Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability in Selective Thinning Technology of Solder Resist for New IC Substrate Architecture

This paper presents a reliability aspect of the solder resist (SR) thinning technology. The resist thinning (RT) process was developed for thinning down SR layer highly uniformly at a large panel scale. By combined with photolithography process, the RT process can thin down SR materials selectively and can form multi-step structures. This paper first describes what the RT process is and what kind of benefit can be brought by applying the process. Then comparative studies in the impact of the RT process on SR material performances, such as surface morphology and material reliability, are reported. From the reliability study, it was revealed that the insulation reliability of SR materials was not impacted by applying the RT process. This research indicates that RT process is a beneficial tool for new SR structuring, without compromising materials performances.
Total and Differential Somatic Cell Counts , Lactation Stage

Milk normally contains some level of somatic cells: neutrophils (PMN cells), lymphocytes (LYM) and macrophages. Macrophages (MAC) comprise the major cell type in milk from healthy udders (Dosogne et al. 2003; LindmarkMånsson et al. 2006). When there is bacterial infection, tissue damage, or other inflammation processes affecting the mammary tissue, the SCC in milk dramatically increases (Sharma et al. 2011; Katherine et al. 2013). This increase in SCC results from the transfer of white blood cells from the blood to the mammary gland (Kelly et al. 2000; Sládek et al. 2006). In addition, the relative proportions of cell types present in milk change significantly, with an increased in PMN level (up to 90%) to protect the udder from bacterial challenge (Alhussien et al. 2016; Kehrli and Shuster, 1994; Zecconi and Smith, 2000). The increase in SCC milk causes the change in the components of cow's milk. The variation of the many components of cow milk with SCC was observed by many authors (Aysan et al. 2011; Brandt et al. 2010; Kelly et al. 2000; Somers et al. 2003; Bansal et al. 2005; Lindmark-Månsson et al. 2006) have reported changes in the composition of milk obtained from cows with infection, but little is known about such changes in camel milk. The relationship between SCC and lipolysis was investigated. It has been suggested that milk cells contribute to the lipolysis of milk fat to provide flavor defects (Azzara and Dimick, 1985; Ma et al. 2000; Santos et al. 2003; Gargouri et al. 2008). On the contrary, other studies indicated no relationship between milk SCC and lipolysis level (Lee et al. 1980; Cartier and Chilliard, 1990). Milk composition varies according to factors such as breed, age, mammary gland health, lactation stage, nutritional management and season (Dobranié et al. 2008). The present study was carried out to investigate the effects of somatic cell counts (SCC), differential SCC (macrophage (MAC), lymphocyte (LYM) and polymorphonuclear leukocytes (PMN)), number and stage lactation on milk composition in camel and cow milk. Camel milk appeared to contain significantly (P<0.05) a higher content of minerals. Lipolysis level is similar in camel milk compared to cow milk. Lipolysis level increased as MAC level increased in camel’s milk but not in cow’s milk. Our results suggest that MAC play a role in the degradation of dromedary milk fat. Mineral compositions were significantly affected by the SCC in camel milk. The milk composition was not affected by lactation number in both species. Total solid, Ca and Na content in camel’s milk were gradually decreased through lactation.
The dark ages, cosmic dawn and epoch of reionization seen at radio wavelengths

A short review is presented of the Dark Ages, Cosmic Dawn and Epoch of Reionization (EoR), in particular focussing on (planned) studies via the redshifted hydrogen 21-cm hyperfine transition. Current constraints on the EoR and current interferometric experiments (with the GMRT, MWA, PAPER and LOFAR) to detect the 21-cm brightness temperature are discussed and a forward look it presented on the Square Kilometre Array (SKA) and how how it can revolutionize this field from statistical (e.g. power-spectra) to tomographic (e.g. image-cube) analyses.
Covert MIMO Communications under Variational Distance Constraint

