Miracle-dz/UniFAR-FTUs huggingface dataset:
Buzzsaw: A System for High Speed Feature Engineering

Feature Engineering is a critical part of machine learning systems. Most feature engineering solutions focus on a single language or computational abstraction for their pipelines, forcing practitioners to either embrace a restricted ecosystem or incur operational and orchestrational complexity. In this paper, we describe Buzzsaw : an independent system for constructing and evaluating complex feature pipelines, designed for embedded usage across multiple languages and platforms. We describe its core interfaces, data types, and algorithms for implementing the system and compare it to three other common feature engineering frameworks.
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features

Deep generative networks trained via maximum likelihood on a natural image dataset like CIFAR10 often assign high likelihoods to images from datasets with different objects (e.g., SVHN). We refine previous investigations of this failure at anomaly detection for invertible generative networks and provide a clear explanation of it as a combination of model bias and domain prior: Convolutional networks learn similar low-level feature distributions when trained on any natural image dataset and these low-level features dominate the likelihood. Hence, when the discriminative features between inliers and outliers are on a high-level, e.g., object shapes, anomaly detection becomes particularly challenging. To remove the negative impact of model bias and domain prior on detecting high-level differences, we propose two methods, first, using the log likelihood ratios of two identical models, one trained on the in-distribution data (e.g., CIFAR10) and the other one on a more general distribution of images (e.g., 80 Million Tiny Images). We also derive a novel outlier loss for the in-distribution network on samples from the more general distribution to further improve the performance. Secondly, using a multi-scale model like Glow, we show that low-level features are mainly captured at early scales. Therefore, using only the likelihood contribution of the final scale performs remarkably well for detecting high-level feature differences of the out-of-distribution and the in-distribution. This method is especially useful if one does not have access to a suitable general distribution. Overall, our methods achieve strong anomaly detection performance in the unsupervised setting, reaching comparable performance as state-of-the-art classifier-based methods in the supervised setting.
Waveform-based Voice Activity Detection Exploiting Fully Convolutional networks with Multi-Branched Encoders.

In this study, we propose an encoder-decoder structured system with fully convolutional networks to implement voice activity detection (VAD) directly on the time-domain waveform. The proposed system processes the input waveform to identify its segments to be either speech or non-speech. This novel waveform-based VAD algorithm, with a short-hand notation "WVAD", has two main particularities. First, as compared to most conventional VAD systems that use spectral features, raw-waveforms employed in WVAD contain more comprehensive information and thus are supposed to facilitate more accurate speech/non-speech predictions. Second, based on the multi-branched architecture, WVAD can be extended by using an ensemble of encoders, referred to as WEVAD, that incorporate multiple attribute information in utterances, and thus can yield better VAD performance for specified acoustic conditions. We evaluated the presented WVAD and WEVAD for the VAD task in two datasets: First, the experiments conducted on AURORA2 reveal that WVAD outperforms many state-of-the-art VAD algorithms. Next, the TMHINT task confirms that through combining multiple attributes in utterances, WEVAD behaves even better than WVAD.
Graphs with Multiple Sources per Vertex

Several attempts have been made at constructing Abstract Meaning Representations (AMRs) compositionally, and recently the idea of using s-graphs with the HR-algebra (Koller, 2015) has been simplified to reduce the number of options when parsing (Groschwitz et al., 2017). This apply-modify algebra (AM-algebra) is a linguistically plausible graph algebra with two classes of operations, both of rank two: the apply operation is used to combine a predicate with its argument; the modify operation is used to modify a predicate. While the AM-algebra correctly handles relative clauses and complex cases of coordination, it cannot parse reflexive sentences like: "The raven washes herself. " To facilitate processing of such reflexive sentences, this paper proposes to change the definition of s-graphs underlying the AM-algebra to allow vertices with multiple sources, and additionally proposes an adaption to the type system of the algebra to correctly handle such vertices.
Towards Reliable Real-time Opera Tracking: Combining Alignment with Audio Event Detectors to Increase Robustness

