Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimality Assessments of Classifiers on Single and Multi-labelled Obstetrics Outcome Classification Problems

It is indisputable that clinicians cannot exactly state the outcome of pregnancies through conventional knowledge and methods even as the surge in human knowledge continues. Hence, several computational techniques have been adapted for precise pregnancy outcome (PO) prediction. Obstetric datasets for PO determination exist as single label learning (SLL), multi-label learning (MLL) and multi-target (MTP) problems. There is however no single classifier recommended to optimally satisfy the needs of all the classification types. This work therefore identifies six widely used PO classifiers and investigates their performances in all three classification categories; to find the best performing classifier. Obstetric dataset exposed to input rank analysis via Principal component Analysis, produced thirteen (13) significant features for the experiment. Accuracy, F1-measure and build/test time were used as evaluation metrics. Decision tree (DT) had an average accuracy and F1 score of 89.23% and 88.23% respectively, with 1.0 average rank. Under MLL configuration, average accuracy (91.71%) and F1 score (94.28%) were highest in the random forest (RF) which had a 1.0 average test time rank. Using MTP, DT had an average accuracy of 88.80% and average F1 score of 71.13%, the multi-layered perceptron (MLP) had the best time cost with an average rank value of 2.0. From the results, RF is most optimal in terms of accuracy and average rank value, while DT is the most efficient in terms of time cost. The comparative analysis of global averages of the six base classifiers shows that RF is the most optimal algorithm with an average accuracy of 87.3% given all three data setups in the study. MLP on the other hand had an unexpectedly high time cost, making it unsuitable for similar data classifications if time is the main criterion. It is recommended that the choice of the classifier should either be RF or DT depending on the application domain and whether or not time cost is a major consideration. Keywords—Pregnancy outcome; random forest; multi-label learning; comparative analytics; machine learning algorithms; single label learning; maternal outcome prediction; decision tree
MILK GEL STRUCTURE. XI. ELECTRON MICROSCOPY OF GLUCONO-δ-LACTONE-INDUCED SKIM MILK GELS1

Gels were prepared from fresh and reconstituted skim milk (10–30% total solids) using glucono-δ-lactone as the acid precursor within the pH range of 4 to 6. Firmness of glucono-δ-lactone-induced skim milk gels increased with (a) decrease in pH of skim milk having the same total solids content, (b) increase in total solids at a given pH, (c) pre-heating skim milk to 90°C, and (d) adding glucono-δ-lactone at a higher temperature. Electron microscopy revealed that in condition (a) larger sizes of casein micelles (particles) were associated with a higher firmness whereas in conditions (b), (c), and (d) the increased firmness was correlated with decreased micelle sizes. The relation between rheological behavior and microstructure of glucono-δ-lactone-induced skim milk gels was more dependent upon the micellar arrangement than upon the size and shape of the casein micelles. Conditions which led to large casein micelle clusters and chains in conditions (b), (c), and (d) resulted both in severe syneresis and in weaker gels. An unusual phenomenon was observed in skim milk gels made at pH 5.5, particularly at the lower total solids content (10%): the casein particles consisted of a solid core surrounded by an outer lining, 0.03–0.05 μm thick, which resembled a membrane-line structure; there was a free annular space of 0.05 to 0.08 μm between the lining and the core. The existence of this structure was confirmed by thin-sectioning and freeze-fracturing techniques.
Improved hydrothermal synthesis of MoS2 sheathed carbon nanotubes

MoS2 sheathed carbon nanotubes have been successfully synthesized using a hydrothermal route under controlled conditions. The resultant material was studied by XRD, EDS, HRTEM, and Raman spectroscopy. Advantages of the preparation presented here compared to other methods are: a) lower reaction temperature, b) high yield of sheathed nanotubes including ends and full body, c) simple process with non-toxic materials, and d) no damage inflicted to nanotubes.
The formation of low-dimensional ionic crystallites in carbon nanotubes

Computer simulation models were constructed and applied to understand the low-dimensional crystallites formed in carbon nanotubes when filled with liquid KI. By using a much simplified model, the precise atomistic region of the specific distortions observed experimentally were determined. As a result, the same model was applied to understanding the actual atomistic mechanism by which these tubes fill. It was shown that the narrow pore radius of the nanotube, coupled with the ionic ordering inherent in the filling material, are critical in the formation of the low-dimension crystallites observed experimentally.
Silicon-based inorganic electrets for application in micromachined devices

