Miracle-dz/UniFAR-FTUs huggingface dataset:
Idiotype-reactive T-cell subsets and tumor load in monoclonal gammopathies

The presence of idiotype-reactive T-cell subsets and their relation to the tumor load were analyzed in 9 patients with monoclonal gammopathy of undetermined significance (MGUS), in 12 patients with multiple myeloma (MM) clinical stage I, and in 9 patients with MM stage II/III. An enzyme-linked immunospot assay was used to identify interferon-gamma (IFN-gamma)-, interleukin-2 (IL-2)-, or IL-4-secreting T cells after stimulation by F(ab')2 fragments of monoclonal IgG. The response to autologous IgG was significantly higher than that induced by isotypic monoclonal IgG. Comparable results were obtained in a proliferation assay (3H-thymidine incorporation). A total of 8 of 9 patients with MGUS, 7 of 12 patients with MM stage I, and 3 of 9 with MM stage II/III had T cells secreting IFN-gamma and/or IL-2 (T helper [Th1] type-1 cells), whereas cells secreting both Th1 and Th2 or Th0 types of cytokines were more frequent in patients with MM, particularly in those with MM stage II/III. The number and frequency of Th1-type cells were significantly higher in MGUS patients as compared with those of MM stage II/III. The results indicate that idiotype-reactive T cells of the Th1 and Th2 or Th0 subsets were present in MGs and might provide indirect evidence that idiotype-reactive Th1-type cells may have a regulatory impact on the human tumor B cells.
Beyond the Amber Waves of Grain: An Examination of Social and Economic Restructuring in the Heartland

Introduction: farm restructuring and crisis in the heartland, Linda Lobao and Paul Lasley the agricultures of the Midwest and their demographic and economic environments, Daryl Hobbs and Robert Weagley. Part 1 The farm enterprise: financial characteristics of farm operations, F. Larry Leistritz and Freddie L. Barnard the process of adaptation and the consequences to the farm system, Bruce B. Johnson and Raymond D. Vlasin plans for changing the farming business and needs for training, Kent D. Olson and William E. Saupe. Part 2 The farm household: the changing division of labour on American farms, Jackie Fellows the effects of financial hardship on familial well-being, P. Lasley perceiving hardship and managing life, Katherine Meyer. Part 3 The farm community: community change, Arlo Biere organization, community and political involvement as responses to rural restructuring, L. Lobao. Part 4 Policy implications: the farm crisis in the Midwest - trends and implications, F.L. Leistritz and K. Meyer methodology, P. Lasley
Mental Computation Performance and Strategy Use of Japanese Students in Grades 2, 4, 6, and 8.

This study assessed attitude, computational preferences, and mental computational performance of 176, 187, 186, and 206 Japanese students in grades 2, 4, 6, and 8, respectively. A sample of students in grades 4 and 8 scoring in the upper and middle quintiles on the mental computation test was interviewed to identify strategies used to mentally compute. All data were collected during the last month of the school year. A wide range of performance on mental computation was found with respect to all types of numbers (whole numbers, decimals, and fractions) and operations at every grade level; the mode of presentation (visual or oral) significantly affected performance levels, with visual items generally producing higher performance; and the range of strategies (initial and alternative) used to do mental computation was narrow, with the most popular approach reflecting a mental version of a learned "paper/pencil" algorithm.
Karyology of Nonea Medicus (Boraginaceae) in Spain; relationships between genera of Boragineae Barbier & Mathez (Anchuseae DC.)

Abstract In a survey of the karyology of Nonea, which completes an examination of Spanish Boragineae, the chromosome number for N. echioides (L.) Roemer & Shultes is reported for the first time. Relationships between the genera that are included in the tribe Boragineae are discussed, considering chromosomal and morphological characters and the habit of the plants.
Relative importance of plosive cues in the effect of vocal cord polyp on Mandarin tones recognition

This paper investigates the relative importance of plosive cues in the effect of vocal cord polyp on Mandarin tones recognition. There are two sections in this research, i.e., acoustic analysis and perceptual analysis. In acoustic analysis, VOTs of plosives were measured in Praat. And in perception analysis, identification results of Mandarin tones before and after the surgeries of vocal cord polyp would be analyzed from the perspective of unaspirated plosive and aspirated plosive. The research results in this research are as follows: first, unaspirated plosives before the surgery of vocal cord polyp have longer VOT than those after the surgery. Second, aspirated plosives before the surgery of vocal cord polyp have shorter VOT than those after the surgery. Third, vocal cord polyp has a significant effect on VOT of aspirated plosives in Tone 2 and Tone 3 before and after the surgeries. Fourth, aspirated plosives are relative important cues in significantly affecting identification rate of Mandarin CV syllables with aspirated plosives in Tone 1 and Tone 2 before and after the surgeries of vocal cord polyp. Fifth, before and after the surgeries of vocal cord polyp, significant difference of VOT in Tone 2 of aspirated plosive is a relative important cue in significantly influencing identification rate of Mandarin CV syllables with aspirated plosives in Tone 2. It is concluded that aspirated plosives are relative important cues in affecting identification rate of Mandarin tones with plosives as their initials before and after the surgeries of vocal cord polyp, but not unaspirated plosives.
Improving Online Rent-or-Buy Algorithms with Sequential Decision Making and ML Predictions

