Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of Wafer Applied Underfill for 3D Stacking

A key element for improving 3D IC stacking and packaging reliability is the choice of the right underfill (UF) material. The underfill is a specialized adhesive whose main purpose is locking together the top and bottom dice; it must fill the gap between bumps and between dice, while reducing the differential movement that would occur during thermal cycling. Traditional underfill processes are based on local dispensing after solder bump reflow (i.e., capillary dispensing), or before flip chip operation with no need of reflow (no flow underfill, NUF). In case of 3D stacking, such processes present some limitations: the need for a dispensing area (die size increase); material flow (spacing between dice), and cost (low throughput). After an introduction on typical underfill applications such as die-to-package and die-to-die assembly, we report the work done to assess the properties of several wafer applied underfill (WAUF) materials and their integration in 3D stacking. These materials were initially applied .
Trace Chemical Analysis Methodology

Abstract : A method was developed for the analysis of aqueous nitrates and nitrites. In this method, the sample is treated with benzene, hydrogen peroxide (for nitrite ion analysis), and sulfuric acid to produce nitrobenzene which is then detected by electron capture gas chromatography. This analytical method was adapted to determine oxides of nitrogen in air. A system to sample and analyze the hydrocarbon portion of jet engine exhaust to assess its environmental consequences was evaluated. Exhaust samples were collected and analyzed from five jet fuels and isooctane. The samples were collected on a combination sorbent trap packed with Tenax GC and Carbosieve B, then desorbed into a gas chromatograph.
A Kinetic Expression for the Growth and Decay of Nitrite Oxidising Bacteria

Two functions were newly developed to model the inhibition of nitrite oxidising bacteria (NOB) for partial nitritation of ammonia to nitrite. Traditional non-competitive and competitive inhibition rate expressions could be applied when NOB were exposed to nitrite for short period of time. On the other hand, in long-term batch tests lasting for over 2 days, it appeared that NOB’s oxygen uptake rate (OUR) recovered within the initial few hours. This recovery was thought to be an adaptation phenomenon from the shock loading that existed on the growth stage of NOB. Upon a peak value, the OUR decreased continuously and this was attributed to poisoning that contributed to the acceleration of decay. To express the growth recovery and the poisoning decay, a new mathematical model was elaborated based on the modification of an existing Activated Sludge Model (ASM). In the model a time-dependent adaptation function on the Monod-type growth process and an additional first-order poisoning function on the decay process were included. The developed model could reproduce the batch OUR curves having various shapes depending on different initial nitrite concentrations (125–2,000 mg-N/ l ).
The ionic hydrogen bond and ion solvation. 2. Solvation of onium ions by one to seven water molecules. Relations between monomolecular, specific, and bulk hydrogen

The relation between enthalpies of solvation of onium ions, BH/sup +/, by one water molecule, -. delta.H/sup 0//sub 0/ /sub 1/, and by four water molecules, -. delta.H/sup 0//sub 0/ /sub 4/, is constant for most onium ions: . delta.H/sup 0//sub 0/ /sub 4//. delta.H/sup 0//sub 0/ /sub 1/ is 2.8 +/- 0.1 for all oxonium ions and monoprotonic ammonium and pyridium ions, and 3.1 +/- 0.1 for polyprotonic ammonium ions. These relations, in conjunction with the correlation between . delta.H/sup 0//sub 0/ /sub 1/ and the proton affinity difference (.delta.PA = PA(B) - PA(H/sub 2/O)), allow the prediction of the total four-molecule specific hydration energy -. delta.H/sup 0//sub 0/ /sub 4/ for all onium ions within the experimental accuracy of +/-3 kcal mol/sup -1/. The observed (or predicted) fourfold specific relative hydration energies simulate closely the relative bulk hydration enthalpies for most ions. In other words, for most onium ions differential hydration effects are determined by specific hydrogen-bonding interactions. Deviations are useful to identify bulk solvation effects. For example, such deviations indicate attenuated bulk solvation of ions with phenyl substituents. 5 figures, 2 tables.
Spectrophotometric determination of ruthenium with prochlorperazine bis(methanesulfonate).