Layers of SiO/sub 2/ and Si/sub 3/N/sub 4/ have good mechanical properties for application in micro-machined electret capacitor microphones. These materials are investigated as single or double layers in terms of chargeability and long-term charge stability. The main emphasis is put on the miniaturization of electret layers. The lateral dimensions of the electrets are reduced to 2 mm and the charge decay characteristics under different environmental conditions are described. According to the experimental data, multilayer samples of silicon dioxide and nitride possess good chargeability and higher charge stability compared to the well investigated single layers. Typically, the double layers loose /spl sim/10% of their surface potential when annealed for 200 min at 300/spl deg/C and show a peak of the thermally stimulated current at 430/spl deg/C. It can also be shown that the miniaturization of samples does not necessarily cause a faster charge decay.
African Pear Seed Oil: Potential Alternative Source to Diesel Oil

Diesel engines represent the most widely used machines in agriculture. The prices of diesel oil as fuel continue to increase because the sources of crude oil are diminishing. In addition, the exhaust gases from the combustion of fossil fuels in the engines pollute the environment. African pear seed (Dacryodes edulis) oil was tested to find out if it possesses physical and chemical properties enabling it to be used as fuel for diesel engines. Oil was extracted from the dried and crushed African pear seed by soxhlet extraction. The oil yield was 27%, and the oil had a light yellow color. The extracted oil and diesel oil were comparatively analyzed for fuel properties: relative density, refractive index, viscosity, water content, calorific value, ash content, and burning characteristics. Similarly, the seed sample was analyzed proximately for moisture, ash, protein, fat, carbohydrate, crude fiber, and energy content. Results showed that the African pear seed oil had fuel properties similar to those of diese.
Mea culpa: How developers fix their own simple bugs differently from other developers

In this work, we study how the authorship of code affects bug-fixing commits using the SStuBs dataset, a collection of single-statement bug fix changes in popular Java Maven projects. More specifically, we study the differences in characteristics between simple bug fixes by the original author — that is, the developer who submitted the bug-inducing commit — and by different developers (i.e., non-authors). Our study shows that nearly half (i.e., 44.3%) of simple bugs are fixed by a different developer. We found that bug fixes by the original author and by different developers differed qualitatively and quantitatively. We observed that bug-fixing time by authors is much shorter than that of other developers. We also found that bug-fixing commits by authors tended to be larger in size and scope, and address multiple issues, whereas bug-fixing commits by other developers tended to be smaller and more focused on the bug itself. Future research can further study the different patterns in bug-fixing and create more tailored tools based on the developer’s needs.
The Interplay of Task Success and Dialogue Quality: An in-depth Evaluation in Task-Oriented Visual Dialogues

When training a model on referential dialogue guessing games, the best model is usually chosen based on its task success. We show that in the popular end-to-end approach, this choice prevents the model from learning to generate linguistically richer dialogues, since the acquisition of language proficiency takes longer than learning the guessing task. By comparing models playing different games (GuessWhat, GuessWhich, and Mutual Friends), we show that this discrepancy is model- and task-agnostic. We investigate whether and when better language quality could lead to higher task success. We show that in GuessWhat, models could increase their accuracy if they learn to ground, encode, and decode also words that do not occur frequently in the training set.
Comments on Leo Breiman's paper 'Statistical Modeling: The Two Cultures' (Statistical Science, 2001, 16(3), 199-231)

Breiman challenged statisticians to think more broadly, to step into the unknown, model-free learning world, with him paving the way forward. Statistics community responded with slight optimism, some skepticism, and plenty of disbelief. Today, we are at the same crossroad anew. Faced with the enormous practical success of model-free, deep, and machine learning, we are naturally inclined to think that everything is resolved. A new frontier has emerged; the one where the role, impact, or stability of the {\it learning} algorithms is no longer measured by prediction quality, but an inferential one -- asking the questions of {\it why} and {\it if} can no longer be safely ignored.
Stability and Deviation Optimal Risk Bounds with Convergence Rate O(1/n)

The sharpest known high probability generalization bounds for uniformly stable algorithms (Feldman, Vondr\'{a}k, 2018, 2019), (Bousquet, Klochkov, Zhivotovskiy, 2020) contain a generally inevitable sampling error term of order $\Theta(1/\sqrt{n})$. When applied to excess risk bounds, this leads to suboptimal results in several standard stochastic convex optimization problems. We show that if the so-called Bernstein condition is satisfied, the term $\Theta(1/\sqrt{n})$ can be avoided, and high probability excess risk bounds of order up to $O(1/n)$ are possible via uniform stability. Using this result, we show a high probability excess risk bound with the rate $O(\log n/n)$ for strongly convex and Lipschitz losses valid for \emph{any} empirical risk minimization method. This resolves a question of Shalev-Shwartz, Shamir, Srebro, and Sridharan (2009). We discuss how $O(\log n/n)$ high probability excess risk bounds are possible for projected gradient descent in the case of strongly convex and Lipschitz losses without the usual smoothness assumption.
Modeling Coral Reef Bleaching Recovery Through KCC-Theory

