Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Interpretability of CNN Models Using Non-Negative Concept Activation Vectors

Convolutional neural network (CNN) models for computer vision are powerful but lack explainability in their most basic form. This deficiency remains a key challenge when applying CNNs in important domains. Recent work for explanations through feature importance of approximate linear models has moved from input-level features (pixels or segments) to features from mid-layer feature maps in the guise of concept activation vectors (CAVs). CAVs contain concept-level information and could be learnt via Clustering. In this work, we rethink the ACE algorithm of Ghorbani et~al., proposing an alternative concept-based explanation framework. Based on the requirements of fidelity (approximate models) and interpretability (being meaningful to people), we design measurements and evaluate a range of dimensionality reduction methods for alignment with our framework. We find that non-negative concept activation vectors from non-negative matrix factorization provide superior performance in interpretability and fidelity based on computational and human subject experiments. Our framework provides both local and global concept-level explanations for pre-trained CNN models.
THE TARSUS OF UKHAATHERIUM NESSOVI (EUTHERIA, MAMMALIA) FROM THE LATE CRETACEOUS OF MONGOLIA: AN APPRAISAL OF THE EVOLUTION OF THE ANKLE IN BASAL THERIANS

Abstract The recently discovered Cretaceous asioryctithere Ukhaatherium nessovi, represented by three individuals with virtually complete postcrania, shows basal character states for Eutheria, that were previously only partially known from the fragmentary postcranium of Asioryctes nemegetensis. A new suite of ankle characters is revealed. This suite renders characters previously considered as present in the hypothetical common ancestor of eutherians, as diagnostic of a more nested group in the mammalian tree. Some possible primitive retentions preserved in one or both asioryctitheres are the absence of a pulley-shaped astragalar trochlea, fibula only slightly encasing the astragalus laterally, absence of an anterior plantar tubercle on the calcaneum, and presence of a ventrally curved tuber calcis (straight in most other eutherians). A set of characters in the ankle joints were plotted on a phylogenetic tree of basal therian taxa, including among others, the mentioned asioryctitheres, Vincelestes neuquenianus, Deltatheridium pretrituberculare, Mayulestes ferox, and Pucadelphys andinus. The states in the hypothetical ancestors of Theria, Metatheria, and Eutheria were inferred with the maximum parsimony algorithm. Derived states of the ankle of Eutheria are fewer, and the hypothetical ancestors of Metatheria and Eutheria turn out to be more similar to each other than previously suspected.
Enhancing strength of MICP-treated sandy soils: from micro to macro scale

Microbial-Induced Calcium carbonate (CaCO3) Precipitation (MICP) has been extensively studied for soil improvement in geotechnical engineering. The properties of calcium carbonate crystals such as size and quantity affect the strength of MICP-treated soil. This study demonstrates how the data from micro-scale microfluidic experiments that examine the effects of injection intervals and concentration of cementation solution on the properties of calcium carbonate crystals can be used to optimise the MICP treatment of macro-scale sand soil column experiments for effective strength enhancement. The micro-scale experiments reveal that, due to Ostwald ripening, longer injection intervals allow smaller crystals to dissolve and reprecipitate into larger crystals regardless of the concentration of cementation solution. By applying this finding in the macro-scale experiments, a treatment duration of 6 days, where injection intervals were 12 h, 24 h, and 48 h for cementation solution concentration of 0.25 M, 0.5 M and 1.0 M, respectively, was long enough to precipitate crystals large enough for effective strength enhancement. This was indicated by the fact that significantly higher soil strength and larger crystals were produced when treatment duration increased from 3 days to 6 days, but not when it increased from 6 days to 12 days.
Deep Probabilistic Accelerated Evaluation: A Certifiable Rare-Event Simulation Methodology for Black-Box Autonomy

Evaluating the reliability of intelligent physical systems against rare catastrophic events poses a huge testing burden for real-world applications. Simulation provides a useful, if not unique, platform to evaluate the extremal risks of these AI-enabled systems before their deployments. Importance Sampling (IS), while proven to be powerful for rare-event simulation, faces challenges in handling these systems due to their black-box nature that fundamentally undermines its efficiency guarantee. To overcome this challenge, we propose a framework called Deep Probabilistic Accelerated Evaluation (D-PrAE) to design IS, which leverages rare-event-set learning and a new notion of efficiency certificate. D-PrAE combines the dominating point method with deep neural network classifiers to achieve superior estimation efficiency. We present theoretical guarantees and demonstrate the empirical effectiveness of D-PrAE via examples on the safety-testing of self-driving algorithms that are beyond the reach of classical variance reduction techniques.
Z
-boson decays into an invisible dark photon at the LHC, HL-LHC, and future lepton colliders