We consider the problem of covert communication over Multiple-Input Multiple-Output (MIMO) Additive White Gaussian Noise (AWGN) channels, in which a transmitter attempts to reliably communicate with a legitimate receiver while ensuring a low probability of detection by an adversary. We exactly characterize the covert capacity under a variational distance covertness constraint when MIMO channel matrices are static and known to all parties. We also characterize the covert capacity for a class of channels in which the legitimate channel matrix is known but the adversary’s channel matrix is only known to satisfy a rank and a spectral norm constraint.
Measuring Performance: Ranking State Success Over Two Decades in India

IZA DP No. 14522 JUNE 2021 Measuring Performance: Ranking State Success over Two Decades in India This paper proposes new composite measures of relative and dynamic state performance to improve governance and delivery of public programs in developing countries with a federal structure. We rank the performance of 19 major Indian states on two large development programs launched by the Indian government over the last two decades using publicly available data. Although we find volatility in performance over time, there exists a positive correlation between measures of state capacity, development and accountability with program outcomes. Our findings have important implications for both the design and implementation of public service programs of such large scale. JEL Classification: H4, I38, O18, C43, R42
Statistical and Alignment Based Methods for Comparison of Non-Coding DNA Sequences

Increase in the amount of biological sequence data being generated is due to the advancement in the sequencing technologies. This has led to an upsurge in the need for methods/techniques that analyze these sequences to make biological inferences. Promoter sequences are one such biological sequences that play an important role in the process of gene regulation. These promoter sequences are made up of motifs arranged/configured in a particular order which is very important for its functionality. In this work, we propose a few techniques that capture the information regarding the arrangement of these motifs. Further this captured information help in the analysis of the promoter sequences. The methods use the motif features obtained from the Position Specific Motif Matrices (PSMMs) of the promoter sequences and then give a dissimilarity measure. Alignment-free as well as alignment based methods are proposed. The experimental results show that these methods are quite successful in bringing out the similarity/dissimilarity existing between given promoters/promoter sets. The alignment based method also helps in the process of inferring homology and in the phylogenetic analysis. In general, all the methods might help biologists in applications like drug target identification, biological pathway analysis and gene expression analysis.
Grassmannian diffusion maps based classification for high-dimensional data

Diffusion Maps is a nonlinear dimensionality reduction technique used to embed high-dimensional data in a low-dimensional Euclidean space where the notion of distance is due to the transition probability of a random walk over the dataset. However, the conventional approach is not capable to reveal the underlying subspace structure hidden in the same dataset, a useful information for machine learning applications such as object classification and facial recognition. To circumvent this limitation, a novel nonlinear dimensionality reduction technique, referred to as Grassmannian Diffusion Maps, is developed herein relying on the affinity between subspaces represented by points on the Grassmann manifold. To this aim, the elements of a dataset are projected onto a low-dimensional Grassmann manifold where the subspace structure of each data point is revealed. Next, given a graph on the Grassmann manifold, a kernel matrix encoding the affinity between connected subspaces is obtained to define the transition probability of the random walk over the dataset via the graph Laplacian normalization. Therefore, one can reveal the nonlinear structure of the Grassmann manifold from the eigendecomposition of the transition matrix. In this paper, three examples are used to evaluate the performance of both conventional and Grassmannian Diffusion Maps. First, a “toy” example shows that the Grassmannian Diffusion Maps can identify a well-defined parametrization of points on the unit sphere, representing a Grassmann manifold. The second example shows that the Grassmannian Diffusion Maps outperforms the conventional Diffusion Maps in classifying elements, later recovered by a conventional clustering, of a dataset by their intrinsic characteristics. In the last example, a novel classification/recognition technique is developed based on the theory of sparse representation. This method incorporates the ability of the Grassmannian Diffusion Maps in revealing the underlying subspace structure of a dataset to create an overcomplete dictionary of reduced dimension, whose atoms are given by the diffusion coordinates. Therefore, a compact and sparse solution of an underdetermined linear system is obtained solving a l1-norm minimization problem and is used to indicate the linear span of the training samples in a given class. An indicative example consists in the recognition of face images subject to varying illumination conditions, changes in face expressions, and occurrence of occlusions. The technique presented high recognition rates (i.e., 95% in the best-case scenario) using a fraction of the data required by conventional methods.
Strict fixed points of '{C}iri'{c}-generalized weak quasicontractive multi-valued mappings of integral type

