Miracle-dz/UniFAR-FTUs huggingface dataset:
RNA expression classifiers from a model of breast epithelial cell organization to predict pathological complete response in triple negative breast cancer

Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is correlated with better outcomes for breast cancer, especially for triple negative breast cancer (TNBC). We developed RNA expression classifiers from a model of breast epithelial cell organization to predict which patients will achieve pCR to NAC, and which will have residual disease (RD). An exclusive collection of retrospective formalin-fixed, paraffin-embedded (FFPE) pretreatment biopsies from 222 multi-institutional breast cancer patients treated with NAC, including 90 TNBC patients, were processed using standard procedures. A novel strategy using machine learning algorithms and statistical cross-validation were used to develop predictive classifiers based on AmpliSeq differential gene expression analysis of patient samples. Two RNA expression classifiers of 18 genes and 15 genes applied sequentially to the total cohort, classified patients into three distinct classes which accurately identified 83.75% of pCR and 86.62% of RD patients in the total population, and 92.10% of pCR and 80.77% of RD patients in the TNBC subset. This new approach identified a subset of TNBC patients predicted to have RD showing significantly higher levels of Ki-67 expression and having significantly poorer survival rates than the other TNBC patients. Stratification of patients may allow identification of TNBC patients with the worst prognosis prior to NAC, allowing for personalized treatments with the potential to improve patient outcomes. Statement of SignificanceStratification of TNBC patients by prognosis prior to NAC, may allow for more personalized treatment approaches with the potential to improve patient outcomes and reduce toxicity.
How has the COVID-19 pandemic impacted on smoking and nicotine dependence among people with severe mental ill health? Analysis of linked data from a UK Closing the Gap Cohort

Smoking rates are higher for people who use mental health services which contributes substantially to health inequalities. Smoking can lead to worse COVID-19 outcomes, yet it remains unclear whether smoking has changed for people who use mental health services. We examined smoking patterns in a large clinical cohort of people with SMI before and during the pandemic. We found high levels of nicotine dependence and heavier patterns of smoking. Whilst some people had reported quitting, it is likely that smoking inequalities have become further entrenched. Mental health services should seek to mitigate this modifiable risk and source of poor health.
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies

Biomedical question-answering (QA) has gained increased attention for its capability to provide users with high-quality information from a vast scientific literature. Although an increasing number of biomedical QA datasets has been recently made available, those resources are still rather limited and expensive to produce; thus, transfer learning via pre-trained language models (LMs) has been shown as a promising approach to leverage existing general-purpose knowledge. However, fine-tuning these large models can be costly and time consuming and often yields limited benefits when adapting to specific themes of specialised domains, such as the COVID-19 literature. Therefore, to bootstrap further their domain adaptation, we propose a simple yet unexplored approach, which we call biomedical entity-aware masking (BEM) strategy, encouraging masked language models to learn entity-centric knowledge based on the pivotal entities characterizing the domain at hand, and employ those entities to drive the LM fine-tuning. The resulting strategy is a downstream process applicable to a wide variety of masked LMs, not requiring additional memory or components in the neural architectures. Experimental results show performance on par with the state-of-the-art models on several biomedical QA datasets.
A Koopman Approach to Understanding Sequence Neural Models

We introduce a new approach to understanding trained sequence neural models: the Koopman Analysis of Neural Networks ( KANN ) method. Motivated by the relation between time-series models and self-maps, we compute approximate Koopman operators which encode well the latent dynamics. Unlike other existing methods whose applicability is limited, our framework is global, and it has only weak constraints over the inputs. Moreover, the Koopman operator is linear, and it is related to a rich mathematical theory. Thus, we can use tools and insights from linear analysis and Koopman Theory in our study. For instance, we show that the operator eigendecomposition is instrumental in exploring the dominant features of the network. Our results extend across tasks and architectures as we demonstrate for the copy problem, and ECG classiﬁcation and sentiment analysis tasks.
Machine Learning–based Cyber Attacks Targeting on Controlled Information

Stealing attack against controlled information, along with the increasing number of information leakage incidents, has become an emerging cyber security threat in recent years. Due to the booming development and deployment of advanced analytics solutions, novel stealing attacks utilize machine learning (ML) algorithms to achieve high success rate and cause a lot of damage. Detecting and defending against such attacks is challenging and urgent so governments, organizations, and individuals should attach great importance to the ML-based stealing attacks. This survey presents the recent advances in this new type of attack and corresponding countermeasures. The ML-based stealing attack is reviewed in perspectives of three categories of targeted controlled information, including controlled user activities, controlled ML model-related information, and controlled authentication information. Recent publications are summarized to generalize an overarching attack methodology and to derive the limitations and future directions of ML-based stealing attacks. Furthermore, countermeasures are proposed towards developing effective protections from three aspects—detection, disruption, and isolation.
Non-Autoregressive Text Generation with Pre-trained Language Models