Ru was detd. spectrophotometrically by reaction with prochlorperazine bis(methanesulfonate) in H3PO4 medium to form a red-colored product. The absorbance was measured at 529 nm. Beer's law was obeyed for 0.2-7.3 ppm Ru with an optimum concn. range of 0.7-6.9 ppm. The molar absorptivity and Sandell sensitivity were 1.17 A� 104 and 8.6 ng/cm2, resp. The std. deviation was Â±0.025 for 3 ppm Ru. The effects of foreign ions were studied. [on SciFinder(R)]
Synthesis, Characterization and AC-Impedance Spectroscopic Studies on Interfacial Synthesis of Pani / PEG Nanocomposite

Polyaniline/PEG nanocomposite was prepared via a interfacial polymerization method by using ammonium peroxedisulphate as an oxidizing agent. A mechanism of formation of polyaniline-polyethylene glycol has been suggested in which polyethylene glycol works as a medium for diffusion-limited growth of the components during their precipitation. On studied AC impedance spectroscopy Studied, it was found that the electrical conductivity of interfacialy synthesized PAni-PEG composite 3.96 x 10 -2 Scm -1 was slightly low than bulk PAni (1.87 x 10 -1 Scm -1 ) due to formation of nanostructures. These materials were characterized by FTIR, UV- Visible, XRD, SEM and AC-impedance spectroscopy.
Matériaux composites à matrice époxyde chargée par des fibres de palmier dattier : effet de l’oxydation au tempo sur les fibres

A travers cette these, nous avons pu valoriser les rejets agricoles du palmier dattier (Phoenix L. dactylifera) (fibres issues des folioles) par leur introduction comme renfort fibreux dans la preparation des biocomposites a matrice polyepoxyde (DGEBA/IPD). Le but de la premiere partie de la these etait l’etude de la modification chimique des fibres de palmier par oxydation au 2,2, 6,6–tetramethylpiperidine-1-oxyle (TEMPO). Le suivi cinetique et la topologie de la reaction, ainsi que la caracterisation des fibres a l’etat brut et a l’etat oxyde, ont ete aussi etudies. Un modele cinetique original a ete propose ensuite rendant compte du caractere heterogene de cette reaction chimique. La deuxieme partie etait consacree a l’etude cinetique et rheocinetique de la polymerisation du systeme DGEBA/IPD seul et en presence de fibres brutes et oxydees. Parallelement, l’etude thermique de la polymerisation du reseau final a ete egalement realisee. La derniere partie avait comme but l’etude des proprietes mecaniques aux petites et grandes deformations de ces composites et mettre en evidence l’effet de l’oxydation des fibres sur ces proprietes. D’autre part, le suivi de mise en forme par RTM (moulage par transfert de resine) a montre un effet positif de l’oxydation sur le deroulement de l’injection. Une meilleure mouillabilite du mat des fibres oxydees par la resine est a l’origine de l’amelioration du procede.
Next generation particle precipitation: Mesoscale prediction through machine learning (a case study and framework for progress)

We advance the modeling capability of electron particle precipitation from the magnetosphere to the ionosphere through a new database and use of machine learning tools to gain utility from those data. We have compiled, curated, analyzed, and made available a new and more capable database of particle precipitation data that includes 51 satellite years of Defense Meteorological Satellite Program (DMSP) observations temporally aligned with solar wind and geomagnetic activity data. The new total electron energy flux particle precipitation nowcast model, a neural network called PrecipNet, takes advantage of increased expressive power afforded by machine learning approaches to appropriately utilize diverse information from the solar wind and geomagnetic activity and, importantly, their time histories. With a more capable representation of the organizing parameters and the target electron energy flux observations, PrecipNet achieves a >50% reduction in errors from a current state-of-the-art model (OVATION Prime), better captures the dynamic changes of the auroral flux, and provides evidence that it can capably reconstruct mesoscale phenomena. We create and apply a new framework for space weather model evaluation that culminates previous guidance from across the solar-terrestrial research community. The research approach and results are representative of the `new frontier' of space weather research at the intersection of traditional and data science-driven discovery and provides a foundation for future efforts.
Discovery of an M-type companion to the Herbig Ae Star V1787 Ori

The intermediate mass Herbig Ae star V1787 Ori is a member of the L1641 star-forming region in the Orion A molecular cloud. We report the detection of an M-type companion to V1787 Ori at a projected separation of 6.66" (corresponding to 2577 au), from the analysis of VLT/NACO adaptive optics $K_s$-band image. Using astrometric data from Gaia DR2, we show that V1787 Ori A and B share similar distance ($d$ $\sim$ 387 pc) and proper motion, indicating that they are physically associated. We estimate the spectral type of V1787 Ori B to be M5 $\pm$ 2 from color--spectral type calibration tables and template matching using SpeX spectral library. By fitting PARSEC models in the Pan-STARRS color-magnitude diagram, we find that V1787 Ori B has an age of 8.1$^{+1.7}_{-1.5}$ Myr and a mass of 0.39$^{+0.02}_{-0.05}$ $M_\odot$. We show that V1787 Ori is a pre-main sequence wide binary system with a mass ratio of 0.23. Such a low mass ratio system is rarely identified in Herbig Ae/Be binary systems. We conclude this work with a discussion on possible mechanisms for the formation of V1787 Ori wide binary system.
Probability bound analysis: A novel approach for quantifying parameter uncertainty in decision‐analytic modeling and cost‐effectiveness analysis

