Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Graph Metric Learning via Gershgorin Disc Alignment

We propose a fast general projection-free metric learning framework, where the minimization objective minM∈S Q(M) is a convex differentiable function of the metric matrix M, and M resides in the set S of generalized graph Laplacian matrices for connected graphs with positive edge weights and node degrees. Unlike lowrank metric matrices common in the literature, S includes the important positive-diagonal-only matrices as a special case in the limit. The key idea for fast optimization is to rewrite the positive definite cone constraint in S as signal-adaptive linear constraints via Gershgorin disc alignment, so that the alternating optimization of the diagonal and off-diagonal terms in M can be solved efficiently as linear programs via Frank-Wolfe iterations. We prove that the Gershgorin discs can be aligned perfectly using the first eigenvector v of M, which we update iteratively using Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) with warm start as diagonal / off-diagonal terms are optimized. Experiments show that our efficiently computed graph metric matrices outperform metrics learned using competing methods in terms of classification tasks.
Gravitational Wilson lines in 3D de Sitter

: We construct local probes in the static patch of Euclidean dS 3 gravity. These probes are Wilson line operators, designed by exploiting the Chern-Simons formulation of 3D gravity. Our prescription uses non-unitary representations of so (4) ’ su (2) L (cid:2) su (2) R , and we evaluate the Wilson line for states satisfying a singlet condition. We discuss how to reproduce the Green’s functions of massive scalar (cid:12)elds in dS 3 , the construction of bulk (cid:12)elds, and the quasinormal mode spectrum. We also discuss the interpretation of our construction in Lorentzian signature in the in(cid:13)ationary patch, via SL(2 ; C ) Chern-Simons theory.
Unsupervised Program Synthesis for Images using Tree-Structured LSTM

Program synthesis has recently emerged as a promising approach to the image parsing task. However, most prior works have relied on supervised learning methods, which require ground truth programs for each training image. We present an unsupervised learning algorithm that can parse constructive solid geometry (CSG) images into context-free grammar with a non-differentiable renderer. We propose a grammar-encoded tree LSTM to effectively constrain our search space by leveraging the structure of the context-free grammar while handling the non-differentiable renderer via REINFORCE and encouraging the exploration by regularizing the objective with an entropy term. Instead of using simple Monte Carlo sampling, we propose a lower-variance entropy estimator with sampling without replacement for effective exploration. We demonstrate the effectiveness of the proposed algorithm on a synthetic 2D CSG dataset, which outperforms baseline models by a large margin.
Interpreting Cloud Computer Vision Pain-Points: A Mining Study of Stack Overflow

Intelligent services are becoming increasingly more pervasive; application developers want to leverage the latest advances in areas such as computer vision to provide new services and products to users, and large technology firms enable this via RESTful APIs. While such APIs promise an easy-to-integrate on-demand machine intelligence, their current design, documentation and developer interface hides much of the underlying machine learning techniques that power them. Such APIs look and feel like conventional APIs but abstract away data-driven probabilistic behaviour—the implications of a developer treating these APIs in the same way as other, traditional cloud services, such as cloud storage, is of concern. The objective of this study is to determine the various pain-points developers face when implementing systems that rely on the most mature of these intelligent services, specifically those that provide computer vision. We use Stack Overflow to mine indications of the frustrations that developers appear to face when using computer vision services, classifying their questions against two recent classification taxonomies (documentation-related and general questions). We find that, unlike mature fields like mobile development, there is a contrast in the types of questions asked by developers. These indicate a shallow understanding of the underlying technology that empower such systems. We discuss several implications of these findings via the lens of learning taxonomies to suggest how the software engineering community can improve these services and comment on the nature by which developers use them.
LIBTwinSVM: A Library for Twin Support Vector Machines

