Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Neural Training with Non-Gradient and Noise Assisted Gradient Methods

In this work we demonstrate provable guarantees on the training of depth-2 neural networks in new regimes than previously explored. (1) We start with exhibiting a non-gradient iterative algorithm "Neuro-Tron" which gives a first-of-its-kind poly-time approximate solving of a neural regression (here in the $\ell_\infty$-norm) problem at finite net widths and with non-realizable data. (2) Next we give a simple stochastic algorithm that can train a ReLU gate in the realizable setting with significantly milder conditions on the data distribution than previous results. Leveraging some additional distributional assumptions we also show near-optimal guarantees of training a ReLU gate when an adversary is allowed to corrupt the true labels. (3) Lastly we analyze the behaviour of noise assisted gradient descent on a ReLU gate in the realizable setting. While making no further distributional assumptions, we locate a ball centered at the origin such that all the iterates remain inside it with high probability.
Cataclysmic variables as possible counterparts of ancient Far Eastern guest stars

Continuing our efforts to select possible classical nova candidates among Far Eastern guest stars and to identify them with modern cataclysmic variables (CVs), we present a search for counterparts in 24 promising areas of the sky corresponding to ancient observations between 204 BCE and 1690 CE. These areas have been derived by us in a previous paper. Based on physical entities of the CVs in our areas and reasonable magnitude limits compatible with the distribution of known eruption amplitudes of telescopic classical novae, we present a catalogue of a total of 80 CVs and related targets which could possibly have caused the historical sightings. This list could potentially be reduced by additionally discussing further information more vaguely given in the text. In some cases, we present a detailed discussion of the interpretation of ancient sources confronting them with properties of the brightest CVs in the field. In order to estimate whether this list is representative, we discuss the distribution of CV types in our catalogue of counterparts for the historical events. Compared to the entire sky, the surface density of most CV subtypes in our search fields reveals similar values, except for polars and intermediate polars, i. e. strongly magnetic CVs, for which a significant excess in our search fields was detected. Finally, we give an outlook towards future research in this topic, and add in an Appendix a complete atlas of the celestial maps of all 24 guest star events, displaying the search areas and locations of CVs within them.
Osmotic dehydration pretreatment for improving the quality attributes of frozen mango: effects of different osmotic solutes and concentrations on the samples

Summary The effect of osmotic dehydration pretreatment on the quality attributes (e.g. colour, hardness, drip loss, vitamin C content and pH) of frozen mango cuboids in different osmotic solutions (sucrose, glucose and maltose) and concentrations (30, 45 and 60% (wt/wt)) was investigated. Results revealed that melting temperature of mango cuboids was affected by both solute type and solid mass fraction. In addition, the dehydrofrozen samples pretreated in maltose had higher quality in vitamin C content (increasing by 23.5–73.0%), colour (colour change reducing by 2.6–39.2%) and drip loss (reducing by 0.7–9.7%) than those pretreated in other osmotic solutions. The cuboids pretreated in glucose displayed higher hardness (increasing by 16.4–36.2%). Based on principal component analysis and group distance, osmotic dehydration in 45% maltose was proposed as the most favourable freezing conditions with the highest sensory score (6.8). The current work indicates osmotic dehydration significantly improves frozen mango quality.
On the TOCTOU Problem in Remote Attestation

Much attention has been devoted to verifying software integrity of remote embedded (IoT) devices. Many techniques, with different assumptions and security guarantees, have been proposed under the common umbrella of so-called Remote Attestation (RA). Aside from executable's integrity verification, RA serves as a foundation for many security services, such as proofs of memory erasure, system reset, software update, and verification of runtime properties. Prior RA techniques verify the remote device's binary at the time when RA functionality is executed, thus providing no information about the device's binary before current RA execution or between consecutive RA executions. This implies that presence of transient malware (in the form of modified binary) may be undetected. In other words, if transient malware infects a device (by modifying its binary), performs its nefarious tasks, and erases itself before the next attestation, its temporary presence will not be detected. This important problem, called Time-Of-Check-Time-Of-Use ( TOCTOU ), is well-known in the research literature and remains unaddressed in the context of hybrid RA. In this work, we propose Remote Attestation with TOCTOU Avoidance (RATA): a provably secure approach to address the RA TOCTOU problem. With RATA, even malware that erases itself before execution of the next RA, can not hide its ephemeral presence. RATA targets hybrid RA architectures, which are aimed at low-end embedded devices. We present two alternative techniques - RATA A and RATA B - suitable for devices with and without real-time clocks, respectively. Each is shown to be secure and accompanied by a publicly available and formally verified implementation. Our evaluation demonstrates low hardware overhead of both techniques. Compared with current hybrid RA architectures - that offer no TOCTOU protection - RATA incurs no extra runtime overhead. In fact, it substantially reduces the time complexity of RA computations: from linear to constant time.
Statistically modeling optical linewidths of nitrogen vacancy centers in microstructures