Recent advances in real-time music score following have made it possible for machines to automatically track highly complex polyphonic music, including full orchestra performances. In this paper, we attempt to take this to an even higher level, namely, live tracking of full operas. We first apply a state-of-the-art audio alignment method based on online Dynamic Time-Warping (OLTW) to full-length recordings of a Mozart opera and, analyzing the tracker's most severe errors, identify three common sources of problems specific to the opera scenario. To address these, we propose a combination of a DTW-based music tracker with specialized audio event detectors (for applause, silence/noise, and speech) that condition the DTW algorithm in a top-down fashion, and show, step by step, how these detectors add robustness to the score follower. However, there remain a number of open problems which we identify as targets for ongoing and future research.
Continuous Variable Single Mode Quantum Decoder for Image Reconstruction and Denoising

Quantum computation using optical modes has been well-established in its ability to construct deep neural networks. We introduce a model that is the quantum analogue of the classical autoencoder - a neural network model that can reconstruct its input via dimensionality reduction and expansion through the phase-space formulation of quantum mechanics. The hallmark of the continuous-variable (CV) model is its ability to forge non-linear functions using a set of gates that allows it to remain completely unitary. We leverage this property of the CV model to encode and decode - classical information and demonstrate denoising applications using parallel single mode photonic circuits. The proposed model exemplifies that the appropriate photonic hardware can be integrated with present day optical communication systems to meet our information processing requirements. Here, using the Strawberry Fields software library on the MNIST dataset of handwritten digits, we demonstrate the adaptability of the network to learn classical information to fidelities of greater than 99.98\%.
The chemistry of insect hemolymph; organic components of the hemolymph of the silkworm, Bombyx mori, and two other species.

1. Hemolymph was collected for analysis from the silkworm, Bombyx mori, in a series of developmental stages ranging from the second molt to the late pupa. The mean pH of larval hemolymph after collection was found to be 6.45, that of pupal hemolymph, 6.57; in vivo values may be slightly lower. Total dry solids ranged from 5.4 to 10.6 per cent. Total protein ranged from 1.2 to 5.3 per cent, increasing rapidly during the fifth instar. 2. Free amino acids were separated chromatographically and estimated. Of 19 amino acids identified, amounting collectively to 823 to 1497 mg. per 100 ml. , glutamine, histidine, and lysine generally occurred in greatest amount. Tryptophan was not detected, and cystine (or cysteine) was found in only one sample. The total free amino acids account for 35 to 55 per cent of the non-protein nitrogen of the plasma. 3. Free sugars, estimated semiquantitatively on chromatograms, comprise glucose, fructose, and sucrose in total amount ranging from about 5 to 40 mg. per 100 ml. Total acid-soluble, ultrafiltrable carbohydrate, estimated as glucose by the anthrone reaction, ranged from 166 to 635 mg. per 100 ml. , indicating the presence of low molecular weight sugar derivatives. 4. Inorganic phosphate amounted to 5 to 15 mg. per 100 ml. , and acid-soluble organic phosphate to 100 to 200 mg. per 100 ml. The latter fraction includes several substances, of which one was tentatively identified as glucose-6-phosphate and the remainder are as yet unidentified. 5. Single samples of hemolymph were also taken from larvae of the wax moth, Galleria mellonella, and the spruce sawfly, Diprion hercyniae. These contained even higher concentrations of solutes than the silkworm samples, but with a generally similar distribution. The proportions of the free amino acids were different in each species.
Bootstrapping Complete The Look at Pinterest

