Miracle-dz/UniFAR-FTUs huggingface dataset:
Synergistic effect of nikkomycin Z with caspofungin and micafungin against Candida albicans and Candida parapsilosis biofilms

Antifungal lock therapy has received significant interest in the last few years because the frequently usage of intravascular devices is associated with an increasing number of catheter‐related bloodstream infections caused by Candida species. Antifungal combinations with synergistic interaction can be a good choice for antifungal lock therapy; therefore, interactions were examined between two echinocandins (caspofungin and micafungin) and the chitin synthesis inhibitor nikkomycin Z against Candida albicans and C. parapsilosis biofilms. Susceptibility was evaluated using the XTT‐based checkerboard microdilution method, while the nature of interactions was assessed by calculating fractional inhibitory concentration indices and using the Bliss independence model. Mathematic‐based evaluations were supplemented with fluorescent LIVE/DEAD viability assay. The results obtained by statistical interaction analyses correlated well with the viability assay. The tested echinocandins with nikkomycin Z caused an extended cell death and the structure of the biofilm was sparse compared to the control, especially for C. albicans. The findings support the simultaneous usage of nikkomycin Z and caspofungin or micafungin in alternative therapies such as the antifungal lock therapy.
A phase II study to assess the safety and efficacy of the dual mTORC1/2 inhibitor vistusertib in relapsed, refractory DLBCL

Patients with relapsed or refractory diffuse large B‐cell lymphoma (DLBCL) who are unfit for or relapsed postautologous stem‐cell transplantation have poor outcomes. Historically, mTORC1 inhibitors have produced responses in approximately 30% of patients in this setting. mTORC1 inhibitor efficacy may be limited by resistance mechanisms including AKT activation by mTORC2. To date, dual mTORC1/2 inhibitors targeting both the TORC1 and TORC2 complexes have not been investigated in DLBCL. This phase II trial investigated the oral dual mTORC1/2 inhibitor vistusertib in an intermittent dosing schedule of 125 mg b.d. for 2 days per week. Thirty patients received vistusertib and six received vistusertib‐rituximab for up to six cycles (28‐day cycles). Two partial responses were achieved on monotherapy. Durations of response were 57 and 62 days, respectively, for these patients. 19% had stable disease within six cycles. In the monotherapy arm, the median progression‐free survival was1.69 (95% confidence interval [CI] 1.61‐2.14) months and median overall survival was 6.58 (95% CI 3.81‐not reached) months, respectively. The median duration of response or stable disease across the trial duration was 153 days (95% CI 112‐not reached). Tumour responses according to positron emission tomography/computed tomography versus computed tomography were concordant. There were no differences noted in tumour volume response according to cell of origin by either gene expression profiling or immunohistochemistry. Vistusertib ± rituximab was well tolerated; across 36 patients 86% of adverse events were grade (G) 1‐2. Common vistusertib‐related adverse events were similar to those described with mTORC1 inhibitors: nausea (47% G1‐2), diarrhoea (27% G1‐2, 6% G3), fatigue (30% G1‐2, 3% G3), mucositis (25% G1‐2, 6% G3), vomiting (17% G1‐2), and dyspepsia (14% G1‐2). Dual mTORC1/2 inhibitors do not clearly confer an advantage over mTORC1 inhibitors in relapsed or refractory DLBCL. Potential resistance mechanisms are discussed within.
Heterogeneous porous scaffold generation in trivariate B-spline solid with triply periodic minimal surface in the parametric domain

A porous scaffold is a three-dimensional network structure composed of a large number of pores, and triply periodic minimal surfaces (TPMSs) are one of conventional tools for designing porous scaffolds. However, discontinuity, incompleteness, and high storage space requirements are the three main shortcomings of TPMSs for porous scaffold design. In this study, we developed an effective method for heterogeneous porous scaffold generation to overcome the abovementioned shortcomings of TPMSs. The input of the proposed method is a trivariate B-spline solid (TBSS) with a cubic parameter domain. The proposed method first constructs a threshold distribution field (TDF) in the cubic parameter domain, and then produces a continuous and complete TPMS within it. Moreover, by mapping the TPMS in the parametric domain to the TBSS, a continuous and complete porous scaffold is generated in the TBSS. In addition, if the TBSS does not satisfy engineering requirements, the TDF can be locally modified in the parameter domain, and the porous scaffold in the TBSS can be rebuilt. We also defined a new storage space-saving file format based on the TDF to store porous scaffolds. The experimental results presented in this paper demonstrate the effectiveness and efficiency of the method using a TBSS as well as the superior space-saving of the proposed storage format.
Free-Space Features: Global Localization in 2D Laser SLAM Using Distance Function Maps