Decisions about health interventions are often made using limited evidence. Mathematical models used to inform such decisions often include uncertainty analysis to account for the effect of uncertainty in the current evidence base on decision‐relevant quantities. However, current uncertainty quantification methodologies, including probabilistic sensitivity analysis (PSA), require modelers to specify a precise probability distribution to represent the uncertainty of a model parameter. This study introduces a novel approach for representing and propagating parameter uncertainty, probability bounds analysis (PBA), where the uncertainty about the unknown probability distribution of a model parameter is expressed in terms of an interval bounded by lower and upper bounds on the unknown cumulative distribution function (p‐box) and without assuming a particular form of the distribution function. We give the formulas of the p‐boxes for common situations (given combinations of data on minimum, maximum, median, mean, or standard deviation), describe an approach to propagate p‐boxes into a black‐box mathematical model, and introduce an approach for decision‐making based on the results of PBA. We demonstrate the characteristics and utility of PBA vs PSA using two case studies. In sum, this study provides modelers with practical tools to conduct parameter uncertainty quantification given the constraints of available data and with the fewest assumptions.
Simulation-based Testing for Early Safety-Validation of Robot Systems

Industrial human-robot collaborative systems must be validated thoroughly with regard to safety. The sooner potential hazards for workers can be exposed, the less costly is the implementation of necessary changes. Due to the complexity of robot systems, safety flaws often stay hidden, especially at early design stages, when a physical implementation is not yet available for testing. Simulation-based testing is a possible way to identify hazards in an early stage. However, creating simulation conditions in which hazards become observable can be difficult. Brute-force or Monte-Carlo-approaches are often not viable for hazard identification, due to large search spaces. This work addresses this problem by using a human model and an optimization algorithm to generate high-risk human behavior in simulation, thereby exposing potential hazards. A proof of concept is shown in an application example where the method is used to find hazards in an industrial robot cell.
Unbalanced Incomplete Multi-View Clustering Via the Scheme of View Evolution: Weak Views are Meat; Strong Views Do Eat

Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous methods assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using previous methods. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view clustering problem; 2) it designs the low-rank representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods. We provide codes for all of our experiments in https://github.com/ZeusDavide/TETCI_UIMC.
Cost–effective Variational Active Entity Resolution

Accurately identifying different representations of the same real–world entity is an integral part of data cleaning and many methods have been proposed to accomplish it. The challenges of this entity resolution task that demand so much research attention are often rooted in the task–specificity and user–dependence of the process. Adopting deep learning techniques has the potential to lessen these challenges. In this paper, we set out to devise an entity resolution method that builds on the robustness conferred by deep autoencoders to reduce human–involvement costs. Specifically, we reduce the cost of training deep entity resolution models by performing unsupervised representation learning. This unveils a transferability property of the resulting model that can further reduce the cost of applying the approach to new datasets by means of transfer learning. Finally, we reduce the cost of labeling training data through an active learning approach that builds on the properties conferred by the use of deep autoencoders. Empirical evaluation confirms the accomplishment of our cost–reduction desideratum, while achieving comparable effectiveness with state–of–the–art alternatives.
Low-Complexity Angle-Domain MIMO NOMA System with partial channel state information for MmWave Communications

In millimeter-wave communication, digital beamsteering (DBS), only based on the user direction, is a promising angle-domain multi-antenna technique to mitigate the severe path loss and multiuser interference, with low-complexity and partial channel state information (CSI). In this paper, we design a power-domain non-orthogonal multiple access (NOMA) scheme that enhances the DBS performance trading-off complexity , energy-consumption and capacity performance. In particular, we propose a user-clustering algorithm to pair users, based on a geometric-interference metric, so that the inter-user interference is reduced. Afterward, based on a fixed inter-cluster power allocation, we derive analytically a sub-optimal intra-cluster power allocation optimization problem to maximize the network throughput. To address the issue of partial CSI, we rewrite the aforementioned optimization problem, by relying only on the user direction. Performance evaluation of the proposed schemes is developed in rural environment, based on the New York University millimeter-wave simulator. The obtained results demonstrate that the proposed low-complexity NOMA-DBS schemes with either full-or partial-CSI achieve significant performance improvement over the classical DBS, in terms of spectral-and energy-efficiencies (up to 26.8% bps/Hz rate gain for 45 users using the proposed scheme with partial CSI).
Full-Duplex Non-Orthogonal Multiple Access Cooperative Overlay Spectrum-Sharing Networks With SWIPT

