Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Pruning via Transformable Architecture Search

Network pruning reduces the computation costs of an over-parameterized network without performance damage. Prevailing pruning algorithms pre-define the width and depth of the pruned networks, and then transfer parameters from the unpruned network to pruned networks. To break the structure limitation of the pruned networks, we propose to apply neural architecture search to search directly for a network with flexible channel and layer sizes. The number of the channels/layers is learned by minimizing the loss of the pruned networks. The feature map of the pruned network is an aggregation of K feature map fragments (generated by K networks of different sizes), which are sampled based on the probability distribution.The loss can be back-propagated not only to the network weights, but also to the parameterized distribution to explicitly tune the size of the channels/layers. Specifically, we apply channel-wise interpolation to keep the feature map with different channel sizes aligned in the aggregation procedure. The maximum probability for the size in each distribution serves as the width and depth of the pruned network, whose parameters are learned by knowledge transfer, e.g., knowledge distillation, from the original networks. Experiments on CIFAR-10, CIFAR-100 and ImageNet demonstrate the effectiveness of our new perspective of network pruning compared to traditional network pruning algorithms. Various searching and knowledge transfer approaches are conducted to show the effectiveness of the two components. Code is at: this https URL.
One-Bit LFMCW Radar: Spectrum Analysis and Target Detection

One-bit radar, performing signal sampling and quantization by a 1-bit analog-to-digital converter, is a promising technology for many civilian applications due to its low-cost and low-power consumptions. In this article, problems encountered by a 1-bit linear-frequency-modulated continuous-wave (LFMCW) radar are studied, and a two-stage target detection method termed as the dimension-reduced generalized approximate message passing (DR-GAMP) approach is proposed. First, the spectrum of 1-bit quantized signals in a scenario with multiple targets is analyzed. It is indicated that high-order harmonics may result in false alarms and cannot be neglected. Second, based on the spectrum analysis, the DR-GAMP approach is proposed to carry out target detection. Specifically, linear preprocessing methods and target predetection are first adopted to perform the dimension reduction, and then, the generalized approximate message passing algorithm is utilized to suppress high-order harmonics and recover true targets. Finally, numerical simulations are conducted to evaluate the performance of the 1-bit LFMCW radar under typical parameters. It is shown that compared to the conventional radar applying linear processing methods, the 1-bit LFMCW radar has about 1.3-dB performance gain when the input signal-to-noise ratios of targets are low. In the presence of a strong target, it has about 1.0-dB performance loss.
Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal

Reading Comprehension has received significant attention in recent years as high quality Question Answering (QA) datasets have become available. Despite state-of-the-art methods achieving strong overall accuracy, Multi-Hop (MH) reasoning remains particularly challenging. To address MH-QA specifically, we propose a Deep Reinforcement Learning based method capable of learning sequential reasoning across large collections of documents so as to pass a query-aware, fixed-size context subset to existing models for answer extraction. Our method is comprised of two stages: a linker, which decomposes the provided support documents into a graph of sentences, and an extractor, which learns where to look based on the current question and already-visited sentences. The result of the linker is a novel graph structure at the sentence level that preserves logical flow while still allowing rapid movement between documents. Importantly, we demonstrate that the sparsity of the resultant graph is invariant to context size. This translates to fewer decisions required from the Deep-RL trained extractor, allowing the system to scale effectively to large collections of documents. The importance of sequential decision making in the document traversal step is demonstrated by comparison to standard IE methods, and we additionally introduce a BM25-based IR baseline that retrieves documents relevant to the query only. We examine the integration of our method with existing models on the recently proposed QAngaroo benchmark and achieve consistent increases in accuracy across the board, as well as a 2-3x reduction in training time.
H0LiCOW XII. Lens mass model of WFI2033 − 4723 and blind measurement of its time-delay distance and H0

We present the lens mass model of the quadruply-imaged gravitationally lensed quasar WFI2033 − 4723, and perform a blind cosmographical analysis based on this system. Our analysis combines (1) time-delay measurements from 14 yr of data obtained by the COSmological MOnitoring of GRAvItational Lenses (COSMOGRAIL) collaboration, (2) high-resolution Hubble Space Telescope imaging, (3) a measurement of the velocity dispersion of the lens galaxy based on ESO-MUSE data, and (4) multi-band, wide-field imaging and spectroscopy characterizing the lens environment. We account for all known sources of systematics, including the influence of nearby perturbers and complex line-of-sight structure, as well as the parametrization of the light and mass profiles of the lensing galaxy. After unblinding, we determine the effective time-delay distance to be $4784_{-248}^{+399}~\mathrm{Mpc}$, an average precision of $6.6{{\ \rm per\ cent}}$. This translates to a Hubble constant $H_{0} = 71. 6_{-4.9}^{+3.8}~\mathrm{km~s^{-1}~Mpc^{-1}}$, assuming a flat ΛCDM cosmology with a uniform prior on Ωm in the range [0.05, 0.5]. This work is part of the H0 Lenses in COSMOGRAIL’s Wellspring (H0LiCOW) collaboration, and the full time-delay cosmography results from a total of six strongly lensed systems are presented in a companion paper (H0LiCOW XIII).
Learning to Prove Theorems via Interacting with Proof Assistants