We investigate the relationship between ion implantation and the optical linewidth of the nitrogen vacancy (NV) zero-phonon line (ZPL) in bulk and structured samples. We also propose a novel approach to ion implantation that we name postimplantation, in which nitrogen is implanted after all fabrication processes have been completed. We examine three post-implanted samples, one implanted with $^{14}\mathrm{N}$ and two with $^{15}\mathrm{N}$ isotopes. We perform photoluminescence excitation (PLE) spectroscopy to assess optical linewidths and optically detected magnetic resonance (ODMR) measurements to isotopically classify the NV centers. From this, we find that NV centers formed from nitrogen naturally occurring in the diamond lattice are characterized by a linewidth distribution peaked at an optical linewidth nearly two orders of magnitude smaller than the distribution characterizing most of the NV centers formed from implanted nitrogen. Surprisingly, we also observe a number of $^{15}\mathrm{NV}$ centers with narrow ($l500\phantom{\rule{4pt}{0ex}}\mathrm{MHz}$) linewidths, implying that implanted nitrogen can yield NV centers with narrow optical linewidths. We further use a Bayesian approach to statistically model the linewidth distributions, to accurately quantify the uncertainty of fit parameters in our model, and to predict future linewidths within a particular sample. Our model is designed to aid comparisons between samples and research groups, in order to determine the best methods of achieving narrow NV linewidths in structured samples.
Insect, not plant, determines gall morphology in the Apiomorpha pharetrata species‐group (Hemiptera: Coccoidea)

Species of the Australian genus Myrabolia are reviewed. Lectotypes are designated for Silvanus brevicornis Erichson, Myrabolia haroldiana Reitter, M. lindensis Blackburn and M. longicornis Blackburn. The following new species are described and illustrated: M. australis, M. blackburni, M. elongata, M. kioloa, M. lawrencei, M. micra, M. pelion and M. tasmanica. Ocholissa leai Grouvelle, originally described in Colydiidae, is here transferred to Myrabolia as M. leai (Grouvelle) comb. n. The nomenclatural history, a diagnosis and distribution data are provided for each species. A key to the species of the genus is presented.
Learning to Detect Unacceptable Machine Translations for Downstream Tasks

The field of machine translation has progressed tremendously in recent years. Even though the translation quality has improved significantly, current systems are still unable to produce uniformly acceptable machine translations for the variety of possible use cases. In this work, we put machine translation in a cross-lingual pipeline and introduce downstream tasks to define task-specific acceptability of machine translations. This allows us to leverage parallel data to automatically generate acceptability annotations on a large scale, which in turn help to learn acceptability detectors for the downstream tasks. We conduct experiments to demonstrate the effectiveness of our framework for a range of downstream tasks and translation models.
Improving supervised prediction of aging-related genes via dynamic network analysis

This study focuses on supervised prediction of aging-related genes from -omics data. Unlike gene expression methods that capture aging-specific information but study genes in isolation, or protein-protein interaction (PPI) network methods that account for PPIs but the PPIs are context-unspecific, we recently integrated the two data types into an aging-specific PPI subnetwork, which yielded more accurate aging-related gene predictions. However, a dynamic aging-specific subnetwork did improve prediction performance compared to a static aging-specific subnetwork, despite the aging process being dynamic. So, here, we propose computational advances towards improving prediction accuracy from a dynamic aging-specific subnetwork. We develop a supervised learning model that when applied to a dynamic subnetwork yields extremely high prediction performance, with F-score of 91.4%, while the best model on any static subnetwork yields F-score of "only" 74.3%. Hence, our predictive model could guide with high confidence the discovery of novel aging-related gene candidates for future wet lab validation.
Mapping Natural Language Instructions to Mobile UI Action Sequences

We present a new problem: grounding natural language instructions to mobile user interface actions, and create three new datasets for it. For full task evaluation, we create PixelHelp, a corpus that pairs English instructions with actions performed by people on a mobile UI emulator. To scale training, we decouple the language and action data by (a) annotating action phrase spans in How-To instructions and (b) synthesizing grounded descriptions of actions for mobile user interfaces. We use a Transformer to extract action phrase tuples from long-range natural language instructions. A grounding Transformer then contextually represents UI objects using both their content and screen position and connects them to object descriptions. Given a starting screen and instruction, our model achieves 70.59% accuracy on predicting complete ground-truth action sequences in PixelHelp.
Manipulation of male attractiveness induces rapid changes in avian maternal yolk androgen deposition