We use Volterra-Hamilton systems theory and their associated cost functional to study the population dynamics and productive processes of coral reefs in recovery from bleaching and show that the cost of production remains the same after the process. The KCC-theory geometrical invariants are determined for the model proposed to describe the renewed symbiotic interaction between coral and algae.
RadarLoc: Learning to Relocalize in FMCW Radar

Relocalization is a fundamental task in the field of robotics and computer vision. There is considerable work in the field of deep camera relocalization, which directly estimates poses from raw images. However, learning-based methods have not yet been applied to the radar sensory data. In this work, we investigate how to exploit deep learning to predict global poses from Emerging Frequency-Modulated Continuous Wave (FMCW) radar scans. Specifically, we propose a novel end-to-end neural network with self-attention, termed RadarLoc, which is able to estimate 6-DoF global poses directly. We also propose to improve the localization performance by utilizing geometric constraints between radar scans. We validate our approach on the recently released challenging outdoor dataset Oxford Radar RobotCar. Comprehensive experiments demonstrate that the proposed method outperforms radar-based localization and deep camera relocalization methods by a significant margin.
BigCarl: Mining frequent subnets from a single large Petri net

While there have been lots of work studying frequent subgraph mining, very few publications have discussed frequent subnet mining from more complicated data structures such as Petri nets. This paper studies frequent subnets mining from a single large Petri net. We follow the idea of transforming a Petri net in net graph form and to mine frequent sub-net graphs to avoid high complexity. Technically, we take a minimal traversal approach to produce a canonical label of the big net graph. We adapted the maximal independent embedding set approach to the net graph representation and proposed an incremental pattern growth (independent embedding set reduction) way for discovering frequent sub-net graphs from the single large net graph, which are finally transformed back to frequent subnets. Extensive performance studies made on a single large Petri net, which contains 10 K events, 40 K conditions and 30 K arcs, showed that our approach is correct and the complexity is reasonable.
Open Domain Question Answering over Tables via Dense Retrieval

Recent advances in open-domain QA have led to strong models based on dense retrieval, but only focused on retrieving textual passages. In this work, we tackle open-domain QA over tables for the first time, and show that retrieval can be improved by a retriever designed to handle tabular context. We present an effective pre-training procedure for our retriever and improve retrieval quality with mined hard negatives. As relevant datasets are missing, we extract a subset of Natural Questions (Kwiatkowski et al., 2019) into a Table QA dataset. We find that our retriever improves retrieval results from 72.0 to 81.1 recall@10 and end-to-end QA results from 33.8 to 37.7 exact match, over a BERT based retriever.
Multiscale Mass Action Law

The classical mass action law is put into the context of multiscale thermodynamics. The emergence of time irreversibility in mechanics becomes in chemical kinetics the emergence of time reversibility in the completely irreversible gradient time evolution of chemically reacting systems. As in mechanics, this type of symmetry breaking is a tool for extracting an overall pattern in the time evolution. The emerging pattern represents then a reduced model of the time evolution. Both extended and the classical mass action laws are also lifted to kinetic equations that take into account inertia, fluctuations, and external forces. By a subsequent reduction to moments we form a Grad-type hierarchy for chemical kinetics. “Anyone whose goal is ’something higher’ must expect someday to suffer vertigo. What is vertigo? Fear of falling? No, Vertigo is something other than fear of falling. It is the voice of the emptiness below us which tempts and lures us, it is the desire to fall, against which, terrified, we defend ourselves. ” —Milan Kundera, The Unbearable Lightness of Being
Communication Technologies for Smart Grid: A Comprehensive Survey

With the ongoing trends in the energy sector such as vehicular electrification and renewable energy, the Smart Grid (SG) is clearly playing a more and more important role in the electric power system industry. One essential feature of the SG is the information flow over high-speed, reliable, and secure data communication networks in order to manage the complex power systems effectively and intelligently. SGs utilize bidirectional communication to function whereas traditional power grids mainly only use one-way communication. The communication requirements and suitable techniques differ depending on the specific environment and scenario. In this paper, we provide a comprehensive and up-to-date survey on the communication technologies used in the SG, including the communication requirements, physical layer technologies, network architectures, and research challenges. This survey aims to help the readers identify the potential research problems in the continued research on the topic of SG communications.
Effect of Electromagnetic Field on Antimicrobial Activity and Novel Antimicrobial Compounds