This paper presents LIBTwinSVM, a free, efficient, and open source library for Twin Support Vector Machines (TSVMs). Our library provides a set of useful functionalities such as fast TSVMs estimators, model selection, visualization, a graphical user interface (GUI) application, and a Python application programming interface (API). The benchmarks results indicate the effectiveness of the LIBTwinSVM library for large-scale classification problems. The source code of LIBTwinSVM library, installation guide, documentation, and usage examples are available at this https URL.
Charm CP: $\Delta A_{CP}$ and Radiative decays

Motivated by the very important discovery of CP violation in charm-decays for the first time, by the LHCb collaboration, the role of nearby resonances such as the scalar $f_0(1710)$ in accounting for the observed CP is discussed. It is suggested that the influence of such a resonance may also explain the long-standing puzzle of such a large breaking of SU(3) seen in its decays. It is also explained that intervention of such resonance(s) will render first principles calculations of $\Delta A_{CP}$ rather difficult. Instead, it is proposed that searches for CP violation in simple radiative final states, such as $D^0 \to \gamma \phi [\to K^+ K^-]$, $D^0 \to l^+ l^- \pi$ etc. has much better chance of theoretical precision studies and therefore theoretical and experimental investigations therein are strongly urged.
Performance Analysis of MIMO K-user Interference Channels with Hardware Impairments

Next generation of wireless systems will employ many antenna branches with low-cost devices and hence, may suffer from severe hardware impairments (HWI) like I/Q imbalance, phase noise, etc. With I/Q imbalance, the received signal is a widely linear transformation of the transmitted signal and noise. Thus, the effective noise may be improper, which means that its real and imaginary parts are not independent and identically distributed. Improper Gaussian signaling (IGS) can improve system performance with improper noise and/or improper interference. This paper studies IGS for multiple-input, multiple-output (MIMO) $K$-user interference channels (IC) with imperfect devices in terms of two performance metrics: achievable rate and energy efficiency (EE). We also define an optimization framework for interference-limited systems when treating interference as noise. This framework obtains a stationary point of any optimization problem in which either the objective function and/or constraints are linear functions of rates. These include the optimization of the rate region, the sum-rate, the EE region, or the global EE, among others. Our numerical results show that IGS can improve the performance of MIMO $K$-user IC with HWI and I/Q imbalance, where its benefits increase with $K$ and the imbalance level and decrease with the number of antennas.
Residual Tangent Kernels

A recent body of work has focused on the theoretical study of neural networks at the regime of large width. Specifically, it was shown that training infinitely-wide and properly scaled vanilla ReLU networks using the L2 loss, is equivalent to kernel regression using the Neural Tangent Kernel (NTK), which is deterministic, and remains constant during training. In this work, we derive the form of the limiting kernel for architectures incorporating bypass connections, namely residual networks (ResNets), as well as to densely connected networks (DenseNets). In addition, we derive finite width and depth corrections for both cases. Our analysis reveals that deep practical residual architectures might operate much closer to the ``kernel regime'' than their vanilla counterparts: while in networks that do not use skip connections, convergence to the NTK requires one to fix depth, while increasing the layers' width. Our findings show that in ResNets, convergence to the NTK may occur when depth and width simultaneously tend to infinity, provided proper initialization. In DenseNets, however, convergence to the NTK as the width tend to infinity is guaranteed, at a rate that is independent of both depth and scale of the weights.
Beyond the Front Page:Measuring Third Party Dynamics in the Field

In the modern Web, service providers often rely heavily on third parties to run their services. For example, they make use of ad networks to finance their services, externally hosted libraries to develop features quickly, and analytics providers to gain insights into visitor behavior. For security and privacy, website owners need to be aware of the content they provide their users. However, in reality, they often do not know which third parties are embedded, for example, when these third parties request additional content as it is common in real-time ad auctions. In this paper, we present a large-scale measurement study to analyze the magnitude of these new challenges. To better reflect the connectedness of third parties, we measured their relations in a model we call third party trees, which reflects an approximation of the loading dependencies of all third parties embedded into a given website. Using this concept, we show that including a single third party can lead to subsequent requests from up to eight additional services. Furthermore, our findings indicate that the third parties embedded on a page load are not always deterministic, as 50 % of the branches in the third party trees change between repeated visits. In addition, we found that 93 % of the analyzed websites embedded third parties that are located in regions that might not be in line with the current legal framework. Our study also replicates previous work that mostly focused on landing pages of websites. We show that this method is only able to measure a lower bound as subsites show a significant increase of privacy-invasive techniques. For example, our results show an increase of used cookies by about 36 % when crawling websites more deeply.
Causal query in observational data with hidden variables