Non-autoregressive generation (NAG) has recently attracted great attention due to its fast inference speed. However, the generation quality of existing NAG models still lags behind their autoregressive counterparts. In this work, we show that BERT can be employed as the backbone of a NAG model for a greatly improved performance. Additionally, we devise two mechanisms to alleviate the two common problems of vanilla NAG models: the inflexibility of prefixed output length and the conditional independence of individual token predictions. To further strengthen the speed advantage of the proposed model, we propose a new decoding strategy, ratio-first, for applications where the output lengths can be approximately estimated beforehand. For a comprehensive evaluation, we test the proposed model on three text generation tasks, including text summarization, sentence compression and machine translation. Experimental results show that our model significantly outperforms existing non-autoregressive baselines and achieves competitive performance with many strong autoregressive models. In addition, we also conduct extensive analysis experiments to reveal the effect of each proposed component.
Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity

Submodular maximization is a classic algorithmic problem with multiple applications in data mining and machine learning; there, the growing need to deal with massive instances motivates the design of algorithms balancing the quality of the solution with applicability. For the latter, an important measure is the adaptive complexity, which captures the number of sequential rounds of parallel computation needed by an algorithm to terminate. In this work we obtain the first constant factor approximation algorithm for non-monotone submodular maximization subject to a knapsack constraint with near-optimal $O(\log n)$ adaptive complexity. Low adaptivity by itself, however, is not enough: a crucial feature to account for is represented by the total number of function evaluations (or value queries). Our algorithm asks $\tilde{O}(n^2)$ value queries, but can be modified to run with only $\tilde{O}(n)$ instead, while retaining a low adaptive complexity of $O(\log^2n)$. Besides the above improvement in adaptivity, this is also the first combinatorial approach with sublinear adaptive complexity for the problem and yields algorithms comparable to the state-of-the-art even for the special cases of cardinality constraints or monotone objectives.
APOGEE spectroscopic evidence for chemical anomalies in dwarf galaxies: The case of M 54 and Sagittarius

We present evidence for globular cluster stellar debris in a dwarf galaxy system (Sagittarius; Sgr) based on an analysis of high-resolution H-band spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey. We add [N/Fe], [Ti/Fe], and [Ni/Fe] abundance ratios to the existing sample of potential members of M 54; this is the first time that [N/Fe] abundances are derived for a large number of stars in M 54. Our study reveals the existence of a significant population of nitrogen- (with a large spread, ≳1 dex) and aluminum-enriched stars with moderate Mg depletions in the core of the M 54+Sgr system, which share the light element anomalies characteristic of second-generation globular cluster stars (GC), thus tracing the typical phenomenon of multiple stellar populations seen in other Galactic GCs at similar metallicity, confirming earlier results based on the Na-O anti-correlation. We further show that most of the stars in M 54 exhibit different chemical patterns evidently not present in Sgr field stars. Furthermore, we report the serendipitous discovery of a nitrogen-enhanced extra-tidal star that has GC second-generation-like chemical patterns for which both chemical and kinematic evidence are commensurate with the hypothesis that the star has been ejected from M 54. Our findings support the existence of chemical anomalies associated with likely tidally shredded GCs that are in dwarf galaxies in the Local Group. These findings motivate future searches for such bona fide stars along other known Milky Way streams.
VA-RED2: Video Adaptive Redundancy Reduction

Performing inference on deep learning models for videos remains a challenge due to the large amount of computational resources required to achieve robust recognition. An inherent property of real-world videos is the high correlation of information across frames which can translate into redundancy in either temporal or spatial feature maps of the models, or both. The type of redundant features depends on the dynamics and type of events in the video: static videos have more temporal redundancy while videos focusing on objects tend to have more channel redundancy. Here we present a redundancy reduction framework, termed VA-RED$^2$, which is input-dependent. Specifically, our VA-RED$^2$ framework uses an input-dependent policy to decide how many features need to be computed for temporal and channel dimensions. To keep the capacity of the original model, after fully computing the necessary features, we reconstruct the remaining redundant features from those using cheap linear operations. We learn the adaptive policy jointly with the network weights in a differentiable way with a shared-weight mechanism, making it highly efficient. Extensive experiments on multiple video datasets and different visual tasks show that our framework achieves $20\% - 40\%$ reduction in computation (FLOPs) when compared to state-of-the-art methods without any performance loss. Project page: http://people.csail. mit.edu/bpan/va-red/.
Matter Density Distribution of General Relativistic Highly Magnetized Jets Driven by Black Holes