Putting together an ideal outfit is a process that involves creativity and style intuition. This makes it a particularly difficult task to automate. Existing styling products generally involve human specialists and a highly curated set of fashion items. In this paper, we will describe how we bootstrapped the Complete The Look (CTL) system at Pinterest. This is a technology that aims to learn the subjective task of "style compatibility" in order to recommend complementary items that complete an outfit. In particular, we want to show recommendations from other categories that are compatible with an item of interest. For example, what are some heels that go well with this cocktail dress? We will introduce our outfit dataset of over 1 million outfits and 4 million objects, a subset of which we will make available to the research community, and describe the pipeline used to obtain and refresh this dataset. Furthermore, we will describe how we evaluate this subjective task and compare model performance across multiple training methods. Lastly, we will share our lessons going from experimentation to working prototype, and how to mitigate failure modes in the production environment. Our work represents one of the first examples of an industrial-scale solution for compatibility-based fashion recommendation.
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization

Matrix square roots and their inverses arise frequently in machine learning, e.g., when sampling from high-dimensional Gaussians $\mathcal{N}(\mathbf 0, \mathbf K)$ or whitening a vector $\mathbf b$ against covariance matrix $\mathbf K$. While existing methods typically require $O(N^3)$ computation, we introduce a highly-efficient quadratic-time algorithm for computing $\mathbf K^{1/2} \mathbf b$, $\mathbf K^{-1/2} \mathbf b$, and their derivatives through matrix-vector multiplication (MVMs). Our method combines Krylov subspace methods with a rational approximation and typically achieves $4$ decimal places of accuracy with fewer than $100$ MVMs. Moreover, the backward pass requires little additional computation. We demonstrate our method's applicability on matrices as large as $50,\! 000 \times 50,\! 000$ - well beyond traditional methods - with little approximation error. Applying this increased scalability to variational Gaussian processes, Bayesian optimization, and Gibbs sampling results in more powerful models with higher accuracy.
DS6: Deformation-aware learning for small vessel segmentation with small, imperfectly labeled dataset

Originating from the initial segment of the middle cerebral artery of the human brain, Lenticulostriate Arteries (LSA) are a collection of perforating vessels that supply blood to the basal ganglia region. With the advancement of 7 Tesla scanner, we are able to detect these LSA which are linked to Small Vessel Diseases(SVD) and potentially a cause for neurodegenerative diseases. Segmentation of LSA with traditional approaches like Frangi or semi-automated/manual techniques can depict medium to large vessels but fail to depict the small vessels. Also, semi-automated/manual approaches are time-consuming. In this paper, we put forth a study that incorporates deep learning techniques to automatically segment these LSA using 3D 7 Tesla Time-of-fight Magnetic Resonance Angiogram images. The algorithm is trained and evaluated on a small dataset of 11 volumes. Deep learning models based on Multi-Scale Supervision U-Net accompanied by elastic deformations to give equivariance to the model, were utilized for the vessel segmentation using semi-automated labeled images. We make a qualitative analysis of the output with the original image and also on imperfect semi-manual labels to confirm the presence and continuity of small vessels.
Cytoreductive Nephrectomy Combined With Sequential Targeted Therapy For Treating Multiple Bilateral Metastatic Renal Cell Carcinoma: A Case Report

Targeted therapy is the standard treatment for advanced renal cell carcinoma that has been used extensively in the past decade. Sequential targeted therapy is an available choice for patients who develop drug resistance. However, it is debatable whether mammalian target of rapamycin inhibitor or another tyrosine kinase inhibitor should be used for sequential therapy after first-line anti-angiogenic drug treatment. In this study, we summarized the case report of a 51-year-old patient with advanced bilateral renal cancer who underwent tumor reduction surgery and sequential targeted therapy. First-line sunitinib, second-line everolimus, and third-line axitinib were given to this patient as part of a sequential therapy. The whole treatment process in details is described and discussed. The results indicate that cytoreductive nephrectomy combined with sequential targeted therapy is safe and effective in the treatment of multiple metastatic renal cell carcinoma. First-line sunitinib and subsequent axitinib may be a good choice for sequential therapy.
Retinal dehydrogenase 5 (RHD5) attenuates metastasis via regulating HIPPO/YAP signaling pathway in Hepatocellular Carcinoma