This paper discusses the problem of causal query in observational data with hidden variables, with the aim of seeking the change of an outcome when "manipulating" a variable while given a set of plausible confounding variables which affect the manipulated variable and the outcome. Such an "experiment on data" to estimate the causal effect of the manipulated variable is useful for validating an experiment design using historical data or for exploring confounders when studying a new relationship. However, existing data-driven methods for causal effect estimation face some major challenges, including poor scalability with high dimensional data, low estimation accuracy due to heuristics used by the global causal structure learning algorithms, and the assumption of causal sufficiency when hidden variables are inevitable in data. In this paper, we develop theorems for using local search to find a superset of the adjustment (or confounding) variables for causal effect estimation from observational data under a realistic pretreatment assumption. The theorems ensure that the unbiased estimate of causal effect is obtained in the set of causal effects estimated by the superset of adjustment variables. Based on the developed theorems, we propose a data-driven algorithm for causal query. Experiments show that the proposed algorithm is faster and produces better causal effect estimation than an existing data-driven causal effect estimation method with hidden variables. The causal effects estimated by the algorithm are as good as those by the state-of-the-art methods using domain knowledge.
Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion

Given a small set of seed entities (e.g., “USA”, “Russia”), corpus-based set expansion is to induce an extensive set of entities which share the same semantic class (Country in this example) from a given corpus. Set expansion benefits a wide range of downstream applications in knowledge discovery, such as web search, taxonomy construction, and query suggestion. Existing corpus-based set expansion algorithms typically bootstrap the given seeds by incorporating lexical patterns and distributional similarity. However, due to no negative sets provided explicitly, these methods suffer from semantic drift caused by expanding the seed set freely without guidance. We propose a new framework, Set-CoExpan, that automatically generates auxiliary sets as negative sets that are closely related to the target set of user’s interest, and then performs multiple sets co-expansion that extracts discriminative features by comparing target set with auxiliary sets, to form multiple cohesive sets that are distinctive from one another, thus resolving the semantic drift issue. In this paper we demonstrate that by generating auxiliary sets, we can guide the expansion process of target set to avoid touching those ambiguous areas around the border with auxiliary sets, and we show that Set-CoExpan outperforms strong baseline methods significantly.
Computing commutator length is hard

The commutator length $cl_G(g)$ of an element $g \in [G,G]$ in the commutator subgroup of a group $G$ is the least number of commutators needed to express $g$ as their product. If $G$ is a non-abelian free groups, then given an integer $n \in \mathbb{N}$ and an element $g \in [G,G]$ the decision problem which determines if $cl_G(g) \leq n$ is NP-complete. Thus, unless P=NP, there is no algorithm that computes $cl_G(g)$ in polynomial time in terms of $|g|$, the wordlength of $g$. This statement remains true for groups which have a retract to a non-abelian free group, such as non-abelian right-angled Artin groups. We will show these statements by relating commutator length to the \emph{cyclic block interchange distance} of words, which we also show to be NP-complete.
The origin and evolution of vertebrate neural crest cells