We study the decay of the $Z$ vector boson into a photon and a massless (invisible) dark photon in high-energy collisions. The photon can be used as trigger for the event, while the dark photon is detected indirectly as missing momentum in the event final state. We investigate the possibility of searching for such a dark photon at the LHC, HL-LHC and future lepton colliders, and compare the respective sensitivities. As expected, the best result is found for the lepton colliders running at the $Z$ mass, FCC-ee and CEPC, with a final sensitivity to branching ratios of order $O(10^{-11})$. We also discuss how to use the photon angular distribution of the events in lepton collisions to discriminate between the dark photon and a pseudo-scalar state like the axion.
Farm and Farmers Specific Characteristics and Analysis of Farmers’ Farm Revenue and Cost Returns among Rural Farmers in Biu Plateau Region of Borno State, Nigeria

A socio-economic survey of rural farmers in Biu Plateau Region of Borno State, Nigeria was conducted in 2018 to study the characteristics of the farmers in order to proffer solutions that can improve their productivity. A structured questionnaire containing over 90 questions were designed and administered to a randomly selected 385 farmers determined from Krejcie and Morgan (1970) sampling table proportionately to the population of each of the selected settlements in the five local government areas that made-up of the region. Data were analyzed using relevant descriptive statistics. The average age of the farmers was found to be 46.13 and male dominated the farming population (77.1%). It was found that, land ownership arrangement was predominantly (58.2%) through inheritance. Majority (50.9%) of the respondents had farming experience of 11-20 years. Only 10% of the respondents used at least 5 hectares of land for their crop production. Majority (97.4%) of the respondents had no access to formal source of credit. The total revenue generated from the sales of the produce by all farmers amounted to the sum of N 101,369,390.00k. On an average, each of the farmers made a profit of N168, 438.98k in the 2018 farming year. For the income of the farmers in the countryside to be improved, the local councils should educate rural farmers on extension services for sustainable farming and best practices. Additionally, critical rural infrastructure, such as water and electricity should be provided at affordable rates. Arguably, this could not only improve farmers’ earnings, but also make the communities attractive to the rural farmers and young people, and stabilize rural populations.
Design of a β-Ga2O3 Schottky Barrier Diode With p-Type III-Nitride Guard Ring for Enhanced Breakdown

This work presents the electrostatic analysis of a novel Ga<sub>2</sub>O<sub>3</sub> vertical Schottky diode with three different guard ring (GR) configurations to reduce the peak electric field at the metal edges. Highly doped p-type GaN, p-type nonpolar AlGaN, and polarization-doped graded p-AlGaN are simulated and analyzed as the GR material, which forms a heterojunction with the Ga<sub>2</sub>O<sub>3</sub> drift layer. GR with nonpolar graded p-AlGaN with a bandgap larger than Ga<sub>2</sub>O<sub>3</sub> is found to show the best performance in terms of screening the electric field at the metal edges. The proposed GR configuration is also compared with a reported Ga<sub>2</sub>O<sub>3</sub> Schottky diode with no GR and a structure with high-resistive nitrogen-doped GR. The optimized design is predicted to have a breakdown voltage as high as 6.2 kV and a specific ON-resistance of 3.55 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega $ </tex-math></inline-formula>-cm<sup>2</sup>, which leads to an excellent power figure of merit of 10.8 GW/cm<sup>2</sup>.
The impact of lianas on 10 years of tree growth and mortality on Barro Colorado Island, Panama

1. Lianas compete intensely with trees, but few studies have examined long‐term effects of liana infestation on tree growth and mortality. We quantified the effects of lianas in tree crowns (n = 2907) and rooted within 2 m of trees (n = 1086) on growth and mortality of 30 tree species from 1995 to 2005 on Barro Colorado Island (BCI), Panama, documented liana infestation in tree crowns in 1996 and 2007 to determine the dynamics of liana infestation, and quantified liana infestation in the crowns of 3231 additional canopy trees (d.b.h. ≥20 cm) in 2007 to compare with the same metric determined by previous studies in 1967 and 1980.
Evolving Metric Learning for Incremental and Decremental Features