In many applications, maintaining a consistent map of the environment is key to enabling robotic platforms to perform higher-level decision making. Detection of already visited locations is one of the primary ways in which map consistency is maintained, especially in situations where external positioning systems are unavailable or unreliable. Mapping in 2D is an important field in robotics, largely due to the fact that man-made environments such as warehouses and homes, where robots are expected to play an increasing role, can often be approximated as planar. Place recognition in this context remains challenging: 2D lidar scans contain scant information with which to characterize, and therefore recognize, a location. This paper introduces a novel approach aimed at addressing this problem. At its core, the system relies on the use of the distance function for representation of geometry. This representation allows extraction of features which describe the geometry of both surfaces and free-space in the environment. We propose a feature for this purpose. Through evaluations on public datasets, we demonstrate the utility of free-space in the description of places, and show an increase in localization performance over a state-of-the-art descriptor extracted from surface geometry.
Supermassive stars as the origin of the multiple populations in globular clusters

Abstract Globular clusters (GCs) display anomalous light-elements abundances (HeCNONaMgAl), resembling the yields of hot-hydrogen burning, but there is no consensus yet on the origin of these ubiquitous multiple populations. We present a model in which a super-massive star (SMS, ≳103 M⊙) forms via stellar collisions during GC formation and pollutes the intra-cluster medium. The growth of the SMS finds a balance with the wind mass loss rate, such that the SMS can produce a significant fraction of the total GC mass in processed material, thereby overcoming the so-called mass-budget problem that plagues other models. Because of continuous rejuvenation, the SMS acts as a ‘conveyer-belt’ of hot-hydrogen burning yields with (relatively) low He abundances, in agreement with empirical constraints. Additionally, the amount of processed material per unit of GC mass correlates with GC mass, addressing the specific mass budget problem. We discuss uncertainties and tests of this new self-enrichment scenario.
Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization

This paper tackles the problem of large-scale image-based localization (IBL) where the spatial location of a query image is determined by finding out the most similar reference images in a large database. For solving this problem, a critical task is to learn discriminative image representation that captures informative information relevant for localization. We propose a novel representation learning method having higher location-discriminating power. It provides the following contributions: 1) we represent a place (location) as a set of exemplar images depicting the same landmarks and aim to maximize similarities among intra-place images while minimizing similarities among inter-place images; 2) we model a similarity measure as a probability distribution on L_2-metric distances between intra-place and inter-place image representations; 3) we propose a new Stochastic Attraction and Repulsion Embedding (SARE) loss function minimizing the KL divergence between the learned and the actual probability distributions; 4) we give theoretical comparisons between SARE, triplet ranking and contrastive losses. It provides insights into why SARE is better by analyzing gradients. Our SARE loss is easy to implement and pluggable to any CNN. Experiments show that our proposed method improves the localization performance on standard benchmarks by a large margin. Demonstrating the broad applicability of our method, we obtained the third place out of 209 teams in the 2018 Google Landmark Retrieval Challenge. Our code and model are available at https://github.com/Liumouliu/deepIBL.
Do Not Return Similarity: Face Recovery with Distance

Machine Learning (ML) already has been integrated into all kinds of systems, helping developers to solve problems with even higher accuracy than human beings. However, when integrating ML models into a system, developers may accidentally take not enough care of the outputs of ML models, mainly because of their unfamiliarity with ML and AI, resulting in severe consequences like hurting data owners' privacy. In this work, we focus on understanding the risks of abusing embeddings of ML models, an important and popular way of using ML. To show the consequence, we reveal several kinds of channels in which embeddings are accidentally leaked. As our study shows, a face verification system deployed by a government organization leaking only distance to authentic users allows an attacker to exactly recover the embedding of the verifier's pre-installed photo. Further, as we discovered, with the leaked embedding, attackers can easily recover the input photo with negligible quality losses, indicating devastating consequences to users' privacy. This is achieved with our devised GAN-like structure model, which showed 93.65% success rate on popular face embedding model under black box assumption.
Finite-Horizon Optimal Control of Boolean Control Networks: A Unified Graph-Theoretical Approach