In this work we study online rent-buy problems as a sequential decision making problem. We show how one can integrate predictions, typically coming from a machine learning (ML) setup, into this framework. Speciﬁcally, we consider the ski-rental problem and the dynamic TCP acknowledgment problem. We present new online algorithms with or without predictions and obtain explicit performance bounds in-terms of the accuracy of the prediction. Our algorithms are close to optimal with accurate predictions while hedging against less accurate predictions
Frenet Coordinate Based Driving Maneuver Prediction at Roundabouts Using LSTM Networks

Driving maneuver prediction is a key requirement for automated vehicles to assess situations and effectively navigate in urban environments. In this paper, we present three models to predict whether a vehicle leaves a roundabout at a specific exit. We develop a Feedforward neural network (FNN), as well as two Long short-term memory (LSTM) networks for this task. We propose several concepts that generalize the models to roundabouts with different radii, layouts, and numbers of exits. For this purpose, we also introduce Frenet coordinates with circles as reference paths. We evaluate our models based on the binary cross-entropy loss and the distance to the exit at which a reliable prediction is obtained in a leave-one-out cross-validation fashion, where one exit is always entirely used as the test set. Training and evaluation is performed on a data set of nearly 4,000 trajectories that we captured using a drone. Our best model achieves a reliable prediction on average 9.34m before an exit for class ”Leaving” and 8.13m before an exit for class ”Staying”.
Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views

Learning object-centric representations of multi-object scenes is a promising approach towards machine intelligence, facilitating high-level reasoning and control from visual sensory data. However, current approaches for unsupervised object-centric scene representation are incapable of aggregating information from multiple observations of a scene. As a result, these"single-view"methods form their representations of a 3D scene based only on a single 2D observation (view). Naturally, this leads to several inaccuracies, with these methods falling victim to single-view spatial ambiguities. To address this, we propose The Multi-View and Multi-Object Network (MulMON) -- a method for learning accurate, object-centric representations of multi-object scenes by leveraging multiple views. In order to sidestep the main technical difficulty of the multi-object-multi-view scenario -- maintaining object correspondences across views -- MulMON iteratively updates the latent object representations for a scene over multiple views. To ensure that these iterative updates do indeed aggregate spatial information to form a complete 3D scene understanding, MulMON is asked to predict the appearance of the scene from novel viewpoints during training. Through experiments, we show that MulMON better-resolves spatial ambiguities than single-view methods -- learning more accurate and disentangled object representations -- and also achieves new functionality in predicting object segmentations for novel viewpoints.
The Autoencoding Variational Autoencoder

Does a Variational AutoEncoder (VAE) consistently encode typical samples generated from its decoder? This paper shows that the perhaps surprising answer to this question is `No'; a (nominally trained) VAE does not necessarily amortize inference for typical samples that it is capable of generating. We study the implications of this behaviour on the learned representations and also the consequences of fixing it by introducing a notion of self consistency. Our approach hinges on an alternative construction of the variational approximation distribution to the true posterior of an extended VAE model with a Markov chain alternating between the encoder and the decoder. The method can be used to train a VAE model from scratch or given an already trained VAE, it can be run as a post processing step in an entirely self supervised way without access to the original training data. Our experimental analysis reveals that encoders trained with our self-consistency approach lead to representations that are robust (insensitive) to perturbations in the input introduced by adversarial attacks. We provide experimental results on the ColorMnist and CelebA benchmark datasets that quantify the properties of the learned representations and compare the approach with a baseline that is specifically trained for the desired property.
Text classification of Diseases Treated by Traditional Chinese Medicine Prescription based on machine learning

OBJECTIVE: To explore the application of machine learning in th e identification of diseases treated by traditional Chinese medicine prescriptions. METHODS: Based on the composition of the text document of Chinese medicine prescriptions, the prescriptions were divided into cough, headache and diarrhea. THUTCT were introduced to establish and train two machine learning text classification models, LibSVM and LibLinear, and the prescriptions to be deter mined were put into the model for classification and prediction. R ESULTS: The Precision rate of LibSVM model and Liblinear mod el were 0.7283 and 0.6690. Seven prescriptions were classified an d predicted, and the results were in line with expectations. CONCLUSION: THUCTC has good universality for the content of TCM prescriptions, high classification accuracy and fast testing speed, which is suitable for the text classification and discrimination research of TCM prescriptions for diseases.
Supporting political violence: The role of ideological passion and social network