High-resolution very long baseline interferometry (VLBI) radio observations have resolved the detailed emission structures of active galactic nucleus jets. General relativistic magnetohydrodynamic (GRMHD) simulations have improved the understanding of jet production physics, although theoretical studies still have difficulty constraining the origin and distribution of jetted matter. We construct a new steady, axisymmetric GRMHD jet model to obtain approximate solutions of black hole (BH) magnetospheres, and examine the matter density distribution of jets. By assuming fixed poloidal magnetic field shapes that mimic force-free analytic solutions and GRMHD simulation results and assuming constant poloidal velocity at the separation surface, which divides the inflow and outflow, we numerically solve the force balance between the field lines at the separation surface and analytically solve the distributions of matter velocity and density along the field lines. We find that the densities at the separation surface in our parabolic field models roughly follow in the far zone from the BH, where r ss is the radius of the separation surface. When the BH spin is larger or the velocity at the separation surface is smaller, the density at the separation surface becomes concentrated closer to the jet edge. Our semianalytic model, combined with radiative transfer calculations, may help us interpret the high-resolution VLBI observations and understand the origin of jetted matter.
Hierarchical VAEs Know What They Don't Know

Deep generative models have been demonstrated as state-of-the-art density estimators. Yet, recent work has found that they often assign a higher likelihood to data from outside the training distribution. This seemingly paradoxical behavior has caused concerns over the quality of the attained density estimates. In the context of hierarchical variational autoencoders, we provide evidence to explain this behavior by out-of-distribution data having in-distribution low-level features. We argue that this is both expected and desirable behavior. With this insight in hand, we develop a fast, scalable and fully unsupervised likelihood-ratio score for OOD detection that requires data to be in-distribution across all feature-levels. We benchmark the method on a vast set of data and model combinations and achieve state-of-the-art results on out-of-distribution detection.
A Law of Robustness for Weight-bounded Neural Networks

Robustness of deep neural networks against adversarial perturbations is a pressing concern motivated by recent findings showing the pervasive nature of such vulnerabilities. One method of characterizing the robustness of a neural network model is through its Lipschitz constant, which forms a robustness certificate. A natural question to ask is, for a fixed model class (such as neural networks) and a dataset of size $n$, what is the smallest achievable Lipschitz constant among all models that fit the dataset? Recently, (Bubeck et al., 2020) conjectured that when using two-layer networks with $k$ neurons to fit a generic dataset, the smallest Lipschitz constant is $\Omega(\sqrt{\frac{n}{k}})$. This implies that one would require one neuron per data point to robustly fit the data. In this work we derive a lower bound on the Lipschitz constant for any arbitrary model class with bounded Rademacher complexity. Our result coincides with that conjectured in (Bubeck et al., 2020) for two-layer networks under the assumption of bounded weights. However, due to our result's generality, we also derive bounds for multi-layer neural networks, discovering that one requires $\log n$ constant-sized layers to robustly fit the data. Thus, our work establishes a law of robustness for weight bounded neural networks and provides formal evidence on the necessity of over-parametrization in deep learning.
A Shuffle Theorem for Paths Under Any Line

Abstract We generalize the shuffle theorem and its $(km,kn)$ version, as conjectured by Haglund et al. and Bergeron et al. and proven by Carlsson and Mellit, and Mellit, respectively. In our version the $(km,kn)$ Dyck paths on the combinatorial side are replaced by lattice paths lying under a line segment whose x and y intercepts need not be integers, and the algebraic side is given either by a Schiffmann algebra operator formula or an equivalent explicit raising operator formula. We derive our combinatorial identity as the polynomial truncation of an identity of infinite series of $\operatorname {\mathrm {GL}}_{l}$ characters, expressed in terms of infinite series versions of LLT polynomials. The series identity in question follows from a Cauchy identity for nonsymmetric Hall–Littlewood polynomials.
NoiseQA: Challenge Set Evaluation for User-Centric Question Answering

When Question-Answering (QA) systems are deployed in the real world, users query them through a variety of interfaces, such as speaking to voice assistants, typing questions into a search engine, or even translating questions to languages supported by the QA system. While there has been significant community attention devoted to identifying correct answers in passages assuming a perfectly formed question, we show that components in the pipeline that precede an answering engine can introduce varied and considerable sources of error, and performance can degrade substantially based on these upstream noise sources even for powerful pre-trained QA models. We conclude that there is substantial room for progress before QA systems can be effectively deployed, highlight the need for QA evaluation to expand to consider real-world use, and hope that our findings will spur greater community interest in the issues that arise when our systems actually need to be of utility to humans.
KINETIC STUDIES ON COOKING OF RICE