Avian eggs contain maternal androgens that may adjust offspring development to environmental conditions. We review evidence and functional explanations for the relationship between androgen concentrations in avian eggs and male attractiveness. Experimental studies in captive birds show generally positive relationships, but results from correlational and experimental field studies are less consistent, perhaps because they lack a within-female design to control for confounding between-female variation. We analyzed the effect of male attractiveness on yolk levels of maternal androgens in a wild bird, using a correlational and experimental approach with a within-female design. We manipulated the sexually selected UV coloration of the crown feathers of male blue tits (Cyanistes caeruleus) after their female had laid the second egg and measured the subsequent effect on androgen concentrations (testosterone and androstenedione) in the fifth, seventh, and ninth eggs relative to that in the second egg. Levels of testosterone, but not androstenedione, in eggs 5 and 7 were higher for control (attractive) than for UV-reduced (unattractive) males. This effect disappeared in the ninth egg, coinciding with the recovery of UV coloration after manipulation. This suggests that females are capable of rapid adjustments of testosterone deposition in response to changes in their mate's ornamental plumage. However, androgen concentrations in the second egg and pretreatment male crown coloration were not correlated. Possibly, the combination of relatively small variation in UV coloration before treatment and the influence of unknown confounding variables in the correlative approach resulted in insufficient statistical power to detect such a correlation. Copyright 2009, Oxford University Press.
Inferring the Spatial Distribution of Physical Activity in Children Population from Characteristics of the Environment

Obesity affects a rising percentage of the children and adolescent population, contributing to decreased quality of life and increased risk for comorbidities. Although the major causes of obesity are known, the obesogenic behaviors manifest as a result of complex interactions of the individual with the living environment. For this reason, addressing childhood obesity remains a challenging problem for public health authorities. The BigO project (https://bigoprogram.eu) relies on large-scale behavioral and environmental data collection to create tools that support policy making and intervention design. In this work, we propose a novel analysis approach for modeling the expected population behavior as a function of the local environment. We experimentally evaluate this approach in predicting the expected physical activity level in small geographic regions using urban environment characteristics. Experiments on data collected from 156 children and adolescents verify the potential of the proposed approach. Specifically, we train models that predict the physical activity level in a region, achieving 81% leave-one-out accuracy. In addition, we exploit the model predictions to automatically visualize heatmaps of the expected population behavior in areas of interest, from which we draw useful insights. Overall, the predictive models and the automatic heatmaps are promising tools in gaining direct perception for the spatial distribution of the population’s behavior, with potential uses by public health authorities.
Comparative Analysis of Word Embeddings for Capturing Word Similarities

Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans. In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
Vafa–Witten invariants from modular anomaly

Recently, a universal formula for a non-holomorphic modular completion of the generating functions of refined BPS indices in various theories with $N=2$ supersymmetry has been suggested. It expresses the completion through the holomorphic generating functions of lower ranks. Here we show that for $U(N)$ Vafa-Witten theory on Hirzebruch and del Pezzo surfaces this formula can be used to extract the holomorphic functions themselves, thereby providing the Betti numbers of instanton moduli spaces on such surfaces. As a result, we derive a closed formula for the generating functions and their completions for all $N$. Besides, our construction reveals in a simple way instances of fiber-base duality, which can be used to derive new non-trivial identities for generalized Appell functions. It also suggests the existence of new invariants, whose meaning however remains obscure.
Efficient convolutional neural networks with smaller filters for human activity recognition using wearable sensors

Recently, human activity recognition (HAR) has been beginning to adopt deep learning to substitute for traditional shallow learning techniques that rely on hand-crafted features. CNNs, in particular, have set latest state-of-the-art on various HAR datasets. However, deep model often requires more computing resources, which limits its applications in embedded HAR. Although many successful methods have been proposed to reduce memory and FLOPs of CNNs, they often involve special network architectures for visual tasks, which are not suitable for deep HAR tasks with time series sensor signals, due to remarkable discrepancy. Therefore, it is necessary to develop lightweight deep models to perform HAR. As filter is the basic unit in constructing CNNs, we must ask whether redesigning smaller filters is applicable for deep HAR. In the paper, inspired by the idea, we proposed a lightweight CNN using re-designed Lego filters for the use of HAR. A set of lower-dimensional filters is used as Lego bricks to be stacked for conventional filters, which does not rely on any special network structure. To our knowledge, this is the first paper that proposes lightweight CNN for HAR in ubiquitous and wearable computing arena. The experiment results on five public HAR datasets, UCI-HAR dataset, OPPORTUNITY dataset, UNIMIB-SHAR dataset, PAMAP2 dataset, and WISDM dataset, indicate that our novel Lego-CNN approach can greatly reduce memory and computation cost over CNN, while maintaining comparable accuracy. We believe that the proposed approach could combine with the existing state-of-the-art HAR architecture and easily deployed onto wearable devices for real HAR applications.
Design of Link-Selection Strategies for Buffer-Aided DCSK-SWIPT Relay System