This research examines how social networks contribute to the process of radicalization, building on work showing that obsessive (vs. harmonious) passion for a cause is linked to greater support for political violence. Study 1 (N = 331) shows that obsessive (vs. harmonious) passion is related to affiliating with radical (vs. moderate) social networks, which in turn is associated with support for political violence. Study 2 (N = 381) provides experimental evidence for this phenomenon, by showing that inducing an obsessive mindset produces a greater proclivity to connect with radical activists, which in turn is associated with greater support for political violence. Drawing from social network analysis, Study 3 (N = 366) shows that network density intensifies obsessively passionate individuals’ affiliation to radical networks. The results offer insight into the group processes behind radicalization across different cultural contexts and ideologies.
Random Reshuffling is Not Always Better

Many learning algorithms, such as stochastic gradient descent, are affected by the order in which training examples are used. It is generally believed that sampling the training examples without-replacement, also known as random reshufﬂing , causes learning algorithms to converge faster. We give a counterexample to the Operator Inequality of Noncommutative Arithmetic and Geometric Means, a longstanding conjecture that relates to the performance of random reshufﬂing in learning algorithms [19]. We use this to give an example of a learning task and algorithm for which with-replacement random sampling outperforms random reshufﬂing
Efficient Low Rank Gaussian Variational Inference for Neural Networks

Bayesian neural networks are enjoying a renaissance driven in part by recent advances in variational inference (VI). The most common form of VI employs a fully factorized or mean-ﬁ eld distribution, but this is known to suffer from several pathologies, especially as we expect posterior distributions with highly correlated parameters. Current algorithms that capture these correlations with a Gaussian approximating family are dif ﬁ cult to scale to large models due to computational costs and high variance of gradient updates. By using a new form of the reparametrization trick, we derive a computationally ef ﬁ cient algorithm for performing VI with a Gaussian family with a low-rank plus diagonal covariance structure. We scale to deep feed-forward and convolutional architectures. We ﬁ nd that adding low-rank terms to parametrized diagonal covariance does not improve predictive performance except on small networks, but low-rank terms added to a constant diagonal covariance improves performance on small and large-scale network architectures.
Dynamic Submodular Maximization

One of the basic primitives in the class of submodular optimization problems is the submodular maximization under a cardinality constraint. Here we are given a ground set V that is endowed with a monotone submodular function f : 2 V → R + and a parameter 0 < k ≤ n and the goal is to return an optimal set S ⊆ V of at most k elements, i.e., f ( S ) is maximum among all subsets of V of size at most k . This basic primitive has many applications in machine learning as well as combinatorial optimization. Example applications are agglomerative clustering, exemplar-based clustering, categorical feature compression, document and corpus summarization, recommender systems, search result diversiﬁcation, data subset selection, minimum spanning tree, max ﬂow, global minimum cut, maximum matching, traveling salesman problem, max clique, max cut, set cover and knapsack, among the others. In this paper, we propose the ﬁrst dynamic algorithm for this problem. Given a stream of inserts and deletes of elements of an underlying ground set V , we develop a dynamic algorithm that with high probability, maintains a ( 12 − (cid:15) ) - approximation of a cardinality-constrained monotone submodular maximization for any sequence of z updates (inserts and deletes) in time O ( k 2 z(cid:15) − 3 · log 5 n ) , where n is the maximum size of V at any time. That is, the amortized update time of our algorithm is O ( k 2 (cid:15) − 3 · log 5 n ) .
A Chiral Bipyridyl Alcohol for Catalytic Enantioselective Nozaki–Hiyama–Kishi Allylation of Aldehydes and Ketones

A class of bipyridyl alcohol ligands has been developed. A catalyst synthesized using a chromium(II)-ligand promotes the enantioselective Nozaki–Hiyama–Kishi (NHK) allylation of aldehydes and ketones with allylic halides. The allylation of various aromatic, α,β-unsaturated, and aliphatic aldehydes and ketones produces the desired homoallylic alcohols in satisfactory yields (up to 98%) and high enantioselectivities (up to 99% ee). The present method can be applied widely and affords an efficient means of obtaining chiral homoallylic alcohols.
STEER : Simple Temporal Regularization For Neural ODEs

Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive. Indeed, computing the forward pass of such models involves solving an ODE which can become arbitrarily complex during training. Recent works have shown that regularizing the dynamics of the ODE can partially alleviate this. In this paper we propose a new regularization technique: randomly sampling the end time of the ODE during training. The proposed regularization is simple to implement, has negligible overhead and is effective across a wide variety of tasks. Further, the technique is orthogonal to several other methods proposed to regularize the dynamics of ODEs and as such can be used in conjunction with them. We show through experiments on normalizing flows, time series models and image recognition that the proposed regularization can significantly decrease training time and even improve performance over baseline models.
A new convergent variant of Q-learning with linear function approximation

