Miracle-dz/UniFAR-FTUs huggingface dataset:
Debris Object Orbit Initialization Using the Probabilistic Admissible Region with Asynchronous Heterogeneous Observations

One of the most challenging problems in treating space debris is the characterization of the orbit of a newly detected and uncorrelated measurement. The admissible region is defined as the set of physically acceptable orbits (i.e. orbits with negative energies) consistent with one or more measurements of a Resident Space Object (RSO). Given additional constraints on the orbital semi-major axis, eccentricity, etc., the admissible region can be constrained, resulting in the constrained admissible region (CAR). Based on known statistics of the measurement process, one can replace hard constraints with a Probabilistic Admissible Region (PAR), a concept introduced in 2014 as a Monte Carlo uncertainty representation approach using topocentric spherical coordinates. Ultimately, a PAR can be used to initialize a sequential Bayesian estimator and to prioritize orbital propagations in a multiple hypothesis tracking framework such as Finite Set Statistics (FISST). To date, measurements used to build the PAR have been collected concurrently and by the same sensor. In this paper, we allow measurements to have different time stamps. We also allow for non-collocated sensor collections; optical data can be collected by one sensor at a given time and radar data collected by another sensor located elsewhere. We then revisit first principles to link asynchronous optical and radar measurements using both the conservation of specific orbital energy and specific orbital angular momentum. The result from the proposed algorithm is an implicit-Bayesian and non-Gaussian representation of orbital state uncertainty.
Hierarchical Semantic Convolutional Neural Network for Lung Cancer Diagnosis

While deep learning methods have demonstrated classification performance comparable to human readers in tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a “black box. ” We present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level semantic features; and 2) a high-level prediction of nodule malignancy. The low-level outputs reflect diagnostic features often reported by radiologists and serve to explain how the model interprets the images in an expert-interpretable manner. The information from these low-level outputs, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level output. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves comparable results with the state-of-the-art methods.
N 0 . 2017-001 Modification of a baroclinic test case to a height-based coordinate system without topography implementation

In this study, a baroclinic wave test case is modified to be conducted without topography implementation in a model with a height-based vertical coordinate system (VCS). In the original test case, the topography implementation is required for a model with a height-based VCS, unlike a model with a pressure-based VCS. We propose a method to conduct the test case without the topography implementation using a model with a height-based VCS. Compared with the original test case, the proposed method provides similar wave structures and evolutions. The values and time evolutions of root mean square errors are almost the same as the original test case. The convergence of the numerical solution can be examined by the proposed method at a degree similar to that of the original method. This means that the proposed method examines the physical performance of the model without the topography implementation with a height-based VCS.
SPIN OF A BATTED BALL TOWARD THE OPPOSITE FIELD IN BASEBALL

The purpose of this study was to investigate the spinning motion of a ball batted toward the opposite field in baseball. A pitching machine was used to launch the balls toward the bat, which was adjusted and tied up to hit the balls in the opposite field. The ball movements were recorded using three high-speed video cameras. The results indicated that the batted ball backspin and sidespin components correlate strongly with the launch elevation angles and launch horizontal angles, respectively. All the batted balls had a certain amount of sidespin components. The balls batted toward the opposite field experienced unavoidable horizontal Magnus force, resulting in curving. Thus, it is important to launch the balls with a larger velocity to hit a long ball toward the opposite field while comparing with the balls batted towards the same field with less sidespin components.
Effect of Diagnostic and Feedback Assessment Approaches in Enhancing Achievement in Mathematics among Secondary School Students in Calabar Municipality

The study is aimed at examining the effect of diagnostic and feedback assessment approaches in enhancing achievement in Mathematics among secondary school students in Calabar Municipality in Cross River State. Two Research questions were raised and two hypotheses stated. The design adopted was a pre-test and post-test quasi experimental research design. 3 secondary schools out of 17 public secondary schools in Calabar municipality were used for the study. In the school selected, two classes were used as experimental group and one class as control group, that is, two experimental groups and one control group were used. Two intact senior secondary classes were randomly selected for each of the two groups. A total number of 119 students were used as participants for the study. The instrument that was used was a Mathematics Achievement Test (MAT) designed by the researchers with the help of two classroom teachers in Mathematics. Items used were adopted from past question papers in WAEC and NECO according to the syllabus in SS2. The instrument containing 30 items was face and content validated and was pilot tested using Cronbach alpha to determine its reliability co-efficient which was found to be 0.84. The research was conducted by giving treatment to the experimental groups while the control group was not given any treatment. The data were collected and analyzed using Analysis of Covariance (ANCOVA) with the pretest as a covariate controlling for pre-existing difference among them. The result shows that diagnostic and feedback assessment approaches enhance academic performance in Mathematics. Recommendations were proffered.
Supplementary Material for Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval

Our dataset consists of 50,107 video-sentence pairs collected from popular social media websites including reddit, Imgur, and Tumblr.1 We crawled the data using the GIPHY API2 with query terms mrw, mfw, hifw, reaction, and reactiongif; we crawled the data from August 2016 to March 2019. Table 1 shows the descriptive statistics of our dataset. We are continuously crawling the data, and plan to release updated versions in the future.
Soil Organic Matter Organic Carbon and Nitrogen

Analysis of soil organic matter (SOM), conducted on the AESA Soil Quality Benchmark sites, has revealed some significant differences in the per cent of organic carbon and total nitrogen in Albertas agricultural ecoregions, depending on soil horizon and slope positions. In ecoregions located further north in the province, the per cent of organic carbon in topsoil generally increased. The per cent of organic carbon in subsoil was higher in ecoregions that had greater below-ground plant productivity. The per cent of nitrogen in both topsoil and subsoil were higher in ecoregions that had higher levels of soil organic matter. Organic carbon and nitrogen levels increased at downslope locations in all ecoregions. Because SOM varies between ecoregions, and within a single field based on slope position, the assessment of SOM is very site-specific. The study findings highlight the importance of conserving high SOM levels in soils and identify some ways farmers can do this.
NUCLEAR STRUCTURE CALCULATIONS WITH SKYRME FORCES

Starting from an effective Skyrme interaction we present a method to take into account the coupling between oneand two-phonon terms in the wave functions of excited states. The approach is a development of a finite rank separable approximation for the quasiparticle RPA calculations proposed in our previous work. As an application of our method we present results of calculations for the isoscalar giant quadrupole resonance properties in the 132Sn.
NSL-KDD Dataset

Denial of Service (DoS) is one type of attack where the attacker depletes computer network resources. The impact of DoS attacks causes the computer to not work properly. Intrusion Detection System (IDS) acts as a detector of various types of attacks on computer networks including DoS. IDS identifies attacks based on network data classification. The many data classifications lead to long periods of time and low accuracy results. The build time of the model is a parameter used to calculate the time required to construct a data classification model while the accuracy is a parameter that assesses the accuracy of the experimental results of the actual data used. To solve this problem, this research used Multiple Classifier System (MCS) method to detect DoS attacks. Because in previous research MCS classification resulted in long build time model in MCS this research using Naive Bayes classification, Decision Tree classification, K Nearest Neighbor (KNN) classification and combiner Majority Voting with the purpose of reducing the build time of the model without affecting the accuracy level and increasing the accuracy value of the MCS method. Based on the results of research conducted to produce 98.12% accuracy with 2.07s model build time
Short-Range Quality-Factor Modulation (SQuirM) for Low Power High Speed Inductive Data Transfer

Wireless data telemetry for implantable medical devices (IMDs) has, in general, been limited to a few Mbps, and used for applications such as transmitting recordings from an implanted monitoring device, or uploading commands to an implanted stimulator. However, modern neural interfaces need to record high resolution potentials from hundreds of neurons; this requires much higher data rates. While fast wireless communication is possible using existing standards such as WiFi, power consumption demands are far too high for IMDs. Short range inductive link based telemetry, in particular impulse-based systems such as pulse-harmonic modulation (PHM), have demonstrated transfer speeds of up to 20 Mbps with a small power budget. However, these systems require complex and precise circuits, making them potentially susceptible to inter-symbol-interference. This work presents a new method named Short-range Quality-factor Modulation (SQuirM), which retains the low power consumption and high data rate of PHM, while improving the resilience of the system and simplifying the circuit design. Transmitter and receiver circuits were fabricated using 0.35 ${\mu }m$ CMOS. The circuits were capable of reliably transceiving data at speeds of up to 50.4 Mbps, with a BER of $< 4.5 \times 10^{-10}$ , and a transmitter energy consumption of 8.11 pJ/b.
PyVacy: Towards Practical Differential Privacy for Deep Learning

In this work, we present an extension to the PyTorch deep learning framework which facilitates differentially private optimization. We discuss the algorithms provided by the extension and compare its contribution to that of other libraries in existence. We then go on to demonstrate the performance of the resulting private models on several statistical tasks, as well as the incurred overhead and structural changes of the resulting scripts. We find that our extension enables the construction of models which retain practical performance while incurring a minimal impact on existing, non-private training procedures.
Research data supporting "Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems"

