Miracle-dz/UniFAR-FTUs huggingface dataset:
A New General Catalogue of the Ants of the World

"A name is forever, or at least as long as taxonomy continues", Barry Bolton writes, and here are all the names, antique and modern, of all the ants that are or ever were - from the arctic to the tropical, the fossilized to the living, the mislabelled to the newly christened members for the family Formicidae. For every name that has ever been applied to ants, the book supplies a history and an account of current usage, together with a fully documented indication of the present-day classification. Its comprehensive bibliography provides references to original description, synonymy, homonymy, changes in rank, status and availability, and alterations in generic status. Organized by family group, genus group and species group, this meticulously detailed but easily used volume is the ultimate resource for myrmecology. Along with Bolton's "Identification Guide to the Ant Genera of the World", it should be a useful reference for anyone, expert or amateur, with an interest in ants.
Improving Efficiency of Training a Virtual Treatment Planner Network via Knowledge-guided Deep Reinforcement Learning for Intelligent Automatic Treatment Planning of Radiotherapy.

PURPOSE We previously proposed an intelligent automatic treatment planning framework for radiotherapy, in which a virtual treatment planner network (VTPN) is built using deep reinforcement learning (DRL) to operate a treatment planning system (TPS) by adjusting treatment planning parameters in it to generate high-quality plans. We demonstrated the potential feasibility of this idea in prostate cancer intensity-modulated radiation therapy (IMRT). Despite the success, the process to train a VTPN via the standard DRL approach with an ε-greedy algorithm was time consuming. The required training time was expected to grow with the complexity of the treatment planning problem, preventing the development of VTPN for more complicated but clinically relevant scenarios. In this study, we proposed a novel knowledge-guided DRL (KgDRL) approach that incorporated knowledge from human planners to guide the training process to improve the efficiency of training a VTPN. METHOD Using prostate cancer IMRT as a testbed, we first summarized a number of rules in the actions of adjusting treatment planning parameters of our in-house TPS. During the training process of VTPN, in addition to randomly navigating the large state-action space, as in the standard DRL approach using the ε-greedy algorithm, we also sampled actions defined by the rules. The priority of sampling actions from rules decreased over the training process to encourage VTPN to explore new policy on parameter adjustment that were not covered by the rules. To test this idea, we trained a VTPN using KgDRL and compared its performance with another VTPN trained using the standard DRL approach. Both networks were trained using 10 training patient cases and 5 additional cases for validation, while another 59 cases were employed for the evaluation purpose. RESULTS It was found that both VTPNs trained via KgDRL and standard DRL spontaneously learned how to operate the in-house TPS to generate high-quality plans, achieving plan quality scores of 8.82 (±0.29) and 8.43 (±0.48), respectively. Both VTPNs outperformed treatment planning purely based on the rules, which had a plan score of 7.81 (±1.59). VTPN trained with eight episodes using KgDRL was able to perform similarly to that trained using DRL with 100 epochs. The training time was reduced from more than a week to ~13 hours. CONCLUSION The proposed KgDRL framework was effective in accelerating the training process of a VTPN by incorporating human knowledge, which will facilitate the development of VTPN for more complicated treatment planning scenarios.
Design of a Microstrip Patch Antenna

