of interest may not require a fully-specified causal model. From a Bayesian perspective, it is also unnatural, since a causal query (e.g., the causal graph or some causal effect) can be viewed as a latent quantity subject to posterior inference -- other unobserved quantities that are not of direct interest (e.g., the full causal model) ought to be marginalized out in this process and contribute to our epistemic uncertainty. In this work, we propose Active Bayesian Causal Inference (ABCI), a fully-Bayesian active learning framework for integrated causal discovery and reasoning, which jointly infers a posterior over causal models and queries of interest. In our approach to ABCI, we focus on the class of causally-sufficient, nonlinear additive noise models, which we model using Gaussian processes. We sequentially design experiments that are maximally informative about our target causal query, collect the corresponding interventional data, and update our beliefs to choose the next experiment. Through simulations, we demonstrate that our approach is more data-efficient than several baselines that only focus on learning the full causal graph. This allows us to accurately learn downstream causal queries from fewer samples while providing well-calibrated uncertainty estimates for the quantities of interest.
The dipole of the astrophysical gravitational-wave background

One of the main pillars of the ΛCDM model is the Cosmological Principle, which states that our Universe is statistically isotropic and homogeneous on large scales. Here we test this hypothesis using the Astrophysical Gravitational Wave Background (AGWB) expected to be measured by the Einstein Telescope-Cosmic Explorer network; in particular we perform a numerical computation of the AGWB dipole, evaluating the intrinsic contribution due to clustering and the kinematic effect induced by the observer motion. We apply a component separation technique in the GW context to disentangle the kinematic dipole, the intrinsic dipole and the shot noise (SN), based on the observation of the AGWB at different frequencies. We show how this technique can also be implemented in matched-filtering to minimize the covariance which accounts for both instrumental noise and SN. Since GW detectors are essentially full-sky, we expect that this powerful tool can help in testing the isotropy of our Universe in the next future.
On the Advance of Making Language Models Better Reasoners

Large language models such as GPT-3 and PaLM have shown remarkable performance in few-shot learning. However, they still struggle with reasoning tasks such as the arithmetic benchmark GSM8K. Recent advances deliberately guide the language model to generate a chain of reasoning steps before producing the ﬁnal answer, successfully boosting the GSM8K benchmark from 17 . 9% to 58 . 1% in terms of problem solving rate. In this paper, we propose a new approach, D I V E RS E (Diverse Veriﬁer on Reasoning Step), to further advance their reasoning capability. D I V E RS E ﬁrst explores different prompts to enhance the diversity in reasoning paths. Second, D I V E RS E introduces a veriﬁer to distinguish good answers from bad answers for a better weighted voting. Finally, D I V E RS E veriﬁes the correctness of each single step rather than all the steps in a whole. We conduct extensive experiments using the latest language model code-davinci-002 and demonstrate that D I - V E RS E can achieve new state-of-the-art performance on six out of eight reasoning benchmarks (e.g., GSM8K 74 . 4% → 83 . 2% ), out-performing the PaLM model with 540B parameters.
The spike gating flow: A hierarchical structure-based spiking neural network for online gesture recognition

Action recognition is an exciting research avenue for artificial intelligence since it may be a game changer in emerging industrial fields such as robotic visions and automobiles. However, current deep learning (DL) faces major challenges for such applications because of the huge computational cost and inefficient learning. Hence, we developed a novel brain-inspired spiking neural network (SNN) based system titled spiking gating flow (SGF) for online action learning. The developed system consists of multiple SGF units which are assembled in a hierarchical manner. A single SGF unit contains three layers: a feature extraction layer, an event-driven layer, and a histogram-based training layer. To demonstrate the capability of the developed system, we employed a standard dynamic vision sensor (DVS) gesture classification as a benchmark. The results indicated that we can achieve 87.5% of accuracy which is comparable with DL, but at a smaller training/inference data number ratio of 1.5:1. Only a single training epoch is required during the learning process. Meanwhile, to the best of our knowledge, this is the highest accuracy among the non-backpropagation based SNNs. Finally, we conclude the few-shot learning (FSL) paradigm of the developed network: 1) a hierarchical structure-based network design involves prior human knowledge; 2) SNNs for content-based global dynamic feature detection.
Actuarial Applications of Natural Language Processing Using Transformers: Case Studies for Using Text Features in an Actuarial Context