This dataset is in JSON format and contains log files of interactions between a turn-taking spoken dialogue system and Amazon Mechanical turkers, collected from our previous live trials. It includes two application domains: San Francisco restaurants and hotels, each of them has around 1000 logs. The user responses are 1-best ASR hypothesis recognised by our ASR system, and the system responses were collected by running another round of data collection on AMT. The number of total collected system responses is around 5.1K for each domain. All users are anonymous.
Two Clusters of HIV-1 Infection, Rural Idaho, USA, 2008

of 54°C, and 45 amplifi cation cycles. PCR blanks containing all reagents except for DNA and extraction blanks were included in every PCR set. Results of the amplifi cation reactions are listed in the Table. All accompanied extraction and PCR controls remained free of amplifi cation products. All amplicons resulting from suicide PCRs were sequenced. Amplicons resulting from the use of primer pairs YP14F/YP13R and pst-F/pst-R matched the reference sequence to 100% (GenBank accession no. AL109969.1). Amplicons resulting from the use of primer pair PCP-F/PCP-R matched this reference sequence to only 97.78%. This deviation is because of a 2-bp insertion (2 Ts, positions 8531 and 8532, GenBank accession no. AL109969.1) at Y. pestis strain CO92 plasmid pPCP1. The sequences obtained from 3 persons’ remains showed in the pPCP1 sequence section between nucleotide positions 8528–8532 only 3 Ts instead of 5 Ts described for Y. pestis strain CO92 plasmid pPCP1 (GenBank accession no. AL109969.1). The sequences found in this study were deposited in GenBank under accession nos. HQ290521–HQ290523. To conclude, the successful recovery of several Y. pestis plasmid pPCP1 DNA sequences in skeletal fi nds from the mass burial site excavated in Manching-Pichl suggests that these persons died of plague. Moreover, our fi ndings constitute a molecularly supported confi rmation for the presence of Y. pestis, the etiologic agent of plague, in late medieval (1250–1500 CE) southern Germany. In future studies, we will attempt to recover chromosomal Y. pestis DNA from the mass grave skeletal remains to obtain clues as to the specifi c Y. pestis strain and the microbiology of past plague in Europe.
Exact, Tractable Inference in the Sigma Cognitive Architecture via Sum-Product Networks

Sum-product networks (SPNs) are a new kind of deep architecture that support exact, tractable inference over a large class of problems for which traditional graphical models cannot. The Sigma cognitive architecture is based on graphical models, posing a challenge for it to handle problems within this class, such as parsing with probabilistic grammars, a potentially important aspect of language processing. This work proves that an early unidirectional extension to Sigma’s graphical architecture, originally added in service of rule-like behavior but later also shown to support neural networks, can be leveraged to yield exact, tractable computations across this class of problems, and further demonstrates this tractability experimentally for probabilistic parsing. It thus shows that Sigma is able to specify any valid SPN and, despite its grounding in graphical models, retain the desirable inference properties of SPNs when solving them.
Investigation of optimal auditory signal for visually-challenged people using auditory evoked magnetic responses

Birdsongs are used as an auditory signal for visually-challenged people in public spaces. More than 40% of the visually-challenged people reported that the auditory signal is hard to identify. We tried to find the auditory signal that is easy to identify based on auditory evoked magnetic field (AEF) responses. We choose eight birdsongs as experimental stimuli. The birdsong was presented in silent and noisy conditions in Experiment 1 and in different interaural time delay (ITD) and reverberation time (RT) conditions in Experiment 2. The most prominent AEF response, N1m, and correlation between the envelope of the birdsongs and the AEF was analyzed. The results indicated that the N1m amplitudes and the correlation showed maximum when the participants listened to the song of Cuckoo and Oriental scops owl, suggesting that the song of Cuckoo and Oriental scops owl is a candidate for optimal auditory signal that is easy to identify.
Variable Gain CMOS LNA

In this paper it is presented a variable gain low noise amplifier (LNA) with differential output using a 0. 18μm CMOS technology. The design of this LNA was specified for a sigma-delta digital receiver, in accordance to the IEEE802.15. 4 standard using the industrial, scientific and medical (ISM) band.
α-Adjoint Weighted Kinetics Parameter Estimation in the Monte Carlo α-Iteration Calculations

Nowadays the accelerator-driven subcritical system (ADS) has been widely studied as a candidate of transmutation reactor. [1] Applications of the conventional point kinetics equation (PKE) [2] using the k-adjoint weighted kinetics parameters may be invalid for the time-dependent ADS analysis because it assumes [3] that its nominal state is critical. In order to enhance the accuracy of the point kinetics analysis for an ADS, Nishihara et al. [3] proposed a PKE using kinetics parameters weighted by Green’s function [4]. In our previous research [5], we proposed a PKE formulation with kinetics parameters weighted by the α-adjoint flux, the fundamental mode solution to the adjoint α-mode eigenvalue equation, because the α-mode eigenvalue equation can accurately represent an off-critical system. In this paper, the physical meaning of the α-adjoint flux is derived by applying the power iteration method for the α-mode eigenvalue equation. Using this physical meaning, algorithms to calculate the α-adjoint weighted kinetics parameters in the Monte Carlo (MC) α iteration method [6] are developed and tested in an infinite homogeneous 2-group problem and the KUCA Th-ADS experimental benchmark. [7]
Probabilistic Methods for 3 – D Object Recognition