During the last decade, the cost to develop and manufacture the microstrip antenna has dropped significantly, because of the development of the microstrip antenna technology, the reduction in cost of the substrate material and manufacturing process, and the simplified design process using the newly developed computeraided design (CAD) tools. Also, the current satellite communication applications benefit greatly from the small-size and low-profile of the microstrip antenna. The performance and advantages of microstrip patch antennas such as low weight, low profile, and low cost made them the perfect choice for communication systems engineering. They have the capability to integrate with microwave circuits and therefore they are very well suited for applications such as cell devices, Wireless Local Area Network (WLAN) applications, navigation systems , Wireless reliability (Wi-Fi) and many other applications. The aim of this work is to design a linear rectangular microstrip patch array antennas with one, two and four elements. The microstrip patch antenna was designed by electromagnetic structure and fed via port found in microwave office and tested using different width and length of the patch dimension and chooses quarter wave transformer, dielectric substrate (flam resistance FR4) constant about 4.7, and loss tangent abut 0.019. In order to make them suitable for wireless applications in the band frequency 2.45GHZ. For twoelement array, notice that, the -10 dB bandwidth is begins from1.9 GHz to 2.1 GHz, the centre frequency is 2 GHz at 18.1 dB and for fourelement array notice that the centre frequency is 2.4 GHz at 2.1 dB. The Bandwidth and the gain is low, high power loss. The Bandwidth and the gain of four-element array are not larger than that for two-element array. After implementing this study we recommended to choose the thickness of the dielectric substrate, choose the loss tangent and choose the dielectric constant.
Policy-Based Federated Learning

We are increasingly surrounded by applications, connected devices, services, and smart environments which require fine-grained access to various personal data. The inherent complexities of our personal and professional policies and preferences in interactions with these analytics services raise important challenges in privacy. Moreover, due to sensitivity of the data and regulatory and technical barriers, it is not always feasible to do these policy negotiations in a centralized manner. In this paper we present PoliFL, a decentralized, edge-based framework for policy-based personal data analytics. PoliFL brings together a number of existing established components to provide privacy-preserving analytics within a distributed setting. We evaluate our framework using a popular exemplar of private analytics, Federated Learning, and demonstrate that for varying model sizes and use cases, PoliFL is able to perform accurate model training and inference within very reasonable resource and time budgets.
How to Obtain the Redshift Distribution from Probabilistic Redshift Estimates

A reliable estimate of the redshift distribution n(z) is crucial for using weak gravitational lensing and large-scale structures of galaxy catalogs to study cosmology. Spectroscopic redshifts for the dim and numerous galaxies of next-generation weak-lensing surveys are expected to be unavailable, making photometric redshift (photo-z) probability density functions (PDFs) the next best alternative for comprehensively encapsulating the nontrivial systematics affecting photo-z point estimation. The established stacked estimator of n(z) avoids reducing photo-z PDFs to point estimates but yields a systematically biased estimate of n(z) that worsens with a decreasing signal-to-noise ratio, the very regime where photo-z PDFs are most necessary. We introduce Cosmological Hierarchical Inference with Probabilistic Photometric Redshifts (CHIPPR), a statistically rigorous probabilistic graphical model of redshift-dependent photometry that correctly propagates the redshift uncertainty information beyond the best-fit estimator of n(z) produced by traditional procedures and is provably the only self-consistent way to recover n(z) from photo-z PDFs. We present the chippr prototype code, noting that the mathematically justifiable approach incurs computational cost. The CHIPPR approach is applicable to any one-point statistic of any random variable, provided the prior probability density used to produce the posteriors is explicitly known; if the prior is implicit, as may be the case for popular photo-z techniques, then the resulting posterior PDFs cannot be used for scientific inference. We therefore recommend that the photo-z community focus on developing methodologies that enable the recovery of photo-z likelihoods with support over all redshifts, either directly or via a known prior probability density.
EFFECTS OF BIRDS AND RODENTS ON SYNTHETIC TALLGRASS COMMUNITIES

We used a bird and mammal exclosure design on plowed ground to test for the effects of granivory and herbivory by small vertebrates on early stages of tallgrass prairie succession. Seed predation by birds and browsing by rodents had major and additive impacts. In high- and low-density plantings, respectively, seed-eating birds reduced plant densities by 20% and 23% and grass biomass by 24% and 34%. Meadow voles did not affect plant numbers but reduced forb biomass by 35% in high- and 57% in low-density plantings. In high- and low-density plots, respectively, birds reduced species richness by 3% and 17% without influencing diversity; selective browsing by voles on two legumes and one coneflower left species number unaffected but reduced diversity by 4% and 25% by accentuating dominance of already-prominent species. Bird effects were more pronounced in high-density plantings, while vole effects were most pronounced in low-density plantings. Results suggest that opportunistic finches and doves that seek out .
Supersoft X-ray nebulae in the Large Magellanic Cloud