Online metric learning has been widely exploited for large-scale data classification due to the low computational cost. However, amongst online practical scenarios where the features are evolving (e.g., some features are vanished and some new features are augmented), most metric learning models cannot be successfully applied to these scenarios, although they can tackle the evolving instances efficiently. To address the challenge, we develop a new online Evolving Metric Learning (EML) model for incremental and decremental features, which can handle the instance and feature evolutions simultaneously by incorporating with a smoothed Wasserstein metric distance. Specifically, our model contains two essential stages: a Transforming stage (T-stage) and a Inheriting stage (I-stage). For the T-stage, we propose to extract important information from vanished features while neglecting non-informative knowledge, and forward it into survived features by transforming them into a low-rank discriminative metric space. It further explores the intrinsic low-rank structure of heterogeneous samples to reduce the computation and memory burden especially for highly-dimensional large-scale data. For the I-stage, we inherit the metric performance of survived features from the T-stage and then expand to include the new augmented features. Moreover, a smoothed Wasserstein distance is utilized to characterize the similarity relationships among the heterogeneous and complex samples, since the evolving features are not strictly aligned in the different stages. In addition to tackling the challenges in one-shot case, we also extend our model into multi-shot scenario. After deriving an efficient optimization strategy for both T-stage and I-stage, extensive experiments on several datasets verify the superior performance of our EML model.
A Benchmark dataset for both underwater image enhancement and underwater object detection

Underwater image enhancement is such an important vision task due to its significance in marine engineering and aquatic robot. It is usually work as a pre-processing step to improve the performance of high level vision tasks such as underwater object detection. Even though many previous works show the underwater image enhancement algorithms can boost the detection accuracy of the detectors, no work specially focus on investigating the relationship between these two tasks. This is mainly because existing underwater datasets lack either bounding box annotations or high quality reference images, based on which detection accuracy or image quality assessment metrics are calculated. To investigate how the underwater image enhancement methods influence the following underwater object detection tasks, in this paper, we provide a large-scale underwater object detection dataset with both bounding box annotations and high quality reference images, namely OUC dataset. The OUC dataset provides a platform for researchers to comprehensive study the influence of underwater image enhancement algorithms on the underwater object detection task.
A functional methodology for determining the groundwater regime needed to maintain the health of groundwater-dependent vegetation

In the past, the phrase ‘environmental allocations of water’ has most often been taken to mean allocation of water to rivers. However, it is now accepted that groundwater-dependent ecosystems are an important feature of Australian landscapes and require an allocation of water to maintain their persistence in the landscape. However, moving from this theoretical realisation to the provision and implementation of a field-based management regime is extremely difficult. The following four fundamental questions are identified as being central to the effective management of groundwater-dependent ecosystems (GDEs): (1) How do we identify GDEs in the field; put another way, which species or species assemblages or habitats are reliant on a supply of groundwater for their persistence in the landscape; (2) what groundwater regime is required to ensure the persistence of a GDE; (3) how can managers of natural resources (principally water and habitats), with limited time, money and other resources, successfully manage GDEs; and (4) what measures of ecosystem function can be monitored to ensure that management is effective? This paper explicitly addresses these questions and provides a step-by-step theoretical and practical framework for providing answers. In particular, this paper provides an introduction to some of the relevant literature and from this, presents a synthesis, presented in the form of a functional methodology for managing groundwater dependent ecosystems.
Perception-Prediction-Reaction Agents for Deep Reinforcement Learning

We introduce a new recurrent agent architecture and associated auxiliary losses which improve reinforcement learning in partially observable tasks requiring long-term memory. We employ a temporal hierarchy, using a slow-ticking recurrent core to allow information to flow more easily over long time spans, and three fast-ticking recurrent cores with connections designed to create an information asymmetry. The \emph{reaction} core incorporates new observations with input from the slow core to produce the agent's policy; the \emph{perception} core accesses only short-term observations and informs the slow core; lastly, the \emph{prediction} core accesses only long-term memory. An auxiliary loss regularizes policies drawn from all three cores against each other, enacting the prior that the policy should be expressible from either recent or long-term memory. We present the resulting \emph{Perception-Prediction-Reaction} (PPR) agent and demonstrate its improved performance over a strong LSTM-agent baseline in DMLab-30, particularly in tasks requiring long-term memory. We further show significant improvements in Capture the Flag, an environment requiring agents to acquire a complicated mixture of skills over long time scales. In a series of ablation experiments, we probe the importance of each component of the PPR agent, establishing that the entire, novel combination is necessary for this intriguing result.
Avoided quasiparticle decay and enhanced excitation continuum in the spin-
12
 near-Heisenberg triangular antiferromagnet 
Ba3CoSb2O9

The authors report inelastic neutron scattering measurements to directly visualize the effects of collective quantum fluctuations in a spin-\textonehalf{} near-Heisenberg antiferromagnet on a triangular lattice. They observe a rich spectrum of sharp magnons at low energies with strongly renormalized dispersions and much reduced spectral weight, which are accompanied at higher energies by a very strong and highly structured continuum of excitations. The dispersion renormalizations and transfer of spectral weight to continuum states are attributed to the effect of quantum fluctuations and interactions beyond the spin-wave approximation.
Evolutionary study of complex organic molecules in high-mass star-forming regions