Humans prove theorems by relying on substantial high-level reasoning and problem-specific insights. Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics. However, human experts have to construct proofs manually by entering tactics into the proof assistant. In this paper, we study the problem of using machine learning to automate the interaction with proof assistants. We construct CoqGym, a large-scale dataset and learning environment containing 71K human-written proofs from 123 projects developed with the Coq proof assistant. We develop ASTactic, a deep learning-based model that generates tactics as programs in the form of abstract syntax trees (ASTs). Experiments show that ASTactic trained on CoqGym can generate effective tactics and can be used to prove new theorems not previously provable by automated methods. Code is available at this https URL.
The Research Purpose, Methods and Results of the "Annual Report for International Citations of China's Academic Journals"

Before 2012, it was hard to come to a comprehensive evaluation of academic journals in China. For this reason the international influence of journals published in China hadn’t been paid enough attention, leading to a bias in the Chinese research assessment system. Since 2012, China National Knowledge Infrastructure (CNKI) invested and carried out the project of the development of the “Annual Report for International Citations of Chinese Academic Journals ”. In the same year, CNKI made a comprehensive study on the international citations of more than 6000 journals in China, and found that some journals had a certain international influence. In order to make a comprehensive assessment of the international influence of those journals, CNKI has developed a comprehensive indicator, named the CI index (clout index), combining the effect of both the impact factor and the citation counts. This article describes the purpose, methods and results of part of this project, providing a fresh idea for a comprehensive evaluation of the influence of Chinese academic journals. Conference Topic Methods and techniques
A next-generation inverse-geometry spallation-driven ultracold neutron source

The physics model of a next-generation spallation-driven high-current ultracold neutron (UCN) source capable of delivering an extracted UCN rate of around an-order-of-magnitude higher than the strongest proposed sources, and around three-orders-of-magnitude higher than existing sources, is presented. This UCN-current-optimized source would dramatically improve cutting-edge UCN measurements that are currently statistically limited. A novel "Inverse Geometry" design is used with 40 L of superfluid $^4$He (He-II), which acts as a converter of cold neutrons (CNs) to UCNs, cooled with state-of-the-art sub-cooled cryogenic technology to $\sim$1.6 K. Our design is optimized for a 100 W maximum heat load constraint on the He-II and its vessel. In our geometry, the spallation target is wrapped symmetrically around the UCN converter to permit raster scanning the proton beam over a relatively large volume of tungsten spallation target to reduce the demand on the cooling requirements, which makes it reasonable to assume that water edge-cooling only is sufficient. Our design is refined in several steps to reach $P_{UCN}=2.1\times10^9\,/$s under our other restriction of 1 MW maximum available proton beam power. We then study effects of the He-II scattering kernel as well as reductions in $P_{UCN}$ due to pressurization to reach $P_{UCN}=1.8\times10^9\,/$s. Finally, we provide a design for the UCN extraction system that takes into account the required He-II heat transport properties and implementation of a He-II containment foil that allows UCN transmission. We estimate a total useful UCN current from our source of $R_{use}=5\times10^8\,/$s from a 18 cm diameter guide 5 m from the source. Under a conservative "no return" approximation, this rate can produce an extracted density of $>1\times10^4\,/$cm$^3$ in $<$1000~L external experimental volumes with a $^{58}$Ni (335 neV) cut-off potential.
Pose estimator and tracker using temporal flow maps for limbs

For human pose estimation in videos, it is significant how to use temporal information between frames. In this paper, we propose temporal flow maps for limbs (TML) and a multi-stride method to estimate and track human poses. The proposed temporal flow maps are unit vectors describing the limbs’ movements. We constructed a network to learn both spatial information and temporal information end-to-end. Spatial information such as joint heatmaps and part affinity fields is regressed in the spatial network part, and the TML is regressed in the temporal network part. We also propose a data augmentation method to learn various types of TML better. The proposed multi-stride method expands the data by randomly selecting two frames within a defined range. We demonstrate that the proposed method efficiently estimates and tracks human poses on the PoseTrack 2017 and 2018 datasets.
Identification of type II toxin-antitoxin modules in Burkholderia pseudomallei.