This article proposes a novel non-orthogonal multiple access (NOMA) assisted cooperative spectrum-sharing network, in which one of the full-duplex (FD) secondary transmitters (STs) is chosen among many for forwarding the primary transmitter’s and its own information to primary receiver and secondary receivers, respectively, using NOMA technique. To stimulate the ST to conduct cooperative transmission and sustain its operations, the simultaneous wireless information and power transfer (SWIPT) technique is utilized by the ST to harvest the primary signal’s energy. In order to evaluate the proposed system’s performance, the outage probability and system throughput for the primary and secondary networks are derived in tight closed-form approximations. Further, the sum rate optimization problem is formulated for the proposed cooperative network and a rapid convergent iterative algorithm is proposed to obtain the optimized power allocation coefficients. Numerical results show that FD, SWIPT, and NOMA techniques greatly boost the performance of cooperative spectrum-sharing network in terms of outage probability, system throughput, and sum rate compared to that of half-duplex NOMA and the conventional orthogonal multiple access-time division multiple access networks.
Long Short Term Memory Networks for Bandwidth Forecasting in Mobile Broadband Networks under Mobility

Bandwidth forecasting in Mobile Broadband (MBB) networks is a challenging task, particularly when coupled with a degree of mobility. In this work, we introduce HINDSIGHT++, an open-source R-based framework for bandwidth forecasting experimentation in MBB networks with Long Short Term Memory (LSTM) networks. We instrument HINDSIGHT++ following an Automated Machine Learning (AutoML) paradigm to first, alleviate the burden of data preprocessing, and second, enhance performance related aspects. We primarily focus on bandwidth forecasting for Fifth Generation (5G) networks. In particular, we leverage 5Gophers, the first open-source attempt to measure network performance on operational 5G networks in the US. We further explore the LSTM performance boundaries on Fourth Generation (4G) commercial settings using NYU-METS, an open-source dataset comprising of hundreds of bandwidth traces spanning different mobility scenarios. Our study aims to investigate the impact of hyperparameter optimization on achieving state-of-the-art performance and beyond. Results highlight its significance under 5G scenarios showing an average Mean Absolute Error (MAE) decrease of near 30% when compared to prior state-of-the-art values. Due to its universal design, we argue that HINDSIGHT++ can serve as a handy software tool for a multitude of applications in other scientific fields.
Learning Synthetic to Real Transfer for Localization and Navigational Tasks

Autonomous navigation consists in an agent being able to navigate without human intervention or supervision, it affects both high level planning and low level control. Navigation is at the crossroad of multiple disciplines, it combines notions of computer vision, robotics and control. This work aimed at creating, in a simulation, a navigation pipeline whose transfer to the real world could be done with as few efforts as possible. Given the limited time and the wide range of problematic to be tackled, absolute navigation performances while important was not the main objective. The emphasis was rather put on studying the sim2real gap which is one the major bottlenecks of modern robotics and autonomous navigation. To design the navigation pipeline four main challenges arise; environment, localization, navigation and planning. The iGibson simulator is picked for its photo-realistic textures and physics engine. A topological approach to tackle space representation was picked over metric approaches because they generalize better to new environments and are less sensitive to change of conditions. The navigation pipeline is decomposed as a localization module, a planning module and a local navigation module. These modules utilize three different networks, an image representation extractor, a passage detector and a local policy. The laters are trained on specifically tailored tasks with some associated datasets created for those specific tasks. Localization is the ability for the agent to localize itself against a specific space representation. It must be reliable, repeatable and robust to a wide variety of transformations. Localization is tackled as an image retrieval task using a deep neural network trained on an auxiliary task as a feature descriptor extractor. The local policy is trained with behavioral cloning from expert trajectories gathered with ROS navigation stack.
Local monodromy formula of Hadamard products

We find an explicit general formula for the iterated local monodromy of singularities of the Hadamard product of functions with integrable singularities. The formula implies the invariance by Hadamard product of the class of functions with integrable singularities with recurrent monodromies. In particular, it implies the recurrence of the local monodromy of functions with finite Hadamard grade as defined by Allouche and Mendes-France. We give other examples of natural classes of functions with recurrent monodromies, functions with algebro-logarithmic singularities, and more generally with polylogarithm monodromies. We sketch applications to elliptic integrals, hypergeometric functions, and to fractional integration.
Low-Dimensional Manifolds Support Multiplexed Integrations in Recurrent Neural Networks

We study the learning dynamics and the representations emerging in recurrent neural networks (RNNs) trained to integrate one or multiple temporal signals. Combining analytical and numerical investigations, we characterize the conditions under which an RNN with n neurons learns to integrate D(≪n) scalar signals of arbitrary duration. We show, for linear, ReLU, and sigmoidal neurons, that the internal state lives close to a D-dimensional manifold, whose shape is related to the activation function. Each neuron therefore carries, to various degrees, information about the value of all integrals. We discuss the deep analogy between our results and the concept of mixed selectivity forged by computational neuroscientists to interpret cortical recordings.