We have studied four complex organic molecules (COMs), the oxygen-bearing methyl formate (CH3OCHO) and dimethyl ether (CH3OCH3) as well as the nitrogen-bearing formamide (NH2CHO) and ethyl cyanide (C2H5CN), towards a large sample of 39 high-mass star-forming regions representing different evolutionary stages, from early to evolved phases. We aim to identify potential correlations and chemical links between the molecules and to trace their evolutionary sequence through the star formation process. We analysed spectra obtained at 3, 2, and 0.9 mm with the IRAM-30m telescope. We derived the main physical parameters for each species by fitting the molecular lines. We compared them and evaluated their evolution while also taking several other interstellar environments into account. We report detections in 20 sources, revealing a clear dust absorption effect on column densities. Derived abundances range between ~ 10−10−10−7 for CH3OCHO and CH3OCH3, ~ 10−12−10−10 for NH2CHO, and ~ 10−11−10−9 for C2H5CN. The abundances of CH3OCHO, CH3OCH3, and C2H5CN are very strongly correlated (r ≥ 0.92) across ~ 4 orders of magnitude. We note that CH3OCHO and CH3OCH3 show the strongest correlations in most parameters, and a nearly constant ratio (~ 1) over a remarkable ~ 9 orders of magnitude in luminosity for the following wide variety of sources: pre-stellar to evolved cores, low- to high-mass objects, shocks, Galactic clouds, and comets. This indicates that COMs chemistry is likely early developed and then preserved through evolved phases. Moreover, the molecular abundances clearly increase with evolution, covering ~ 6 orders of magnitude in the luminosity/mass ratio. We consider CH3OCHO and CH3OCH3 to be most likely chemically linked. They could, for example, share a common precursor, or be formed one from the other. Based on correlations, ratios, and the evolutionary trend, we propose a general scenario for all COMs, involving a formation in the cold, earliest phases of star formation and a following increasing desorption with the progressive thermal and shock-induced heating of the evolving core.
How does the extracellular matrix affect the rigidity of an embedded spheroid

Cellularized tissue and polymer networks can both transition from floppy to rigid as a function of their control parameters, and, yet, the two systems often mechanically interact, which may affect their respective rigidities. To study this interaction, we consider a vertex model with interfacial tension (a spheroid) embedded in a spring network in two dimensions. We identify two regimes with different global spheroid shapes, governed by the pressure resulting from competition between interfacial tension and tension in the network. In the first regime, the tissue remains compact, while in the second, a cavitation-like instability leads to the emergence of gaps at the tissue-network interface. Intriguingly, compression of the tissue promotes fluidization, while tension promotes cellular alignment and rigidification, with the mechanisms driving rigidification differing on either side of the instability.
Forecasting Precipitable Water Vapor Using LSTMs

Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time. In this paper, this ability is applied to learning the pattern of Global Positioning System (GPS)-based Precipitable Water Vapor (PWV) measurements over a period of 4 hours. The trained model was evaluated on more than 1500 hours of recorded data. It achieves a root mean square error (RMSE) of 0.098mm for a forecasting interval of 5 minutes in the future, and outperforms the naive approach for a lead-time of up to 40 minutes.
Software Enabled Security Architecture and Mechanisms for Securing 5G Network Services

The 5G network systems are evolving and have complex network infrastructures. There is a great deal of work in this area focused on meeting the stringent service requirements for the 5G networks. Within this context, security requirements play a critical role as 5G networks can support a range of services such as healthcare services, financial and critical infrastructures. 3GPP and ETSI have been developing security frameworks for 5G networks. Our work in 5G security has been focusing on the design of security architecture and mechanisms enabling dynamic establishment of secure and trusted end to end services as well as development of mechanisms to proactively detect and mitigate security attacks in virtualised network infrastructures. The focus of this paper is on the latter, namely the facilities and mechanisms, and the design of a security architecture providing facilities and mechanisms to detect and mitigate specific security attacks. We have developed a simplified version of the security architecture using Software Defined Networks (SDN) and Network Function Virtualisation (NFV) technologies. The specific security functions developed in this architecture can be directly integrated into the 5G core network facilities enhancing its security.
Combine Convolution with Recurrent Networks for Text Classification

Convolutional neural network (CNN) and recurrent neural network (RNN) are two popular architectures used in text classification. Traditional methods to combine the strengths of the two networks rely on streamlining them or concatenating features extracted from them. In this paper, we propose a novel method to keep the strengths of the two networks to a great extent. In the proposed model, a convolutional neural network is applied to learn a 2D weight matrix where each row reflects the importance of each word from different aspects. Meanwhile, we use a bi-directional RNN to process each word and employ a neural tensor layer that fuses forward and backward hidden states to get word representations. In the end, the weight matrix and word representations are combined to obtain the representation in a 2D matrix form for the text. We carry out experiments on a number of datasets for text classification. The experimental results confirm the effectiveness of the proposed method.