Supersoft X-rays sources (SSSs) have been proposed as potential Type Ia supernova (SN Ia) progenitors. If such objects are indeed persistently X-ray luminous and embedded in sufficiently dense ISM, they will be surrounded by extended nebular emission. These nebulae should persist even long after a SN Ia explosion, due to the long recombination and cooling times involved. With this in mind, we searched for nebular [O III] emission around four SSSs and three SNRs in the Large Magellanic Cloud, using the 6.5m Baade telescope at Las Campanas Observatory and the IMACS camera. We confirm that, out of the four SSS candidates, only CAL 83 can be associated with an [O III] nebula. The [O III] luminosity for the other objects are constrained to $\lesssim 17\%$ of that of CAL 83 at 6.8 pc from the central source. Models computed with the photoionization code CLOUDY indicate that either the ISM densities in the environments of CAL 87, RX J0550.0-7151 and RX J0513.9-6951 must be significantly lower than surrounding CAL 83, or the average X-ray luminosities of these sources over the last $\lesssim 10,000$ years must be significantly lower than presently observed, in order to be consistent with the observed luminosity upper limits. For the three SNRs we consider (all with ages $< 1000$ years), our [O III] flux measurements together with the known surrounding ISM densities strongly constrain the ionizing luminosity of their progenitors in the last several thousand years, independent of the progenitor channel.
Machine Learning Explanations to Prevent Overtrust in Fake News Detection

Combating fake news and misinformation propagation is a challenging task in the post-truth era. News feed and search algorithms could potentially lead to unintentional large-scale propagation of false and fabricated information with users being exposed to algorithmically selected false content. Our research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms to study the effects of algorithmic transparency on end-users. We present evaluation results and analysis from multiple controlled crowdsourced studies. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining. The study results indicate that explanations helped participants to build appropriate mental models of the intelligent assistants in different conditions and adjust their trust according to their perceptions of model limitations.
Gestational diabetes mellitus and obstetric outcomes in a Ghanaian community

Introduction This study was aimed at evaluating effect of Gestational diabetes mellitus (GDM) and maternal characteristics on pregnancy outcome. GDM has several risk factors including; advanced maternal age, ethnic background, obesity and family history of diabetes mellitus. These pregnancy complications are associated with fetal morbidity and mortality and may lead to macrosomia and shoulder dystocia. Others are stillbirth, miscarriages, preterm and small for gestational age babies. Methods This was a retrospective case-case control study which compared maternal characteristics and pregnancy outcome among pregnant women with and without GDM. Diagnosis of GDM was done in accordance with the American Diabetes Association (ADA) criteria. Weight and height were determined and Body mass index (BMI) calculated. Pregnancy outcome was determined at the end of pregnancy and information on maternal characteristics obtained using questionnaire and patient folders. Results Those who developed GDM were significantly older (OR= 1.772; 95% CI =1.432-2.192; P<0.0001) and had higher BMI (OR=1.637; 95% CI=1.004-1.289; P=0.044) than those who did not. A significant number of those who developed GDM also had stillbirths OR= 5.188; 95% CI=1.093-24.613; p=0.038) and cesarean deliveries (OR=14.362; 95% CI=3.661-56.335; p= 0.001). Conclusion Women who develop GDM are more likely to deliver stillborn or macrosmic babies and may require surgical intervention in order to have normal deliveries.
Reversible and Irreversible Voltage Manipulation of Interfacial Magnetic Anisotropy in 
Pt
/
Co
/Oxide Multilayers

The perpendicular magnetic anisotropy at the Co/oxide interface in Pt/Co/MOx (MOx = MgOx, AlOx, TbOx) was modified by an electric field using a 10 nm-thick ZrO2 as a solid electrolyte. The large voltage-driven modification of interfacial magnetic anisotropy and the non-volatility of the effect is explained in terms of the migration of oxygen ions towards/away from the Co/MOx interface. While the effect is reversible in Pt/Co/AlOx and Pt/Co/TbOx, where the Co layer can be oxidised or reduced, in Pt/Co/MgOx the effect has been found to be irreversible. We propose that these differences may be related to the different nature of the ionic conduction within the MOx layers.
Screening of higher plants for fungitoxicity against Rhizoctonia solani in vitro