‎‎Many authors such as Amini-Harandi‎, ‎Rezapour ‎et al., ‎Kadelburg ‎et al.‎‎, ‎have tried to find at least one fixed point for quasi-contractions when $alphain[frac{1}{2}‎, ‎1)$ but no clear answer exists right now and many of them either have failed or changed to a lighter version‎. In this paper‎, ‎we introduce some new strict fixed point results in the set of multi-valued '{C}iri'{c}-generalized weak quasi-contraction mappings of integral type‎. ‎We consider a necessary and sufficient condition on such mappings which guarantees the existence of unique strict fixed point of such mappings. Our result is a partial positive answer for the mentioned problem ‎ which has remained open for many years‎. Also, we give an strict fixed point result of ‎$‎alpha‎$-‎$‎psi‎$‎-quasicontractive multi-valued mappings of integral type. Our results generalize and improve many existing results on multi-valued mappings in literature. ‎Moreover‎, ‎some examples are presented to support our new class of multi-valued contractions.
A Generalized Runge-Kutta Method of order three

We present a numerical method for the solution of sti systems of ODE's and index one DAE's. The type of method is a 4 stage Generalized Linear Method that is reformulated in a special Semi Implicit Runge Kutta Method of SDIRK type. Error estimation is by imbedding a method of order 4 based on the same stages as the method and the coe cients are selected for ease of implementation. The method has 4 stages and the stage order is 2. For purposes of generating dense output and for initializing the iteration in the internal stages a continuous extension is derived. The method is A-stable and we present the region of absolute stability and the order star of the order 3 method that is used for delivering the solution.
A novel method of measuring spatial rotation angle using MEMS tilt sensors

This paper presents a novel method of measuring spatial rotation angle with a dual-axis micro-electro-mechanical systems tilt sensor. When the sensor is randomly mounted on the surface of the rotating object, there are three unpredictable and unknown mounting position parameters: α, the sensor’s swing angle on the measuring plane; β, the angle between the rotation axis and the horizontal plane; and γ, the angle between the measuring plane and the rotation axis. Thus, the sensor’s spatial rotation model is established to describe the relationship between the measuring axis, rotation axis, and horizontal plane, and the corresponding analytical equations are derived. Furthermore, to eliminate the deviation caused by the uncertain direction of the rotation axis, an extra perpendicularly mounted, single-axis tilt sensor is combined with the dual-axis tilt sensor, forming a three-axis tilt sensor. Then, by measuring the sensors’ three tilts and solving the model’s equations, the object’s spatial rotation angle is obtained. Finally, experimental results show that the developed tilt sensor is capable of measuring spatial rotation angle in the range of ±180° with an accuracy of 0.2° if the angle between the rotation axis and the horizontal plane is less than 75°.
The closure of ideals of $ell^1(Sigma)$ in its enveloping $mathrm{C}^*$-algebra

If $X$ is a compact Hausdorff space and $\sigma$ is a homeomorphism of $X$, then an involutive Banach algebra $\ell^1(\Sigma)$ of crossed product type is naturally associated with the topological dynamical system $\Sigma=(X,\sigma)$. We initiate the study of the relation between two-sided ideals of $\ell^1(\Sigma)$ and ${\mathrm C}^\ast(\Sigma)$, the enveloping $\mathrm{C}^\ast$-algebra ${\mathrm C}(X)\rtimes_\sigma \mathbb Z$ of $\ell^1(\Sigma)$. Among others, we prove that the closure of a proper two-sided ideal of $\ell^1(\Sigma)$ in ${\mathrm C}^\ast(\Sigma)$ is again a proper two-sided ideal of ${\mathrm C}^\ast(\Sigma)$.
USTC FLICAR: A Multisensor Fusion Dataset of LiDAR-Inertial-Camera for Heavy-duty Autonomous Aerial Work Robots