The mechanism of cooking rice was investigated in this study. The rheological method using the parallel plate plastometer was adopted for measurement of the degree of cooking. The range of temperatures measured ran from 75–150°C. Experimental results showed that cooking rate followed the equation of a first order chemical reaction. We designated the proportional constant as the cooking rate constant, but the slope of Arrhenius plots of the cooking rate constants changed around 110°C. The activation energy of cooking at temperatures below 110°C and above 110°C was about 19,000 and 8,800 cal/mol respectively. The influences of water soaking time before cooking were also studied. We concluded that the cooking process comprises two mechanisms; at temperatures below 110°C the cooking rate is limited by the reaction rate of rice components with water; and at temperatures above 110°C it is limited by the rate of diffusion of water through the cooked layer toward the interface of uncooked core where the reaction occurs. The reaction rate constant and the diffusion coefficient of water were calculated by assuming the core model or shell-type model.
The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variables

Many real-world decision-making tasks require learning causal relationships between a set of variables. Traditional causal discovery methods, however, require that all variables are observed, which is often not feasible in practical scenarios. Without additional assumptions about the unobserved variables, it is not possible to recover any causal relationships from observational data. Fortunately, in many applied settings, additional structure among the confounders can be expected. In particular, pervasive confounding is commonly encountered and has been utilized for consistent causal estimation in linear causal models. In this paper, we present a provably consistent method to estimate causal relationships in the non-linear, pervasive confounding setting. The core of our procedure relies on the ability to estimate the confounding variation through a simple spectral decomposition of the observed data matrix. We derive a DAG score function based on this insight, prove its consistency in recovering a correct ordering of the DAG, and empirically compare it to previous approaches. We demonstrate improved performance on both simulated and real datasets by explicitly accounting for both confounders and non-linear effects.
Successive Pruning for Model Compression via Rate Distortion Theory

Neural network (NN) compression has become essential to enable deploying over-parameterized NN models on resource-constrained devices. As a simple and easy-to-implement method, pruning is one of the most established NN compression techniques. Although it is a mature method with more than 30 years of history, there is still a lack of good understanding and systematic analysis of why pruning works well even with aggressive compression ratios. In this work, we answer this question by studying NN compression from an information-theoretic approach and show that rate distortion theory suggests pruning to achieve the theoretical limits of NN compression. Our derivation also provides an end-to-end compression pipeline involving a novel pruning strategy. That is, in addition to pruning the model, we also ﬁnd a minimum-length binary representation of it via entropy coding. Our method consistently outperforms the existing pruning strategies and reduces the pruned model’s size by 2 . 5 times. We evaluate the efﬁcacy of our strategy on MNIST, CIFAR-10 and ImageNet datasets using 5 distinct architectures.
Electronic structure and magnetism in 
UGa2
: 
DFT+DMFT
 approach

The debate whether uranium 5f electrons are closer to being localized or itinerant in the ferromagnetic compound UGa2 is not yet fully settled. The experimentally determined magnetic moments are large, approximately 3 Bohr magnetons, suggesting the localized character of the 5f electrons. In the same time, one can identify signs of itinerant as well as localized behavior in various spectroscopic observations. The band theory, employing local exchange-correlation functionals, is biased toward itinerant 5f states and severely underestimates the moments. Using material-specific dynamical mean-field theory (DMFT), we probe how a less approximate description of electron-electron correlations improves the picture. We present two variants of the theory: starting either from spin-restricted (LDA) or spin-polarized (LSDA) band structure. We show that the L(S)DA+DMFT method can accurately describe the magnetic moments in UGa2 as long as the exchange interaction between the uranium 6d and 5f electrons is preserved by a judicious choice of the spin-polarized double-counting correction. We discuss the computed electronic structure in relation to photoemission experiments and show how the correlations reduce the Sommerfeld coefficient of the electronic specific heat by shifting the 5f states slightly away from the Fermi level.
Automated Identification of Vulnerable Devices in Networks using Traffic Data and Deep Learning

Many IoT devices are vulnerable to attacks due to flawed security designs and lacking mechanisms for firmware updates or patches to eliminate the security vulnerabilities. Device-type identification combined with data from vulnerability databases can pinpoint vulnerable IoT devices in a network and can be used to constrain the communications of vulnerable devices for preventing damage. In this contribution, we present and evaluate two deep learning approaches to the reliable IoT device-type identification, namely a recurrent and a convolutional network architecture. Both deep learning approaches show accuracies of 97% and 98%, respectively, and thereby outperform an up-to-date IoT device-type identification approach using hand-crafted fingerprint features obtaining an accuracy of 82%. The runtime performance for the IoT identification of both deep learning approaches outperforms the hand-crafted approach by three magnitudes. Finally, importance metrics explain the results of both deep learning approaches in terms of the utilization of the analyzed traffic data flow.