Thirteen plant species were screened in vitro for fungitoxicity against R. solani, the causal agent of sheath blight of paddy rice. Fresh leaf samples of Allium cepa, Azadirachta indica, Caesalpinia pulcherrima, Eucalyptus globulus, Calotropis gigantea, Ipomoea carnia [I. carnea], Lawsonia inermis, Ocimum sanctum [O. tenuiflorum], Parthenium hysterophorus, Piper betel [P. betle], Pongamia glabra [P. pinnata], Prosopis juliflora and Thevetia peruviana were collected and cold and hot water extracts were prepared. The maximum inhibition of mycelial growth was observed with the cold water extract of P. juliflora followed by T. peruviana. With hot water extraction, T. peruviana was the most fungitoxic followed by P. juliflora. Complete inhibition of sclerotial production was recorded with the cold water extracts of C. pulcherrima, E. globulus and L. inermis and in the hot water extracts of C. gigantea, O. sanctum and P. juliflora at all the concentrations tested (2.5, 5 and 10%). The fungitoxicity ofC. pulcherrima and E. globulus was not lost even after air drying or autoclaving. All the plant extracts tested maintained their fungitoxicity following thermal treatment at 70deg C for 10 minutes.
Distributional Reinforcement Learning with Maximum Mean Discrepancy

Distributional reinforcement learning (RL) has achieved state-of-the-art performance in Atari games by recasting the traditional RL into a distribution estimation problem, explicitly estimating the probability distribution instead of the expectation of a total return. The bottleneck in distributional RL lies in the estimation of this distribution where one must resort to an approximate representation of the return distributions which are infinite-dimensional. Most existing methods focus on learning a set of predefined statistic functionals of the return distributions requiring involved projections to maintain the order statistics. We take a different perspective using deterministic sampling wherein we approximate the return distributions with a set of deterministic particles that are not attached to any predefined statistic functional, allowing us to freely approximate the return distributions. The learning is then interpreted as evolution of these particles so that a distance between the return distribution and its target distribution is minimized. This learning aim is realized via maximum mean discrepancy (MMD) distance which in turn leads to a simpler loss amenable to backpropagation. Experiments on the suite of Atari 2600 games show that our algorithm outperforms the standard distributional RL baselines and sets a new record in the Atari games for non-distributed agents.
Exploiting the Surrogate Gap in Online Multiclass Classification

We present Gaptron, a randomized first-order algorithm for online multiclass classification. In the full information setting we show expected mistake bounds with respect to the logistic loss, hinge loss, and the smooth hinge loss with constant regret, where the expectation is with respect to the learner's randomness. In the bandit classification setting we show that Gaptron is the first linear time algorithm with $O(K\sqrt{T})$ expected regret, where $K$ is the number of classes. Additionally, the expected mistake bound of Gaptron does not depend on the dimension of the feature vector, contrary to previous algorithms with $O(K\sqrt{T})$ regret in the bandit classification setting. We present a new proof technique that exploits the gap between the zero-one loss and surrogate losses rather than exploiting properties such as exp-concavity or mixability, which are traditionally used to prove logarithmic or constant regret bounds.
Keeping the user in the cloud: a cognitive social capital antecedent to use continuance and trust-commitment in personal cloud storage services

ABSTRACT While personal cloud storage services are popular, providers experience difficulty retaining users. Two explanations of user continuance intention, being use continuance theory and trust-commitment theory, emerge from prior literature. However, there is no unifying theoretical lens to reconcile these two explanations. We use a cognitive social capital explanation to unify these two literature streams. We apply our research model to 237 cloud storage users. We find that the cognitive social capital lens significantly predicts both use continuance intention and trust-commitment in personal cloud storage services. Implications for cloud service providers are offered.
Spatial combinatorics