In this paper, we present the USTC FLICAR Dataset , which is dedicated to the development of simultaneous localization and mapping and precise 3D reconstruction of the workspace for heavy-duty autonomous aerial work robots. In recent years, numerous public datasets have played signiﬁcant roles in the advancement of autonomous cars and unmanned aerial vehicles (UAVs). However, these two platforms differ from aerial work robots: UAVs are limited in their payload capacity, while cars are restricted to two-dimensional movements. To ﬁll this gap, we create the “Giraffe” mapping robot based on a bucket truck, which is equipped with a variety of well-calibrated and synchronized sensors: four 3D LiDARs, two stereo cameras, two monocular cameras, Inertial Measurement Units (IMUs), and a GNSS/INS system. A laser tracker is used to record the millimeter-level ground truth positions. We also make its ground twin, the “Okapi” mapping robot, to gather data for comparison. The proposed dataset extends the typical autonomous driving sensing suite to aerial scenes. Therefore, the dataset is named “FLICAR” to denote ﬂying cars. We believe this dataset can also represent the ﬂying car scenarios, speciﬁcally the takeoff and landing of VTOL (Vertical Takeoff and Landing) ﬂying cars. The dataset is available for download at: https://ustc-flicar.github. io/ .
The Obstinate Passion of Foreign Exchange Professionals : Technical Analysis

Technical analysis involves the prediction of future exchange rate (or other assetprice) movements from an inductive analysis of past movements. A reading of the large literature on this topic allows us to establish a set of stylised facts, including the facts that technical analysis is an important and widely used method of analysis in the foreign exchange market and that applying certain technical trading rules over a sustained period may lead to significant positive excess returns. We then analyze four arguments that have been put forward to explain the continuing widespread use of technical analysis and its apparent profitability: that the foreign exchange market may be characterised by not-fully-rational behaviour; that technical analysis may exploit the influence of central bank interventions; that technical analysis may be an efficient form of information processing; and finally that it may provide information on nonfundamental influences on foreign exchange movements. Although all of these positions may be relevant to some degree, neither non-rationality nor official interventions seem to be widespread and persistent enough to explain the obstinate passion of foreign exchange professionals for technical analysis. JEL-Classification: F 31
A construction of low-density parity-check codes

Low-density parity-check ( LDPC ) codes were ﬁrst presented by Gallager in 1962. They are linear block codes and their bit error rate (BER) performance approaches remarkably close to the Shannon limit. The LDPC codes created much interest after the rediscovery by Mackay and Neal in 1995. This paper introduces some new LDPC codes by considering some combinatorial structures. We present regular LDPC codes based on group divisible designs which have Tanner graphs free of four-cycles.
Theoretical Properties of Functional Multi Layer

In this paper, we study a natural extension of Multi Layer Perceptrons (MLP) to functional inputs. We show that fundamental results for numerical MLP can be extended to functional MLP. We ob- tain universal approximation results that show the expressive power of functional MLP is comparable to the one of numerical MLP. We obtain consistency results which imply that optimal parameters estimation for functional MLP is consistent.
Optical/near-infrared observations of the Fried Egg Nebula: Multiple shell ejections on a 100 yr timescale from a massive yellow hypergiant

