Miracle-dz/UniFAR-FTUs huggingface dataset:
Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset

Automated red blood cell classification on blood smear images helps hematologist to analyze RBC lab results in less time and cost. Overlapping cells can cause incorrect predicted results that have to separate into multiple single RBCs before classifying. To classify multiple classes with deep learning, imbalance problems are common in medical imaging because normal samples are always higher than rare disease samples. This paper presents a new method to segment and classify red blood cells from blood smear images, specifically to tackle cell overlapping and data imbalance problems. Focusing on overlapping cell separation, our segmentation process first estimates ellipses to represent red blood cells. The method detects the concave points and then finds the ellipses using directed ellipse fitting. The accuracy is 0.889 on 20 blood smear images. Classification requires balanced training datasets. However, some RBC types are rare. The imbalance ratio is 34.538 on 12 classes with 20,875 individual red blood cell samples. The use of machine learning for RBC classification with an imbalance dataset is hence more challenging than many other applications. We analyze techniques to deal with this problem. The best accuracy and f1 score are 0.921 and 0.8679 on EfficientNet-b1 with augmentation. Experimental results show that the weight balancing technique with augmentation has the potential to deal with imbalance problems by improving the f1 score on minority classes while data augmentation significantly improves the overall classification performance.
Classifier Joint VAE latent space Classifier Semantic feature space Image feature space Image feature space Semantic feature space Image space Semantic

Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space. As labeled images are rare, one direction is to augment the dataset by generating either images or image features. However, the former misses finegrained details and the latter requires learning a mapping associated with class embeddings. In this work, we take feature generation one step further and propose a model where a shared latent space of image features and class embeddings is learned by modality-specific aligned variational autoencoders. This leaves us with the required discriminative information about the image and classes in the latent features, on which we train a softmax classifier. The key to our approach is that we align the distributions learned from images and from side-information to construct latent features that contain the essential multi-modal information associated with unseen classes. We evaluate our learned latent features on several benchmark datasets, i.e. CUB, SUN, AWA1 and AWA2, and establish a new state of the art on generalized zero-shot as well as on few-shot learning. Moreover, our results on ImageNet with various zero-shot splits show that our latent features generalize well in largescale settings.
Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning

In this paper we present an end-to-end framework for addressing the problem of dynamic pricing on E-commerce platform using methods based on deep reinforcement learning (DRL). By using four groups of different business data to represent the states of each time period, we model the dynamic pricing problem as a Markov Decision Process (MDP). Compared with the state-of-the-art DRL-based dynamic pricing algorithms, our approaches make the following three contributions. First, we extend the discrete set problem to the continuous price set. Second, instead of using revenue as the reward function directly, we define a new function named difference of revenue conversion rates (DRCR). Third, the cold-start problem of MDP is tackled by pre-training and evaluation using some carefully chosen historical sales data. Our approaches are evaluated by both offline evaluation method using real dataset of Alibaba Inc., and online field experiments on Tmall.com, a major online shopping website owned by Alibaba Inc. In particular, experiment results suggest that DRCR is a more appropriate reward function than revenue, which is widely used by current literature. In the end, field experiments, which last for months on 1000 stock keeping units (SKUs) of products demonstrate that continuous price sets have better performance than discrete sets and show that our approaches significantly outperformed the manual pricing by operation experts.
Cross-modal Retrieval and Synthesis (X-MRS): Closing the modality gap in shared subspace

Computational food analysis (CFA), a broad set of methods that attempt to automate food understanding, naturally requires analysis of multi-modal evidence of a particular food or dish, e.g. images, recipe text, preparation video, nutrition labels, etc. A key to making CFA possible is multi-modal shared subspace learning, which in turn can be used for cross-modal retrieval and/or synthesis, particularly, between food images and their corresponding textual recipes. In this work we propose a simple yet novel architecture for shared subspace learning, which is used to tackle the food image-to-recipe retrieval problem. Our proposed method employs an effective transformer based multilingual recipe encoder coupled with a traditional image embedding architecture. Experimental analysis on the public Recipe1M dataset shows that the subspace learned via the proposed method outperforms the current state-of-the-arts (SoTA) in food retrieval by a large margin, obtaining recall@1 of 0.64. Furthermore, in order to demonstrate the representational power of the learned subspace, we propose a generative food image synthesis model conditioned on the embeddings of recipes. Synthesized images can effectively reproduce the visual appearance of paired samples, achieving R@1 of 0.68 in the image-to-recipe retrieval experiment, thus effectively capturing the semantics of the textual recipe.
Refined geometric realization of a quantum group and Lusztig’s symmetries