We define and study a spatial (infinite-dimensional) counterpart of Stirling numbers. In classical combinatorics, the Pochhammer symbol (m)n can be extended from a natural number m ∈ N to the falling factorials (z)n = z(z−1) · · · (z−n+1) of an argument z from F = R or C, and Stirling numbers of the first and second kinds are the coefficients of the expansions of (z)n through z k, k ≤ n and vice versa. When taking into account spatial positions of elements in a locally compact Polish space X, we replace N by the space of configurations—discrete Radon measures γ = ∑ i δxi on X, where δxi is the Dirac measure with mass at xi. The spatial falling factorials (γ)n := ∑ i1 ∑ i2 6=i1 · · · ∑ in 6=i1,. ,in 6=in−1 δ(xi1 ,xi2 ,.,xin ) can be naturally extended to mappings M (1)(X) 3 ω 7→ (ω)n ∈ M (n)(X), where M (n)(X) denotes the space of F-valued, symmetric (for n ≥ 2) Radon measures on Xn. There is a natural duality between M (n)(X) and the space CF (X) of F-valued, symmetric continuous functions on Xn with compact support. The Stirling operators of the first and second kind, s(n, k) and S(n, k), are linear operators, acting between spaces CF (X) and CF (X) such that their dual operators, acting from M (k)(X) into M (n)(X), satisfy (ω)n = ∑n k=1 s(n, k) ∗ω⊗k and ω⊗n = ∑n k=1 S(n, k) (ω)k, respectively. In the case where X has only a single point, the Stirling operators can be identified with Stirling numbers. We derive combinatorial properties of the Stirling operators, present their connections with a generalization of the Poisson point process and with the Wick ordering under the canonical commutation relations.
A Survey on Graph Neural Networks for Knowledge Graph Completion

Knowledge Graphs are increasingly becoming popular for a variety of downstream tasks like Question Answering and Information Retrieval. However, the Knowledge Graphs are often incomplete, thus leading to poor performance. As a result, there has been a lot of interest in the task of Knowledge Base Completion. More recently, Graph Neural Networks have been used to capture structural information inherently stored in these Knowledge Graphs and have been shown to achieve SOTA performance across a variety of datasets. In this survey, we understand the various strengths and weaknesses of the proposed methodology and try to find new exciting research problems in this area that require further investigation.
The Milky Way’s bulge star formation history as constrained from its bimodal chemical abundance distribution

We conduct a quantitative analysis of the star formation history (SFH) of the Milky Way’s (MW) bulge by exploiting the constraining power of its stellar [Fe/H] and [Mg/Fe] distribution functions. Using Apache Point Observatory Galactic Evolution Experiment survey data, we confirm the previously established bimodal [Mg/Fe]–[Fe/H] distribution within 3 kpc of the inner Galaxy. To fit the chemical bimodal distribution, we use a simple but flexible star formation framework, which assumes two distinct stages of gas accretion and star formation, and systematically evaluate a wide multidimensional parameter space. We find that the data favour a three-phase SFH that consists of an initial starburst, followed by a rapid star formation quenching episode, and a lengthy, quiescent secular evolution phase. The metal-poor, high-α bulge stars ([Fe/H] < 0.0 and [Mg/Fe] > 0.15) are formed rapidly (<2 Gyr) during the early starburst. The density gap between the high- and low-α sequences is due to the quenching process. The metal-rich, low-α population ([Fe/H] > 0.0 and [Mg/Fe] < 0.15) then accumulates gradually through inefficient star formation during the secular phase. This is qualitatively consistent with the early SFH of the inner disc. Given this scenario, a notable fraction of young stars (age <5 Gyr) is expected to persist in the bulge. Combined with extragalactic observations, these results suggest that a rapid star formation quenching process is responsible for bimodal distributions in both the MW’s stellar populations and in the general galaxy population and thus plays a critical role in galaxy evolution.