The neural crest is a vertebrate-specific migratory stem cell population that generates a remarkably diverse set of cell types and structures. Because many of the morphological, physiological and behavioural novelties of vertebrates are derived from neural crest cells, it is thought that the origin of this cell population was an important milestone in early vertebrate history. An outstanding question in the field of vertebrate evolutionary-developmental biology (evo-devo) is how this cell type evolved in ancestral vertebrates. In this review, we briefly summarize neural crest developmental genetics in vertebrates, focusing in particular on the gene regulatory interactions instructing their early formation within and migration from the dorsal neural tube. We then discuss how studies searching for homologues of neural crest cells in invertebrate chordates led to the discovery of neural crest-like cells in tunicates and the potential implications this has for tracing the pre-vertebrate origins of the neural crest population. Finally, we synthesize this information to propose a model to explain the origin of neural crest cells. We suggest that at least some of the regulatory components of early stages of neural crest development long pre-date vertebrate origins, perhaps dating back to the last common bilaterian ancestor. These components, originally directing neuroectodermal patterning and cell migration, served as a gene regulatory ‘scaffold' upon which neural crest-like cells with limited migration and potency evolved in the last common ancestor of tunicates and vertebrates. Finally, the acquisition of regulatory programmes controlling multipotency and long-range, directed migration led to the transition from neural crest-like cells in invertebrate chordates to multipotent migratory neural crest in the first vertebrates.
Improving the workplace conditions to successfully attain sustainable development goals

© 2020 Muller Journal of Medical Sciences and Research | Published by Wolters Kluwer Medknow Dear Editor, Occupational hazard is a well-known entity and is a global public health concern, primarily because it accounts for a major proportion of the global disease burden. [1] In fact, more than 2% of the total deaths reported worldwide and a significant percentage of the disease-associated life years have been attributed to the improper working conditions. [2] There are no doubts that the working environment is a crucial determinant of the health and sustainable development, as exposure to harmful working conditions often affects people of low socioeconomic status and living in lowand middle-income nations. [2] It is quite obvious that the presence of adverse workplace settings will account for a poor health outcome and an increase in health expenditure. [2,3]
Dilated cardiomyopathy following H1N1 infection

Acute myocarditis and dilated cardiomyopathy are known to be common manifestations of several viral infections, presenting with a wide spectrum of symptoms. The exact pathogenesis of influenza virus-induced myocarditis is still unclear. Management involves early detection using echocardiography and cardiac support to tide over acute phase. Following recovery, prognosis is known to be quite good; however, fulminant cases have poor prognosis. A 2-year-old female infant previously diagnosed to have H1N1 bronchopneumonia a year back, who was found to have compromised cardiac function suggestive of dilated cardiomyopathy on follow-up and was started on oral antifailure medications and kept on regular follow-up, presented to the emergency department with symptoms suggestive of severe form of dilated cardiomyopathy and succumbed to illness. The association between influenza viral infection and myocarditis is being linked with host immune response as well as direct cellular damage. Vaccination is being said to be the most cost-effective strategy to prevent this potentially fatal complication. Recent-onset cardiac symptomatology with a preceding history suggestive of influenza infection warrants a high index of suspicion for myocarditis and dilated cardiomyopathy. Early detection and prompt cardiac supportive care and management are paramount and help to reduce morbidity and mortality.
Role of Frozen Section in Surgical Management of Ovarian Neoplasm