ABSTRACT In this paper, we shall give a refinement of the geometric realization of Lusztig’s algebra f given by Lusztig. This realization gives a geometric interpretation of the decomposition of f. As an application, we shall give geometric realizations of Lusztig’s symmetries on the whole quantum group.
Efficient model selection in switching linear dynamic systems

The computational resources required for the optimal switching Kalman Filter (SKF) increase exponentially with the number of modes, an impediment that limits its utility especially for estimating a high-dimensional state variable in real-time. In this paper, a systematic graph representation for a switching linear dynamic system (SLDS) is proposed along with hierarchical clustering to reduce the cardinality of the set of switching modes offline before collecting the measurements. It is shown that the induced error caused by model complexity reduction can be quantified using the proposed graph framework. The simulation results verify that clustering based on the proposed framework can effectively reduce the model complexity and approximately quantify the effect of model complexity reduction on the estimation error.
NELA-GT-2022: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles

In this paper, we present the fifth installment of the NELA-GT datasets, NELA-GT-2022. The dataset contains 1,778,361 articles from 361 outlets between January 1st, 2022 and December 31st, 2022. Just as in past releases of the dataset, NELA-GT-2022 includes outlet-level veracity labels from Media Bias/Fact Check and tweets embedded in collected news articles. The NELA-GT-2022 dataset can be found at: https://doi.org/10.7910/DVN/AMCV2H
Learning large-scale heteroassociative memories in spiking neurons

Associative memories have been an active area of research over the last forty years (Willshaw et al., 1969; Kohonen, 1972; Hopfield, 1982) because they form a central component of many cognitive architectures (Pollack, 1988; Anderson & Lebiere, 1998). We focus specifically on associative memories that store associations between arbitrary pairs of neural states. When a noisy version of an input state vector is presented to the network, it must output a “clean” version of the associated state vector. We describe a method for building large-scale networks for online learning of associations using spiking neurons, which works by exploiting the techniques of the Neural Engineering Framework (Eliasmith & Anderson, 2003). This framework has previously been used by Stewart et al. (2011) to create memories that possess a number of desirable properties including high accuracy, a fast, feedforward recall process, and efficient scaling, requiring a number of neurons linear in the number of stored associations. These memories have played a central role in several recent neural cognitive models including Spaun, the world’s largest functional brain model (Eliasmith et al., 2012), as well as a proposal for human-scale, biologically plausible knowledge representation (Crawford et al., 2013). However, these memories are constructed using an offline optimization method that is not biologically plausible. Here we demonstrate how a similar set of connection weights can be arrived at through a biologically plausible, online learning process featuring a novel synaptic learning rule inspired in part by the well-known Oja learning rule (Oja, 1989). We present the details of our method and report the results of simulations exploring the storage capacity of these networks. We show that our technique scales up to large numbers of associations, and that recall performance degrades gracefully as the theoretical capacity is exceeded. This work has been implemented in the Nengo simulation package (http://nengo.ca), which will allow straightforward implementations of spiking neural networks on neuromorphic hardware. The result of our work is a fast, adaptive, scalable associative memory composed of spiking neurons which we expect to be a valuable addition to large systems peforming online neural computation. Our network consists of three layers of spiking neurons. The first layer encodes the current input, and the third layer encodes the current output. Associations are learned by changing the efferent and afferent connection weights of the middle layer using biologically plausible learning rules. 1 Input Learning Rule Given a learning rate η, an input vector x encoded by the activity of the input layer, the filtered activity a(t) of neurons in the middle layer, and the matrix e whose rows are the “preferred
An alternative approach for solving the problem of close to singular covariance matrices in modern portfolio theory

In this thesis the effects of utilizing the sample covariance matrix in the estimation of the global minimum variance (GMV) portfolio are presented. When the number of assets, N, are close to the number of observations, T, the sample covariance matrix approaches singularity, leading to a lot of uncertainties in form of estimation error. Due to that the computations of the sample GMV portfolio are linear transformations of the sample co- variance matrix inverse, the error is transferred to the portfolio weights. As a consequence, the portfolio performance out-of-sample is misleading and inadequate. To solve this shortcoming an alternative approach is presented. By grouping similar stocks together, utilizing sector indices, the dimension of the sample covariance matrix is decreased and consequently the estimation error. As a result, the sample sector index portfolio displays a more stable structure than the sample GMV portfolio counterpart as N / T → 1. This leads to more accurate parameter estimates and less volatile portfolio performances out-of-sample. (Less)
Constrained Bayesian Optimization for Automatic Chemical Design