Context. The fate of a massive star during the latest stages of its evolution is highly dependent on its mass-loss rate/geometry and therefore knowing the geometry of the circumstellar material close to the star and its surroundings is crucial. Aims. We aim to study the nature (i.e. geometry, rates) of mass-loss episodes. In this context, yellow hypergiants are great targets. Methods. We analyse a large set of optical/near-infrared data, in spectroscopic and photometric (X-shooter/VLT), spectropolarimetric (ISIS/WHT), and interferometric GRAVITY-AMBER/VLTI) modes, toward the yellow hypergiant IRAS 17163-3907. We present the first model-independent reconstructed images of IRAS 17163-3907 at these wavelengths at milli-arcsecond scales. Lastly, we apply a 2D radiative transfer model to fit the dereddened photometry and the radial profiles of published VISIR images at 8.59 {\mu}m, 11.85 {\mu}m and 12.81 {\mu}m simultaneously, adopting the revised Gaia distance (DR2). Results. The interferometric observables around 2 {\mu}m show that the Br{\gamma} emission is more extended and asymmetric than the Na i and the continuum emission. In addition to the two known shells surrounding IRAS 17163-3907 we report on the existence of a third hot inner shell with a maximum dynamical age of only 30 yr. Conclusions. The interpretation of the presence of Na i emission at closer distances to the star compared to Br{\gamma} has been a challenge in various studies. We argue that the presence of a pseudophotosphere is not needed, but it is rather an optical depth effect. The three observed distinct mass-loss episodes are characterised by different mass-loss rates and can inform the theories on mass-loss mechanisms, which is a topic still under debate. We discuss these in the context of photospheric pulsations and wind bi-stability mechanisms.
Globetrotter: Connecting Languages by Connecting Images

Training a language model, as opposed to a text representation only designed for image retrieval, has the crucial advantage that it can be finetuned to perform downstream NLP tasks. In this work we are interested in evaluating how well the representations generalize across languages, after training on a downstream task. We evaluate our model on sentence correspondence: we split sentences in two, and half of the times we swap the second half of the sentences with other sentences of the same language. The model has to determine whether or not a sentence is coherent and the beginning of the sentence corresponds to the end of the sentence. We control for uppercase, word breaks, length of sentences etc. so that the model cannot find an easy shortcut (cheat), and has to rely on the semantic and syntactic structure of the sentence. We show examples of the test in Tab. 4 for English. We train all the models for one epoch on half of the languages in the testing split (first half in alphabetical order), and test on both held-out samples from that half, and on the languages from the other half (new languages the sentence correspondence downstream task has not seen). We train a single transformer layer on top of our representation, with one head. For [7], we do not apply the max-pooling over words in order to have a representation for each word. We show results on Tab. 5. The results show that methods trained with language models are much better at performing language tasks. It also shows that our method, trained with alignment, not only performs better on the languages the downstream task has been trained on, but it also generalizes better to other languages the sentence correspondence task has never seen, indicating that the model has a very aligned representation across languages. The relative decrease in accuracy is computed as the percentage decrease of the difference between the accuracy and the chance accuracy.
Natural Language Processing and Machine Learning Classifier used for Detecting the Author of the Sentence

Detecting the author of the sentence in a collective document can be done by choosing a suitable set of features and implementing using Natural Language Processing in Machine Learning. Training our machine is the basic idea to identify the author name of a specific sentence. This can be done by using 8 different NLP steps like applying stemming algorithm, finding stop-list words, preprocessing the data, and then applying it to a machine learning classifier-Support vector machine (SVM) which classify the dataset into a number of classes specifying the author of the sentence and defines the name of author for each and every sentence with an accuracy of 82%. This paper helps the readers who are interested in knowing the names of the authors who have written some specific words
Virtual Haptic Exploratory Visualization of Line Graphs and Charts

This paper describes ongoing research investigating how visualizations, especially line-graphs and charts, may be represented by haptics both to understand the structure and the values associated with the graphical realization. Much of the current research has focused mainly on the structure of the line-graph; some more recent work has used sound to depict the value of the curve. There has been limited work on multiple curves and more complex charts, with problems occurring between the crossover points of (say) a curve. We use the PHANToM Haptic interface to feel objects within the virtual world. Our investigations are focussing on a three-stage methodology: (a) unguided exploration, where the user may wander and explore the haptic visualization in their own time, (b) Constrained navigation, where the user’s point of interest is constrained to a particular path, but the user can still explore within these constraints, and (c) Tours, where the user is completely guided round a predefined path.