Retinal dehydrogenase 5 (RDH5) is an important enzyme in the visual cycle. Several studies have reported that the RDH family may play crucial roles in tumor prognosis. However, the role of RDH5 in tumor prognosis is still unclear. We examined the mRNA level of RDH5 by using q-PCR in hepatocellular carcinoma (HCC) and adjacent non-cancerous tissues. The proliferation rate of HCC cells was detected by MTS assay, and the invasive ability was examined by transwell and scratch wound assays. The YAP protein localization and expression were visualized by immunofluorescence in two different cell lines. CpG islands in the promoter region were predicted by using the methprimer database. Clinical characteristics of a patient cohort data came from The Cancer Genome Atlas database. RDH5 was significantly downregulated in hepatocellular carcinoma tissues, and low RDH5 expression was associated with metastasis and poor patient prognosis. Functional assays revealed that the RDH5 promoter is methylated in HCC cell lines. Moreover, overexpressing RDH5 can suppress metastasis by reversing the epithelial-mesenchymal transition (EMT) process, and RDH5 also inhibits cell proliferation in HCC cell lines. Furthermore, suppressing RDH5 can activate the Hippo/YAP signaling pathway and promote the nuclear translocation of YAP. Clinical data demonstrated that RDH5 is an independent prognostic factor in HCC. In our study, we provided the first evidence that RDH5 plays a crucial role in suppressing proliferation and metastasis, and the RDH5 promoter is methylated in hepatocellular carcinoma. And as an important regulator, RDH5 can suppress the Hippo/YAP signaling pathway. Taken together, it revealed that RDH5 might be a potential therapeutic target in HCC patients.
Cooperative Multiagent Reinforcement Learning With Partial Observations

In this article, we propose a distributed zeroth-order policy optimization method for multiagent reinforcement learning (MARL). Existing MARL algorithms often assume that every agent can observe the states and actions of all the other agents in the network. This can be impractical in large-scale problems, where sharing the state and action information with multihop neighbors may incur significant communication overhead. The advantage of the proposed zeroth-order policy optimization method is that it allows the agents to compute the local policy gradients needed to update their local policy functions using local estimates of the global accumulated rewards that depend on partial state and action information only and can be obtained using consensus. Specifically, to calculate the local policy gradients, we develop a new distributed zeroth-order policy gradient estimator that relies on one-point residual-feedback which, compared to existing zeroth-order estimators that also rely on one-point feedback, significantly reduces the variance of the policy gradient estimates improving, in this way, the learning performance. We show that the proposed distributed zeroth-order policy optimization method with constant stepsize converges to the neighborhood of a policy that is a stationary point of the global objective function. The size of this neighborhood depends on the agents' learning rates, the exploration parameters, and the number of consensus steps used to calculate the local estimates of the global accumulated rewards. Moreover, we provide numerical experiments that demonstrate that our new zeroth-order policy gradient estimator is more sample-efficient compared to other existing one-point estimators.
Home range and habitat analysis using dynamic time geography

Wildlife home ranges continue to be a common spatial unit for modeling animal habitat selection. Telemetry data are increasing in spatial and temporal detail and new methods are being developed to incorporate fine resolution data into home range delineation. We extended a previously developed home range estimation technique that incorporates theory from time geography, the potential path area (PPA) home range, to allow the home range to be defined at multiple spatial scales depending on the observed rate of movement within the data. The benefits of this approach are demonstrated with a simulation study, which uses multi-state correlated random walks to represent dynamic movement phases to compare the modified PPA home range technique with a suite of other home range estimation methods (PPA home range, kernel density estimation, Brownian bridges, and dynamic Brownian bridges). We used a case study on caribou (Rangifer tarandus) movement from northern Canada to highlight the value of this approach for characterizing habitat conditions associated with wildlife habitat analysis. We used a simple habitat covariate, percent forest cover, to explore the potential for misleading habitat estimates when home ranges do not include potentially visited locations (omission area) or include areas not possibly visited (commission area). We highlight the advantages of the dynamic PPA home range in the context of quantifying omission and commission areas in other home range techniques. Finally, we provide our R code for calculating dynamic PPA home range estimates. © 2015 The Wildlife Society.
Improving the Convergence Rate of One-Point Zeroth-Order Optimization using Residual Feedback