The objective of this study is to determine the antimicrobial activity of the essential oils and effect of electromagnetic field on the activity. To find out antimicrobial activity of essential oils (Thyme, Clove, Lavender), against test organisms disc diffusion assay was performed. MIC of all essential oils were determined under electromagnetic field and non-electromagnetic field. Both studies confirmed the antimicrobial activity of oils and effect of electromagnetic field. However, time killing experiment was conducted to calculate rate of inhibition for essential oils, lavender oil and thyme oil showed higher rate as compared to that of clove oil. This study provides evidence to antimicrobial activity of essential oils, and also confirmed the effect of electromagnetic field on the activity. The proposed mechanism for antimicrobial activity is inhibition of metabolic enzymes which has supported with Analytical Profile Identification (API 20NE).
Stochastic Geometry Modeling and Analysis for THz-mmWave Hybrid IoT Networks

Terahertz (THz) band contains abundant spectrum resources that can offer ultra-high data rates. However, due to the THz band's inherent characteristics, i.e., low penetrability, high path loss, and non-negligible molecular absorption effect, THz communication can only provide limited coverage. To overcome these fundamental obstacles and fully utilize the THz band, we consider a hybrid Internet-of-Things (IoT) network consisting of THz and millimeter wave (mmWave) cells. A hybrid IoT network can dynamically switch between mmWave and THz links to ensure reliable and ultra-fast data connection. We use a stochastic geometric framework to evaluate the proposed hybrid IoT network's coverage probability and spectral efficiency and validate the analysis through numerical simulation. In this paper, we derive a closed-form expression of the Laplace transform of the interference while considering an accurate multi-level Flat-top (MLFT) antenna pattern. We observed that a large antenna array with a strong bias to the THz base station (TBS) improves the end-to-end network performance through numerical results. Furthermore, we recognized a fundamental trade-off relation between the TBS's node density and the bias to mmWave/THz; e.g., high TBS density with a strong bias to the TBS may degrade the network performance.
Self-Supervised Test-Time Learning for Reading Comprehension

Recent work on unsupervised question answering has shown that models can be trained with procedurally generated question-answer pairs and can achieve performance competitive with supervised methods. In this work, we consider the task of unsupervised reading comprehension and present a method that performs “test-time learning” (TTL) on a given context (text passage), without requiring training on large-scale human-authored datasets containing context-question-answer triplets. This method operates directly on a single test context, uses self-supervision to train models on synthetically generated question-answer pairs, and then infers answers to unseen human-authored questions for this context. Our method achieves accuracies competitive with fully supervised methods and significantly outperforms current unsupervised methods. TTL methods with a smaller model are also competitive with the current state-of-the-art in unsupervised reading comprehension.
A mathematical theory of computational resolution limit in multi-dimensional spaces

Resolving a linear combination of point sources from their Fourier data in a bounded domain is a fundamental problem in imaging and signal processing. With incomplete Fourier data and inevitable noise in the measurement, there is a fundamental limit on the separation distance between the point sources that can be resolved. This is the so-called resolution limit problem. Characterization of this resolution limit is still a long-standing puzzle despite the prevalent use of the classical Rayleigh limit. It is well-known that the Rayleigh limit is heuristic and its drawbacks become prominent when dealing with data that is subjected to elaborated processing, as is what modern computational imaging methods do. Therefore, a more precise characterization of the resolution limit becomes increasingly necessary with the development of data processing methods. For this purpose, we developed a theory of ‘computational resolution limit’ for both the number detection problem and the support recovery problem in one dimension in (Liu and Zhang 2019 arXiv:1912.05430; Liu and Zhang 2021 IEEE Trans. Inf. Theory 67 4812–27). In this paper, we extend the theory from dimension one to multiple dimensions. More precisely, we define and quantitatively characterize the ‘computational resolution limit’ for the number detection problem and the support recovery problem respectively in general multi-dimensional spaces. Our results indicate that there exists a phase transition phenomenon regarding the super-resolution factor and the signal-to-noise ratio in each of the two recovery problems. Our main results are obtained by using a projection strategy. Finally, to verify the theory, we propose deterministic projection-based algorithms for the number detection problem and the support recovery problem respectively. The numerical results in dimensions two and three confirm the phase transition phenomenons.
Semantic 3D Map Change Detection and Update based on Smartphone Visual Positioning System

Accurate localization and 3D maps are increasingly needed for various artificial intelligence based IoT applications such as augmented reality, intelligent transportation, crowd monitoring, robotics, etc. This article proposes a novel semantic 3D map change detection and update based on a smartphone visual positioning system (VPS) for the outdoor and indoor environments. The proposed method presents an alternate solution to SLAM for map update in terms of efficiency, cost, availability, and map reuse. Building on existing 3D maps of recent years, a system is designed to use artificial intelligence to identify high-level semantics in images for positioning and map change detection. Then, a virtual LIDAR that estimates the depth of objects in the 3D map is used to generate a compact point cloud to update changes in the scene. We present an excellent performance of localization with respect to other state-of-the-art smartphone positioning solutions to accurately update semantic 3D maps. It is shown that the proposed solution can position users within 1.9m, and update objects with an average error of 2.1m.