Type II toxin-antitoxin (TA) systems are believed to be widely distributed amongst bacteria although their biological functions are not clear. We have identified eight candidate TA systems in the genome of the human pathogen Burkholderia pseudomallei. Five of these were located in genome islands. Of the candidate toxins, BPSL0175 (RelE1) or BPSS1060 (RelE2) caused growth to cease when expressed in Escherichia coli, whereas expression of BPSS0390 (HicA) or BPSS1584 (HipA) (in an E. coli ΔhipBA background) caused a reduction in the number of culturable bacteria. The cognate antitoxins could restore growth and culturability of cells.
Binary Classification with Bounded Abstention Rate

We consider the problem of binary classification with abstention in the relatively less studied \emph{bounded-rate} setting. We begin by obtaining a characterization of the Bayes optimal classifier for an arbitrary input-label distribution $P_{XY}$. Our result generalizes and provides an alternative proof for the result first obtained by \cite{chow1957optimum}, and then re-derived by \citet{denis2015consistency}, under a continuity assumption on $P_{XY}$. We then propose a plug-in classifier that employs unlabeled samples to decide the region of abstention and derive an upper-bound on the excess risk of our classifier under standard \emph{Holder smoothness} and \emph{margin} assumptions. Unlike the plug-in rule of \citet{denis2015consistency}, our constructed classifier satisfies the abstention constraint with high probability and can also deal with discontinuities in the empirical cdf. We also derive lower-bounds that demonstrate the minimax near-optimality of our proposed algorithm. To address the excessive complexity of the plug-in classifier in high dimensions, we propose a computationally efficient algorithm that builds upon prior work on convex loss surrogates, and obtain bounds on its excess risk in the \emph{realizable} case. We empirically compare the performance of the proposed algorithm with a baseline on a number of UCI benchmark datasets.
Approximately Maximizing the Broker's Profit in a Two-sided Market

We study how to maximize the broker's (expected) profit in a two-sided market, where she buys items from a set of sellers and resells them to a set of buyers. Each seller has a single item to sell and holds a private value on her item, and each buyer has a valuation function over the bundles of the sellers' items. We consider the Bayesian setting where the agents' values/valuations are independently drawn from prior distributions, and aim at designing dominant-strategy incentive-compatible (DSIC) mechanisms that are approximately optimal. Production-cost markets, where each item has a publicly-known cost to be produced, provide a platform for us to study two-sided markets. Briefly, we show how to covert a mechanism for production-cost markets into a mechanism for the broker, whenever the former satisfies cost-monotonicity. This reduction holds even when buyers have general combinatorial valuation functions. When the buyers' valuations are additive, we generalize an existing mechanism to production-cost markets in an approximation-preserving way. We then show that the resulting mechanism is cost-monotone and thus can be converted into an 8-approximation mechanism for two-sided markets.
Design Dimensions for Software Certification: A Grounded Analysis

In many domains, software systems cannot be deployed until authorities judge them fit for use in an intended operating environment. Certification standards and processes have been devised and deployed to regulate operations of software systems and prevent their failures. However, practitioners are often unsatisfied with the efficiency and value proposition of certification efforts. In this study, we compare two certification standards, Common Criteria and DO-178C, and collect insights from literature and from interviews with subject-matter experts to identify design options relevant to the design of standards. The results of the comparison of certification efforts---leading to the identification of design dimensions that affect their quality---serve as a framework to guide the comparison, creation, and revision of certification standards and processes. This paper puts software engineering research in context and discusses key issues around process and quality assurance and includes observations from industry about relevant topics such as recertification, timely evaluations, but also technical discussions around model-driven approaches and formal methods. Our initial characterization of the design space of certification efforts can be used to inform technical discussions and to influence the directions of new or existing certification efforts. Practitioners, technical commissions, and government can directly benefit from our analytical framework.
Nullspace Structure in Model Predictive Control

Robotic tasks can be accomplished by exploiting different forms of redundancies. This work focuses on planning redundancy within Model Predictive Control (MPC) in which several paths can be considered within the MPC time horizon. We present the nullspace structure in MPC with a quadratic approximation of the cost and a linearization of the dynamics. We exploit the low rank structure of the precision matrices used in MPC (encapsulating spatiotemporal information) to perform hierarchical task planning, and show how nullspace computation can be treated as a fusion problem (computed with a product of Gaussian experts). We illustrate the approach using proof-of-concept examples with point mass objects and simulated robotics applications.
Leveraging Uncertainty in Deep Learning for Selective Classification

The wide and rapid adoption of deep learning by practitioners brought unintended consequences in many situations such as in the infamous case of Google Photos' racist image recognition algorithm; thus, necessitated the utilization of the quantified uncertainty for each prediction. There have been recent efforts towards quantifying uncertainty in conventional deep learning methods (e.g., dropout as Bayesian approximation); however, their optimal use in decision making is often overlooked and understudied. In this study, we propose a mixed-integer programming framework for classification with reject option (also known as selective classification), that investigates and combines model uncertainty and predictive mean to identify optimal classification and rejection regions. Our results indicate superior performance of our framework both in non-rejected accuracy and rejection quality on several publicly available datasets. Moreover, we extend our framework to cost-sensitive settings and show that our approach outperforms industry standard methods significantly for online fraud management in real-world settings.
Evidence-based retrieval in evidence-based medicine.