In this work, we identify a novel set of conditions that ensure convergence with probability 1 of Q -learning with linear function approximation, by proposing a two time-scale variation thereof. In the faster time scale, the algorithm features an update similar to that of DQN, where the impact of bootstrapping is attenuated by using a Q -value estimate akin to that of the target network in DQN. The slower time-scale, in turn, can be seen as a modiﬁed target network update. We establish the convergence of our algorithm, provide an error bound and discuss our results in light of existing convergence results on reinforcement learning with function approximation. Finally, we illustrate the convergent behavior of our method in domains where standard Q -learning has previously been shown to diverge.
Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms

We analyze the extent to which existing methods rely on accurate training data for a speciﬁc class of reinforcement learning (RL) algorithms, known as Safe and Seldonian RL. We introduce a new measure of security to quantify the susceptibility to perturbations in training data by creating an attacker model that represents a worst-case analysis, and show that a couple of Seldonian RL methods are extremely sensitive to even a few data corruptions. We then introduce a new algorithm that is more robust against data corruptions, and demonstrate its usage in practice on some RL problems, including a grid-world and a diabetes treatment simulation.
A Bayesian Nonparametrics View into Deep Representations

We investigate neural network representations from a probabilistic perspective. Specifically, we leverage Bayesian nonparametrics to construct models of neural activations in Convolutional Neural Networks (CNNs) and latent representations in Variational Autoencoders (VAEs). This allows us to formulate a tractable complexity measure for distributions of neural activations and to explore global structure of latent spaces learned by VAEs. We use this machinery to uncover how memorization and two common forms of regularization, i.e. dropout and input augmentation, influence representational complexity in CNNs. We demonstrate that networks that can exploit patterns in data learn vastly less complex representations than networks forced to memorize. We also show marked differences between effects of input augmentation and dropout, with the latter strongly depending on network width. Next, we investigate latent representations learned by standard β-VAEs and Maximum Mean Discrepancy (MMD) β-VAEs. We show that aggregated posterior in standard VAEs quickly collapses to the diagonal prior when regularization strength increases. MMD-VAEs, on the other hand, learn more complex posterior distributions, even with strong regularization. While this gives a richer sample space, MMD-VAEs do not exhibit independence of latent dimensions. Finally, we leverage our probabilistic models as an effective sampling strategy for latent codes, improving quality of samples in VAEs with rich posteriors.
Ultra-Low Precision 4-bit Training of Deep Neural Networks

In this paper, we propose a number of novel techniques and numerical representation formats that enable, for the very ﬁrst time, the precision of training systems to be aggressively scaled from 8-bits to 4-bits. To enable this advance, we explore a novel adaptive Gradient Scaling technique (GradScale) that addresses the challenges of insufﬁcient range and resolution in quantized gradients as well as explores the impact of quantization errors observed during model training. We theoretically analyze the role of bias in gradient quantization and propose solutions that mitigate the impact of this bias on model convergence. Finally, we examine our techniques on a spectrum of deep learning models in computer vision, speech and NLP. In combination with previously proposed solutions for 4-bit quantization of weight and activation tensors, 4-bit training shows non-signiﬁcant loss in accuracy across application domains while enabling signiﬁcant hardware acceleration (>7 × over state of the art FP16 systems).
ICNet: Intra-saliency Correlation Network for Co-Saliency Detection

Intra-saliency and inter-saliency cues 3 have been extensively studied for co-saliency detection (Co-SOD). Model-based methods produce coarse Co-SOD results due to hand-crafted intra-and inter-saliency features. Current data-driven models exploit inter-saliency cues, but undervalue the potential power of intra-saliency cues. In this paper, we propose an Intra-saliency Correlation Network (ICNet) to extract intra-saliency cues from the single image saliency maps (SISMs) predicted by any off-the-shelf SOD method, and obtain inter-saliency cues by correlation techniques. Speciﬁcally, we adopt normalized masked average pooling (NMAP) to extract latent intra-saliency categories from the SISMs and semantic features as intra cues. Then we employ a correlation fusion module (CFM) to obtain inter cues by exploiting correlations between the intra cues and single-image features. To improve Co-SOD performance, we propose a category-independent rearranged self-correlation feature (RSCF) strategy. Experiments on three benchmarks show that our ICNet outperforms previous state-of-the-art methods on Co-SOD. Ablation studies validate the effectiveness of our contributions. The PyTorch code is available at https://github.com/blanclist/ICNet .