A new Bayesian framework for 3–D object classification and localization is introduced. Objects are represented as probability density functions, and observed features are treated as random variables. These probability density functions turn out a non geometric nature of models and characterize the statistical behavior of local object features like points or lines. The parameterization of model densities covers several terms of object recognition: locations and instabilities of features, rotation and translation, projection, the assignment of image and model features, as well as relations. This paper treats especially the probabilistic modeling of relational dependencies between single features. The mathematical framework, the training algorithms, as well as the localization and classification modules are discussed in detail. The experimental evaluation shows the usefulness of the introduced concepts on real image data.
CNN training using additionally training data extracted from frames of endoscopic videos

Insufficient amounts of labeled training data poses a big problem in machine learning, especially for medical applications where medical image data sets are usually quite small. In this work we propose a method to increase the amount of labeled endoscopic image data in order to improve the classification accuracy of automated diagnosis systems for the classification of colonic polyps. Starting from a small colonic polyp endoscopic image database, we increase the number of images by tracking the content shown in the images through the endoscopic videos and by extracting patches from frames of the videos that show the same content as in the images of the database, but under different viewing conditions. By means of our proposed method we are able to increase the amount of labeled image data by factor 40, without adding images of insufficient image quality or images without clearly visible features for the differentiation of colonic polyps. We will show that this increased amount of training images can drastically improve the performance of CNNs, which are state-of-the-art in the automated classification of colonic polyps.
Characterization and performance of high performance concrete for pavements

Several states in the U.S. are undertaking a variety of innovative research in high performance concrete pavement materials and innovative design/construction features. This paper addresses the investigation undertaken in exploring the use of fiber reinforced and low shrinkage concrete in pavements. Past experience with these materials have indicated i) potential benefits in flexural fatigue resistance and reduction in crack development, and ii) potential reduction in slab warping effects with implications on pavement slab longevity. The objective of this study was to examine the design and lab performance of these materials for local conditions in Maryland, monitor their lab and field performance, and quantify potential benefits. This paper presents the laboratory shrinkage, toughness, and fatigue analysis results for mixtures used in the construction of a major highway artery in Route 50 in Maryland.
Analytical framework for analyzing brake squeal noise using assumed-modes approach

Sometimes a loud noise or high pitched squeal occurs when the brakes are applied. It is generated during the braking phase and is characterized by a harmonic spectrum. Brake squeal is induced by self-excited vibrations, consequences of local nonlinearities at the contact interface. Many researchers have examined the problem with experimental, analytical, and computational techniques, but there is still no method to fully annihilate brake squeal. This paper deals with presentation of a new model to analyze the brake squeal behavior. In this paper, a lumped-continuous vibration model is presented for the braking system and nonlinear equations are obtained using the Hamilton’s principle. Then, the linearization of nonlinear equations is done around the equilibrium point of system and linear stability analysis is discussed. Furthermore, the effects of different braking parameters such as friction coefficient, rotational speed, pad stiffness, calipers etc. on the brake squeal noise are investigated.
Fusion of Depth Maps for Multiview Reconstruction

Depth fusion has been considered as an effective approach to realize 3D reconstruction from multiple images captured at different points of view. However, the noises originating from redundant 3D points during fusion usually lead to over-complete representation and further deteriorate the 3D shape interpretation. The presented method models objects and scenes as 3D point clouds using multi-view images. By analyzing the depth consensus in multiple views a strict selection of points is made for precise reconstruction. The 3D point cloud is further processed by applying a bayesian-based model to analyze the photometric property of the images at sub-pixel accuracy. Finally, a set of experiments is carried out to study the influence of different parameters on the reconstruction quality.
Advanced Cosmology : Primordial non-Gaussianities

with amplitude |f(k)| = √ ak + b 2 k. Reality of f(x) imposes ak = a−k , bk = −b−k. In words, for a given configuration in real space f(x), a set of real numbers (ak, bk), parameterized by the vector k, completely (and uniquely) describe it. Different configurations of f(x) are described by different set of numbers. Suppose now we want to randomly generate a field configuration f(x), then one way to do it is to prescribe a probability distribution function for the set (ak, bk). If, furthermore, we want f(x) to be a Random Gaussian Field configuration, then this put a tight constraint on what is the PDF for (ak, bk). Let’s begin by studying the PDF for one particular mode k. Definition : A Gaussian distribution for a mode is one such that ak and bk is drawn from the following Gaussian distribution in k space with zero mean, and k-dependent variance σk