OBJECTIVE Clinical decisions based on a meta-analysis that is based on an ineffective retrieval strategy may have serious negative consequences for patients. The study objective was to investigate the extent to which meta-analyses report proof of their retrieval strategies' effectiveness. METHODS The authors examined a random sample (n = 100) of articles in the 1996 to 2002 full-text subset of Ovid MEDLINE indexed as "meta-analysis. " We classified the articles in three ways: the article (A) reported both a retrieval strategy in sufficient detail (such that it could be repeated) and with evidence of the strategy's effectiveness, (B) reported a retrieval strategy in sufficient detail but not with evidence of the strategy's effectiveness, or (C) neither reported a strategy in detail nor evidence of the strategy's effectiveness. Articles classified as (A) were further classified according to the level of evidence reported. RESULTS Of the eighty-nine articles in our final analysis, six (6.7%) were classified as category (A), fifty-seven (64%) as (B), and twenty-six (29%) as (C). Articles in category (A) reported a previously validated search, a published strategy, or strategy based on expert opinion. CONCLUSION Peer-review standards must be developed that require authors of meta-analyses to report evidence for the effectiveness of their retrieval strategies.
Misspelling Oblivious Word Embeddings

In this paper we present a method to learn word embeddings that are resilient to misspellings. Existing word embeddings have limited applicability to malformed texts, which contain a non-negligible amount of out-of-vocabulary words. We propose a method combining FastText with subwords and a supervised task of learning misspelling patterns. In our method, misspellings of each word are embedded close to their correct variants. We train these embeddings on a new dataset we are releasing publicly. Finally, we experimentally show the advantages of this approach on both intrinsic and extrinsic NLP tasks using public test sets.
Aspartyl Protease-Mediated Cleavage of BAG6 Is Necessary for Autophagy and Fungal Resistance in Plants[OPEN]

BAG6 processing confers plant immunity via autophagy. The Bcl-2-associated athanogene (BAG) family is an evolutionarily conserved group of cochaperones that modulate numerous cellular processes. Previously we found that Arabidopsis thaliana BAG6 is required for basal immunity against the fungal phytopathogen Botrytis cinerea. However, the mechanisms by which BAG6 controls immunity are obscure. Here, we address this important question by determining the molecular mechanisms responsible for BAG6-mediated basal resistance. We show that Arabidopsis BAG6 is cleaved in vivo in a caspase-1-like-dependent manner and via a combination of pull-downs, mass spectrometry, yeast two-hybrid assays, and chemical genomics, we demonstrate that BAG6 interacts with a C2 GRAM domain protein (BAGP1) and an aspartyl protease (APCB1), both of which are required for BAG6 processing. Furthermore, fluorescence and transmission electron microscopy established that BAG6 cleavage triggers autophagy in the host that coincides with disease resistance. Targeted inactivation of BAGP1 or APCB1 results in the blocking of BAG6 processing and loss of resistance. Mutation of the cleavage site blocks cleavage and inhibits autophagy in plants; disease resistance is also compromised. Taken together, these results identify a mechanism that couples an aspartyl protease with a molecular cochaperone to trigger autophagy and plant defense, providing a key link between fungal recognition and the induction of cell death and resistance.
A new secure multi-hop untrusted relaying scheme

Cooperative relaying is utilized as an efficient method for data communication in wireless sensor networks and Internet of Things (IoT). However, sometimes due to the necessity of multi-hop relaying in such communication networks, it is challenging to guarantee the secrecy of cooperative transmissions when the relays may themselves be eavesdroppers, i.e., we may face with the untrusted relaying scenario where the relays are both necessary helpers and potential eavesdroppers. To obviate this issue, a new cooperative jamming scheme is proposed in this paper, in which the data can be confidentially communicated from the source to the destination through multi-hop untrusted relays. Toward this end, we first consider a two successive untrusted relaying network, i.e, a three-hop communication network. In our proposed secure transmission scheme, all the legitimate nodes contribute to provide secure communication by smartly injecting artificial noises to the network in different communication phases. Given this system model, a novel closed-form expression is presented in the high signal-to-noise ratio (SNR) regime for the ergodic secrecy rate (ESR). Furthermore, we evaluate the high SNR slope and power offset of the ESR to gain a basic comparison of the proposed secure transmission scheme and the state-of-arts. Our numerical results highlight that the proposed secure transmission scheme provides better secrecy rate compared with the two-hop untrusted relaying scheme as well as the direct transmission scheme.