Automatic Chemical Design leverages recent advances in deep generative modelling to provide a framework for performing continuous optimization of molecular properties. Although the provision of a continuous representation for prospective lead drug candidates has opened the door to gradient-based optimization, some challenges remain for the design process. One known pathology is the model’s tendency to decode invalid molecular structures. The goal of this paper is to test the hypothesis that the origin of the pathology is rooted in the current formulation of Bayesian optimization. Recasting the optimization procedure as a constrained Bayesian optimization problem allows the model to produce novel drug compounds consistently ranking in the 100th percentile of the distribution over training set scores
Heuristic Black-box Adversarial Attacks on Video Recognition Models

We study the problem of attacking video recognition models in the black-box setting, where the model information is unknown and the adversary can only make queries to detect the predicted top-1 class and its probability. Compared with the black-box attack on images, attacking videos is more challenging as the computation cost for searching the adversarial perturbations on a video is much higher due to its high dimensionality. To overcome this challenge, we propose a heuristic black-box attack model that generates adversarial perturbations only on the selected frames and regions. More specifically, a heuristic-based algorithm is proposed to measure the importance of each frame in the video towards generating the adversarial examples. Based on the frames' importance, the proposed algorithm heuristically searches a subset of frames where the generated adversarial example has strong adversarial attack ability while keeps the perturbations lower than the given bound. Besides, to further boost the attack efficiency, we propose to generate the perturbations only on the salient regions of the selected frames. In this way, the generated perturbations are sparse in both temporal and spatial domains. Experimental results of attacking two mainstream video recognition methods on the UCF-101 dataset and the HMDB-51 dataset demonstrate that the proposed heuristic black-box adversarial attack method can significantly reduce the computation cost and lead to more than 28% reduction in query numbers for the untargeted attack on both datasets.
Adelic geometry on arithmetic surfaces I: idelic and adelic interpretation of Deligne pairing

For an arithmetic surface $X\to B=\operatorname{Spec} O_K$ the Deligne pairing $\left \colon \operatorname{Pic}(X) \times \operatorname{Pic}(X) \to \operatorname{Pic}(B)$ gives the "schematic contribution" to the Arakelov intersection number. We present an idelic and adelic interpretation of the Deligne pairing; this is the first crucial step for a full idelic and adelic interpretation of the Arakelov intersection number. For the idelic approach we show that the Deligne pairing can be lifted to a pairing $\left _i:\ker(d^1_\times)\times \ker(d^1_\times)\to\operatorname{Pic}(B) $, where $\ker(d^1_\times)$ is an important subspace of the two dimensional idelic group $\mathbf A_X^\times$. On the other hand, the argument for the adelic interpretation is entirely cohomological.
Distributionally Robust Optimization Approaches for a Stochastic Mobile Facility Routing and Scheduling Problem

We study a mobile facility (MF) fleet sizing, routing, and scheduling problem with random demand. Specifically, we seek to find the number of MFs to use in a given service region over a specified planning horizon and the route and schedule for each selected MF. The demand for MF service at each demand node in each period is random. The distributions of the demands are unknown. To address uncertainty and distributional ambiguity, we propose and analyze two distributionally robust MF routing and scheduling (DMFRS) models. These models seek to find MF fleet sizing, routing, and scheduling decisions that minimize the fixed cost of establishing the MF fleet and maximum expected transportation and unmet demand costs over all possible demand distributions residing within an ambiguity set. In the first model, we use a moment-based ambiguity set. In the second model, we use an ambiguity set that incorporates all distributions within a 1-Wasserstein distance from a reference distribution. To solve DMFRS models, we propose a decompositionbased algorithm and derive lower bound and two-families of symmetry breaking inequalities to strengthen the master problem and speed up convergence. We extend DMFRS models using a mean conditional value-at-risk criterion to model the decision maker’s risk-averse attitude. Finally, we present extensive computational experiments comparing the operational and computational performance of the proposed distributionally robust models and a stochastic programming model and drive insights into DMFRS.
Capacity Optimality of AMP in Coded Systems

This paper studies a large random matrix system (LRMS) model involving an arbitrary signal distribution and forward error control (FEC) coding. We establish an area property based on the approximate message passing (AMP) algorithm. Under the assumption that the state evolution for AMP is correct for the coded system, the achievable rate of AMP is analyzed. We prove that AMP achieves the constrained capacity of the LRMS with an arbitrary signal distribution provided that a matching condition is satisfied. As a byproduct, we provide an alternative derivation for the constraint capacity of an LRMS using a proved property of AMP. We discuss realization techniques for the matching principle of binary signaling using irregular low-density parity-check (LDPC) codes and provide related numerical results. We show that the optimized codes demonstrate significantly better performance over un-matched ones under AMP. For quadrature phase shift keying (QPSK) modulation, bit error rate (BER) performance within 1 dB from the constrained capacity limit is observed.
2 7 N ov 2 01 8 What is Interpretable ? Using Machine Learning to Design Interpretable Decision-Support Systems