Many existing zeroth-order optimization (ZO) algorithms adopt two-point feedback schemes due to their fast convergence rate compared to one-point feedback schemes. However, two-point schemes require two evaluations of the objective function at each iteration, which can be impractical in applications where the data are not all available a priori, e.g., in online optimization. In this paper, we propose a novel one-point feedback scheme that queries the function value only once at each iteration and estimates the gradient using the residual between two consecutive feedback points. When optimizing a deterministic Lipschitz function, we show that the query complexity of ZO with the proposed one-point residual feedback matches that of ZO with the existing two-point feedback schemes. Moreover, the query complexity of the proposed algorithm can be improved when the objective function has Lipschitz gradient. Then, for stochastic bandit optimization problems, we show that ZO with one-point residual feedback achieves the same convergence rate as that of ZO with two-point feedback with uncontrollable data samples. We demonstrate the effectiveness of the proposed one-point residual feedback via extensive numerical experiments.
Probabilistic Fair Clustering

In clustering problems, a central decision-maker is given a complete metric graph over vertices and must provide a clustering of vertices that minimizes some objective function. In fair clustering problems, vertices are endowed with a color (e.g., membership in a group), and the features of a valid clustering might also include the representation of colors in that clustering. Prior work in fair clustering assumes complete knowledge of group membership. In this paper, we generalize prior work by assuming imperfect knowledge of group membership through probabilistic assignments. We present clustering algorithms in this more general setting with approximation ratio guarantees. We also address the problem of "metric membership", where different groups have a notion of order and distance. Experiments are conducted using our proposed algorithms as well as baselines to validate our approach and also surface nuanced concerns when group membership is not known deterministically.
Particle production induced by vacuum decay in real time dynamics

We discuss particle production associated with vacuum decay, which changes the mass of a scalar field coupled to a background field which induces the decay. By utilizing the Stokes phenomenon, we can optimally track the time evolution of the mode function and hence calculate particle production properly. In particular, we use real time formalisms for vacuum decay in Minkowski and de Sitter spacetime together with the Stokes phenomenon method. For each case, we consider the flyover vacuum decay model and stochastic inflation, respectively. Within the real time formalism, the particle production can be viewed as that caused by nontrivial external fields. This gives us a novel perspective of the real time formalism of vacuum decay.
Insect bioassay techniques for screening Bt transgenic cotton plants

Development of efficient insect bioassays is imperative for rapid screening of Bt cotton for insect resistance. The transgenic cotton plants are assessed for insect resistance using part of the plant only ie leaf disc or detached leaf assays. This is the non-destructive method most commonly adopted for screening of transformed plants for insect resistance in early generations. Whereas bioassay using whole plants is the destructive method mainly used for screening further progeny plants since it reflects field condition. However these bioassays are time consuming, often laborious and require a lot of space which make them a hard choice for testing the efficacy of Cry toxins against target pests in greenhouse and large scale field studies. Screening of transgenic cotton plants using efficient bioassay techniques leads to the design of effective tools for controlling insect pests.
Hyperparameter Analysis for Image Captioning

In this paper, we perform a thorough sensitivity analysis on state-of-the-art image captioning approaches using two different architectures: CNN+LSTM and CNN+Transformer. Experiments were carried out using the Flickr8k dataset. The biggest takeaway from the experiments is that fine-tuning the CNN encoder outperforms the baseline and all other experiments carried out for both architectures.