This paper demonstrates workflows to incorporate text data into actuarial classification and regression tasks. The main focus is on methods employing transformer-based models. A dataset of car accident descriptions with an average length of 400 words, available in English and German, and a dataset with short property insurance claims descriptions, are used to demonstrate these techniques. The case studies tackle challenges related to a multilingual setting and long input sequences. They also show ways to interpret model output and to assess and improve model performance, by fine-tuning the models to the domain of application or to a specific prediction task. Finally, the paper provides practical approaches to handle classification tasks in situations with no or only few labelled data. The results achieved by using the language-understanding skills of off-the-shelf natural language processing (NLP) models with only minimal pre-processing and fine-tuning clearly demonstrate the power of transfer learning for practical applications.
Bayesian and Frequentist Inference for Synthetic Controls

The synthetic control method has become a widely popular tool to estimate causal effects with observational data. Despite this, inference for synthetic control methods remains challenging. Often, inferential results rely on linear factor model data generating processes. In this paper, we characterize the conditions on the factor model primitives (the factor loadings) for which the statistical risk minimizers are synthetic controls (in the simplex). Then, we propose a Bayesian alternative to the synthetic control method that preserves the main features of the standard method and provides a new way of doing valid inference. We explore a Bernstein-von Mises style result to link our Bayesian inference to the frequentist inference. For linear factor model frameworks we show that a maximum likelihood estimator (MLE) of the synthetic control weights can consistently estimate the predictive function of the potential outcomes for the treated unit and that our Bayes estimator is asymptotically close to the MLE in the total variation sense. Through simulations, we show that there is convergence between the Bayes and frequentist approach even in sparse settings. Finally, we apply the method to re-visit the study of the economic costs of the German re-unification and the Catalan secession movement. The Bayesian synthetic control method is available in the bsynth R-package.
Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon

With the development of online travel services, it has great application prospects to timely mine users' evaluation emotions for travel services and use them as indicators to guide the improvement of online travel service quality. In this paper, we study the text sentiment classification of online travel reviews based on social media online comments and propose the SCCL model based on capsule network and sentiment lexicon. SCCL model aims at the lack of consideration of local features and emotional semantic features of the text in the language model that can efficiently extract text context features like BERT and GRU. Then make the following improvements to their shortcomings. On the one hand, based on BERT-BiGRU, the capsule network is introduced to extract local features while retaining good context features. On the other hand, the sentiment lexicon is introduced to extract the emotional sequence of the text to provide richer emotional semantic features for the model. To enhance the universality of the sentiment lexicon, the improved SO-PMI algorithm based on TF-IDF is used to expand the lexicon, so that the lexicon can also perform well in the field of online travel reviews.
An information upper bound for probability sensitivity

Uncertain input of a mathematical model induces uncertainties in the output and probabilistic sensitivity analysis identifies the influential inputs to guide decision-making. Of practical concern is the probability that the output would, or would not, exceed a threshold, and the probability sensitivity depends on this threshold which is often uncertain. The Fisher information and the Kullback-Leibler divergence have been recently proposed in the literature as threshold-independent sensitivity metrics. We present mathematical proof that the information-theoretical metrics provide an upper bound for the probability sensitivity. The proof is elementary, relying only on a special version of the Cauchy-Schwarz inequality called Titu’s lemma. Despite various inequalities exist for probabilities, little is known of probability sensitivity bounds and the one proposed here is new to the present authors’ knowledge. The probability sensitivity bound is extended, analytically and with numerical examples, to the Fisher information of both the input and output. It thus provides a solid mathematical basis for decision-making based on probabilistic sensitivity metrics.