Adaptive link selection for buffer-aided relaying can achieve significant performance gain compared with the conventional relaying with fixed transmission criterion. However, most of the existing link-selection strategies are designed based on perfect channel state information (CSI), which are very complex by requiring channel estimator. To solve this issue, in this paper, we investigate a buffer-aided differential chaos-shift-keying based simultaneous wireless information and power transfer (DCSK-SWIPT) relay system, where a decode-and-forward protocol is considered and the relay is equipped with a data buffer and an energy buffer. In particular, we propose two link-selection protocols for the proposed system based on harvested energy, data-buffer status and energy-shortage status, where the CSI is replaced by the harvested energy to avoid the channel estimation and the practical problem of the decoding cost at the relay is considered. Furthermore, the bit-error-rate (BER) and average-delay closed-form expressions of the proposed protocols are derived over multipath Rayleigh fading channels, which are validated via simulations. Finally, results demonstrate that both the proposed protocols not only provide better BER performance than the conventional DCSK system and DCSK-SWIPT relay system but also achieve better BER performance and lower average delay in comparison to the conventional signal-to-noise-ratio-based buffer-aided DCSK-SWIPT relay systems.
One-Shot Object Detection without Fine-Tuning

Deep learning has revolutionized object detection thanks to large-scale datasets, but their object categories are still arguably very limited. In this paper, we attempt to enrich such categories by addressing the one-shot object detection problem, where the number of annotated training examples for learning an unseen class is limited to one. We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module, the combination of which integrates metric learning with an anchor-free Faster R-CNN-style detection pipeline, eventually eliminating the need to fine-tune on the support images. We also propose novel training strategies that effectively improve detection performance. Extensive quantitative and qualitative evaluations were performed and our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
Growth, Survival, and Recruitment of Hybrid Crappies Stocked in Small Impoundments

Abstract White crappie Pomoxis annularis and black crappie P. nigromaculatus are popular sport fish, but because of erratic recruitment they are difficult to manage and generally are not recommended for stocking in small impoundments. We examined the growth, survival, and recruitment of interspecific F1 hybrid crappies stocked in ponds with established predator and prey populations, to determine if they were more amenable to management in small impoundments than their parent species. The F1 hybrids had lengths at age similar to those of white crappies through age 5. Survival of the stocked F1 hybrids was low, especially beyond age 1. Recruitment was also low; none of the stocked hybrid crappie populations were capable of maintaining their initial densities. Because their recruitment is low and they have growth rates similar to their parent species, we believe hybrid crappies provide a viable alternative to stocking white and black crappies in small impoundments. Before more specific management recommendat.
Yield and quality of maize from spent engine oil contaminated soils amended with compost under screenhouse conditions

Abstract The study investigated the effects of poultry manure compost (PMC) on the growth performance and yield of maize (Zea mays L.) when cultivated on spent engine oil (SEO) contaminated soil with a view to assessing its nutritional composition. The experiment consisted of three SEO treatments (0%, 1% and 2% w/w) with six levels (0, 2, 4, 6, 8 and 10 t ha-1) of PMC applications. Each treatment was replicated thrice and arranged in a 3 × 6 × 3 factorial completely randomized design to give a total of 54 pots. At full physiological maturity, maize grains were harvested and threshed manually for analysis. As the bioavailability of metals increased with increasing PMC, their uptake by maize plants was enhanced. The soil amended with 10 t ha-1 and contaminated with 1% SEO recorded the highest uptake rates of 1.761 × 10-2, 2.345 × 10-2 and 4.285 × 10-2 day-1 for Fe, Cu and Pb respectively. Also, the significantly (P<0.05) highest yield of maize (5.8 t ha-1) and the highest nutritional values of crude protein (11.27 g 100 g-1) and crude fibre (2.71 g 100 g-1) were obtained with zero SEO soil contamination at 10 t ha-1 PMC applications. There was evidence of yield and nutritional quality reduction of maize when cultivated on SEO contaminated soils with or without poultry compost fertilization
On a Variant of the Elliott-Halberstam Conjecture and the Goldbach Conjecture

In this paper we prove that the binary Goldbach conjecture for sufficiently large even integers would follow under the assumptions of both the Elliott-Halberstam conjecture and a variant of the Elliott-Halberstam conjecture twisted by the Mobius function, provided that the sum of their level of distributions exceeds 1. This continues the work of Pan. An analogous result for the twin prime conjecture is obtained by Ram Murty and Vatwani.