Motivation: Machine Learning (ML) models have been shown in many cases to achieve higher predictive power than simpler statistical methods [1]. It is therefore easy to envision sophisticated, carefully calibrated ML models being utilized to support critical decision-making as in, e.g., [2], [3]. However, despite their ability to provide accurate predictions, ML models have not been heavily utilized in fields such as medicine and prognostic research [4] [5] [6].
Amplitude analysis and branching-fraction measurement of D+s → K0SK−π+π+

Using 6.32 fb−1 of e+e− collision data collected by the BESIII detector at the center-of-mass energies between 4.178 and 4.226 GeV, an amplitude analysis of the D+s → K0SK−π+π+ decays is performed .
DeepSym: Deep Symbol Generation and Rule Learning from Unsupervised Continuous Robot Interaction for Planning

Autonomous discovery of discrete symbols and rules from continuous interaction experience is a crucial building block of robot AI, but remains a challenging problem. Solving it will overcome the limitations in scalability, flexibility, and robustness of manually-designed symbols and rules, and will constitute a substantial advance towards autonomous robots that can learn and reason at abstract levels in open-ended environments. Towards this goal, we propose a novel and general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them that can be used in complex action planning. Our robot interacts with single and multiple objects using a given action repertoire and observes the effects created in the environment. In order to form action-grounded object, effect, and relational categories, we employ a binarized bottleneck layer of a predictive, deep encoder-decoder network that takes as input the image of the scene and the action applied, and generates the resulting object displacements in the scene (action effects) in pixel coordinates. The binary latent vector represents a learned, action-driven categorization of objects. To distill the knowledge represented by the neural network into rules useful for symbolic reasoning, we train a decision tree to reproduce its decoder function. From its branches we extract probabilistic rules and represent them in PPDDL, allowing off-the-shelf planners to operate on the robot's sensorimotor experience. Our system is verified in a physics-based 3d simulation environment where a robot arm-hand system learned symbols that can be interpreted as 'rollable', 'insertable', 'larger-than' from its push and stack actions; and generated effective plans to achieve goals such as building towers from given cubes, balls, and cups using off-the-shelf probabilistic planners.
Ultrafast X-Ray Induced Changes of the Electronic and Magnetic Response of Solids Due to Valence Electron Redistribution

We report a novel mechanism, consisting of redistribution of valence electrons near the Fermi level, during interactions of intense femtosecond X-ray pulses with a Co/Pd multilayer. The changes in Co 3d valence shell occupation were directly revealed by fluence-dependent changes of the Co L$_3$ X-ray absorption and magnetic circular dichroism spectra near the excitation threshold. The valence shell redistribution arises from inelastic scattering of high energy Auger electrons and photoelectrons that lead to transient holes below and electrons above the Fermi level on the femtosecond time scale. The valence electron reshuffling effect scales with the energy deposited by X-rays and within 17 fs extends to valence states within 2 eV of the Fermi level. As a consequence the sample demagnetizes by more than twenty percent due to magnon generation.
Optimal Power Flow for DC Networks with Robust Feasibility and Stability Guarantees

With high penetrations of renewable generation and variable loads, there is significant uncertainty associated with power flows in DC networks such that stability and constraint satisfaction are important concerns. Most existing DC network optimal power flow (DN-OPF) formulations assume exact knowledge about loading conditions and do not provide stability guarantees. In contrast, this paper studies a DN-OPF formulation which considers both stability and constraint satisfaction under uncertainty. The need to account for a range of uncertainty realizations in this paper's robust optimization formulation results in a challenging semi-infinite program (SIP). The proposed solution algorithm reformulates this SIP into a computationally amenable problem whose solution provides a feasible operating point for the SIP. This algorithm constructs a convex stability set using sufficient conditions for the existence of a feasible and stable power flow solution. Solving an optimization problem based on this convex stability set provides generator set points which ensure that the DC network has a stable operating point for any considered uncertainty realization. This optimization problem takes a form similar to a deterministic DN-OPF problem and is therefore tractable. The proposed algorithm is demonstrated using various DC networks adapted from IEEE test cases.