Objective: Cancers of the adnexae, including ovarian and fallopian tube, constitute the eighth most common cancers among women worldwide. Surgery remains the cornerstone in the management of ovarian cancer. Intraoperative frozen section diagnosis of ovarian tumors is widely used in making this distinction and to decide the course of surgery. Therefore, the accuracy of this technique is very important. The aim was to determine the overall accuracy, sensitivity, specificity, and predictive values of frozen section for ovarian tumors and to evaluate the role of frozen section in the surgical management of ovarian tumors. Materials and Methods: This was a descriptive longitudinal study conducted in the gynecology department of a tertiary care hospital. During the 1 ½ year period of data collection, frozen section was performed among 60 cases of ovarian neoplasms. The overall accuracy, sensitivity, specificity and predictive values of frozen section for benign, borderline and malignant categories of ovarian tumors were studied. Results: Out of the 60 patients of ovarian tumors, frozen section diagnosis showed that 43 (71.7%) tumors were benign, 11 (18.3%) were malignant and 6 (10%) were of borderline nature. Final histopathological diagnosis showed that 45 (75%) tumors were benign, 11 (18.3%) were malignant and 4 (6.7%) were borderline. Frozen section for benign tumors had 95% sensitivity, 100% specificity, 100% positive predictive value (PPV) and 88% negative predictive value (NPV). Malignant tumors had 90% sensitivity, 97% specificity, 90% PPV and 97% NPV with frozen section. However, frozen section had low sensitivity (75%) and PPV (50%) for borderline tumors. Specificity was 94% and NPV 98% in this group of tumors. Conclusion: Frozen section was found to be an accurate and useful modality in the intraoperative evaluation of patients with ovarian neoplasm. The results can help to decide the type and extent of surgery.
Stroke with persisting false-negative diffusion-weighted imagings: A case report

Rationale: Diffusion-weighted imaging (DWI) is a cardinal tool for detecting acute brain ischemia; however, recent studies have shown that DWI may not show the ischemic lesion during the hyperacute period. Patient concerns: A 76-year old male patient was admitted due to an acute onset of dysarthria and right lower paresis which progressed on the second day of admission. Diagnosis: The preliminary diagnosis was an ischemic stroke and the final diagnosis was left medullary stroke. Interventions: Antiaggregant and anticoagulant therapies were initiated after excluding hemorrhagic stroke by cranial computed tomography. However, DWIs, which were recorded on the 6th hour and 19th hour after the symptom onset, were in normal ranges. Hence, the patient was referred to a senior hospital for further etiological investigations. Outcome: The patient was discharged after treatments with aspirin and atorvastatin, and the neurological examination showed mildmoderate dysarthria and moderate right-sided paralysis (3/5). Lessons: Clinicians should pay more attention to stroke patients with negative DWI and perform DWI several times when the diagnostic is unclear.
Editorial: Neurotransmitters and Emotions

1 Institute of Brain and Psychological Science, Sichuan Normal University, Chengdu, China, Department of Psychology, Beijing University, Beijing, China, Department of Psychology, Shandong University, Jinan, China, Department of Psychology, National University of Singapore, Singapore, Singapore, Department of Neurosurgery, Baylor Scott & White Health Center, Temple, TX, United States, Department of Surgery, Texas A&M University, Temple, TX, United States
Deep Eutectic Solvent Pretreatment of Transgenic Biomass With Increased C6C1 Lignin Monomers

The complex and heterogeneous polyphenolic structure of lignin confers recalcitrance to plant cell walls and challenges biomass processing for agroindustrial applications. Recently, significant efforts have been made to alter lignin composition to overcome its inherent intractability. In this work, to overcome technical difficulties related to biomass recalcitrance, we report an integrated strategy combining biomass genetic engineering with a pretreatment using a bio-derived deep eutectic solvent (DES). In particular, we employed biomass from an Arabidopsis line that expressed a bacterial hydroxycinnamoyl-CoA hydratase-lyase (HCHL) in lignifying tissues, which results in the accumulation of unusual C6C1 lignin monomers and a slight decrease in lignin molecular weight. The transgenic biomass was pretreated with renewable DES that can be synthesized from lignin-derived phenols. Biomass from the HCHL plant line containing C6C1 monomers showed increased pretreatment efficiency and released more fermentable sugars up to 34% compared to WT biomass. The enhanced biomass saccharification of the HCHL line is likely due to a reduction of lignin recalcitrance caused by the overproduction of C6C1 aromatics that act as degree of polymerization (DP) reducers and higher chemical reactivity of lignin structures with such C6C1 aromatics. Overall, our findings demonstrate that strategic plant genetic engineering, along with renewable DES pretreatment, could enable the development of sustainable biorefinery.