This article investigates the finite-horizon optimal control (FHOC) problem of Boolean control networks (BCNs) from a graph theory perspective. We first formulate two general problems to unify various special cases studied in the literature: 1) the horizon length is a priori fixed and 2) the horizon length is unspecified but finite for given destination states. Notably, both problems can incorporate time-variant costs, which are rarely considered in existing work, and a variety of constraints. The existence of an optimal control sequence is analyzed under mild assumptions. Motivated by BCNs’ finite state space and control space, we approach the two general problems intuitively and efficiently under a graph-theoretical framework. A weighted state transition graph and its time-expanded variants are developed, and the equivalence between the FHOC problem and the shortest-path (SP) problem in specific graphs is established rigorously. Two algorithms are developed to find the SP and construct the optimal control sequence for the two problems with reduced computational complexity, though technically, a classical SP algorithm in graph theory is sufficient for all problems. Compared with existing algebraic methods, our graph-theoretical approach can achieve state-of-the-art time efficiency while targeting the most general problems. Furthermore, our approach is the first one capable of solving Problem 2) with time-variant costs. Finally, a genetic network in the bacterium E. coli and a signaling network involved in human leukemia are used to validate the effectiveness of our approach. The results of two common tasks for both networks show that our approach can dramatically reduce the running time. Python implementation of our algorithms is available at GitHub https://github.com/ShuhuaGao/FHOC.
Geometric Sample Reweighting for Monte Carlo Integration

Numerical integration is fundamental in multiple Monte Carlo rendering problems. We present a sample reweighting scheme, including underlying theory, and analysis of numerical performance for the integration of an unknown one‐dimensional function. Our method is simple to implement and builds upon the insight to link the weights to a function reconstruction process during integration. We provide proof that our solution is unbiased in one‐dimensional cases and consistent in multi‐dimensional cases. We illustrate its effectiveness in several use cases.
The velocity field of the Lyra complex

Context. The formation of cosmic structures culminates with the assembly of galaxy clusters, a process that is quite different from cluster to cluster. Aims. We present the study of the structure and dynamics of the Lyra complex, consisting of the two clusters RXC J1825.3+3026 and CIZA J1824.1+3029, which was very recently studied by using both X-ray and radio data. Methods. This is the first analysis based on the kinematics of member galaxies. New spectroscopic data for 285 galaxies were acquired at the Italian Telescopio Nazionale Galileo and were used in combination with PanSTARRS photometry. The result of our member selection is a sample of 198 galaxies. Results. For RXCJ1825 and CIZAJ1824 we report the redshifts, z = 0.0645 and z = 0.0708, the first estimates of velocity dispersion, σv = 995+131−125 km s−1 and σv = 700 ± 50 km s−1, and of dynamical mass, M200 = 1.1 ± 0.4 × 1015 M⊙ and M200 = 4 ± 0.1 × 1014 M⊙. The past assembly of RXCJ1825 is traced by the two dominant galaxies, which are both aligned with the major axis of the galaxy distribution along the east–west direction, and by a minor northeast substructure. We also detect a quite peculiar high velocity field in the southwest region of the Lyra complex. This feature is likely related to a very luminous galaxy, which is characterized by a high velocity. This galaxy is suggested to be the central galaxy of a group that is in interaction with RXCJ1825 according to very recent studies based on X-ray and radio data. The redshift of the whole Lyra complex is z = 0.067. Assuming that the redshift difference between RXCJ1825 and CIZAJ1824 is due to the relative kinematics, the projected distance between the cluster centers is D ∼ 1.3 Mpc and the line–of–sight velocity difference is ∼1750 km s−1. A dynamical analysis of the system shows that the two clusters are likely to be gravitationally bound in a pre-merger phase, and that CIZAJ1824 is moving toward RXCJ1825. Conclusions. Our results corroborate a picture where the Lyra region is the place of a very complex scenario of cluster assembly.
Semilocal exchange-correlation potentials for solid-state calculations: Current status and future directions

Kohn-Sham (KS) density functional theory (DFT) is a very efficient method for calculating various properties of solids as, for instance, the total energy, the electron density, or the electronic band structure. The KS-DFT method leads to rather fast calculations, however the accuracy depends crucially on the chosen approximation for the exchange and correlation (xc) functional $E_{\text{xc}}$ and/or potential $v_{\text{xc}}$. Here, an overview of xc methods to calculate the electronic band structure is given, with the focus on the so-called semilocal methods that are the fastest in KS-DFT and allow to treat systems containing up to thousands of atoms. Among them, there is the modified Becke-Johnson potential that is widely used to calculate the fundamental band gap of semiconductors and insulators. The accuracy for other properties like the magnetic moment or the electron density, that are also determined directly by $v_{\text{xc}}$, is also discussed.
Sparse hierarchical representation learning on molecular graphs

Architectures for sparse hierarchical representation learning have recently been proposed for graph-structured data, but so far assume the absence of edge features in the graph. We close this gap and propose a method to pool graphs with edge features, inspired by the hierarchical nature of chemistry. In particular, we introduce two types of pooling layers compatible with an edge-feature graph-convolutional architecture and investigate their performance for molecules relevant to drug discovery on a set of two classification and two regression benchmark datasets of MoleculeNet. We find that our models significantly outperform previous benchmarks on three of the datasets and reach state-of-the-art results on the fourth benchmark, with pooling improving performance for three out of four tasks, keeping performance stable on the fourth task, and generally speeding up the training process.
TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems

A trojan backdoor is a hidden pattern typically implanted in a deep neural network. It could be activated and thus forces that infected model behaving abnormally only when an input data sample with a particular trigger present is fed to that model. As such, given a deep neural network model and clean input samples, it is very challenging to inspect and determine the existence of a trojan backdoor. Recently, researchers design and develop several pioneering solutions to address this acute problem. They demonstrate the proposed techniques have a great potential in trojan detection. However, we show that none of these existing techniques completely address the problem. On the one hand, they mostly work under an unrealistic assumption (e.g. assuming availability of the contaminated training database). On the other hand, the proposed techniques cannot accurately detect the existence of trojan backdoors, nor restore high-fidelity trojan backdoor images, especially when the triggers pertaining to the trojan vary in size, shape and position. In this work, we propose TABOR, a new trojan detection technique. Conceptually, it formalizes a trojan detection task as a non-convex optimization problem, and the detection of a trojan backdoor as the task of resolving the optimization through an objective function. Different from the existing technique also modeling trojan detection as an optimization problem, TABOR designs a new objective function--under the guidance of explainable AI techniques as well as heuristics--that could guide optimization to identify a trojan backdoor in a more effective fashion. In addition, TABOR defines a new metric to measure the quality of a trojan backdoor identified. Using an anomaly detection method, we show the new metric could better facilitate TABOR to identify intentionally injected triggers in an infected model and filter out false alarms.
PROV-DM: The PROV Data Model

Provenance is information about entities, activities, and people involved in producing a piece of data or thing, which can be used to form assessments about its quality, reliability or trustworthiness. PROV-DM is the conceptual data model that forms a basis for the W3C provenance (PROV) family of specifications. PROV-DM distinguishes core structures, forming the essence of provenance information, from extended structures catering for more specific uses of provenance. PROV-DM is organized in six components, respectively dealing with: (1) entities and activities, and the time at which they were created, used, or ended; (2) derivations of entities from entities; (3) agents bearing responsibility for entities that were generated and activities that happened; (4) a notion of bundle, a mechanism to support provenance of provenance; (5) properties to link entities that refer to the same thing; and, (6) collections forming a logical structure for its members. This document introduces the provenance concepts found in PROV and defines PROV-DM types and relations. The PROV data model is domain-agnostic, but is equipped with extensibility points allowing domain-specific information to be included. Two further documents complete the specification of PROV-DM. First, a companion document specifies the set of constraints that provenance should follow. Second, a separate document describes a provenance notation for expressing instances of provenance for human consumption; this notation is used in examples in this document.
Hermitian matrices for clustering directed graphs: insights and applications

Graph clustering is a basic technique in machine learning, and has widespread applications in different domains. While spectral techniques have been successfully applied for clustering undirected graphs, the performance of spectral clustering algorithms for directed graphs (digraphs) is not in general satisfactory: these algorithms usually require symmetrising the matrix representing a digraph, and typical objective functions for undirected graph clustering do not capture cluster-structures in which the information given by the direction of the edges is crucial. To overcome these downsides, we propose a spectral clustering algorithm based on a complex-valued matrix representation of digraphs. We analyse its theoretical performance on a Stochastic Block Model for digraphs in which the cluster-structure is given not only by variations in edge densities, but also by the direction of the edges. The significance of our work is highlighted on a data set pertaining to internal migration in the United States: while previous spectral clustering algorithms for digraphs can only reveal that people are more likely to move between counties that are geographically close, our approach is able to cluster together counties with a similar socio-economical profile even when they are geographically distant, and illustrates how people tend to move from rural to more urbanised areas.
DoorGym: A Scalable Door Opening Environment And Baseline Agent

Reinforcement Learning (RL) has brought forth ideas of autonomous robots that can navigate real-world environments with ease, aiding humans in a variety of tasks. RL agents have just begun to make their way out of simulation into the real world. Once in the real world, benchmark tasks often fail to transfer into useful skills. We introduce DoorGym, a simulation environment intended to be the first step to move RL from toy environments towards useful atomic skills that can be composed and extended towards a broader goal. DoorGym is an open-source door simulation framework designed to be highly configurable. We also provide a baseline PPO (Proximal Policy Optimization) and SAC (Soft Actor-Critic)implementation, which achieves a success rate of up to 70% for common tasks in this environment. Environment kit available here:this https URL