Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining LoRaWAN and a New 3D Motion Model for Remote UAV Tracking

Over the last few years, the many uses of Unmanned Aerial Vehicles (UAVs) have captured the interest of both the scientific and the industrial communities. A typical scenario consists in the use of UAVs for surveillance or target-search missions over a wide geographical area. In this case, it is fundamental for the command center to accurately estimate and track the trajectories of the UAVs by exploiting their periodic state reports. In this work, we design an ad hoc tracking system that exploits the Long Range Wide Area Network (LoRaWAN) standard for communication and an extended version of the Constant Turn Rate and Acceleration (CTRA) motion model to predict drone movements in a 3D environment. Simulation results on a publicly available dataset show that our system can reliably estimate the position and trajectory of a UAV, significantly outperforming baseline tracking approaches.
A Visual-inertial Navigation Method for High-Speed Unmanned Aerial Vehicles

This paper investigates the localization problem of high-speed high-altitude unmanned aerial vehicle (UAV) with a monocular camera and inertial navigation system. It proposes a navigation method utilizing the complementarity of vision and inertial devices to overcome the singularity which arises from the horizontal flight of UAV. Furthermore, it modifies the mathematical model of localization problem via separating linear parts from nonlinear parts and replaces a nonlinear least-squares problem with a linearly equality-constrained optimization problem. In order to avoid the ill-condition property near the optimal point of sequential unconstrained minimization techniques(penalty methods), it constructs a semi-implicit continuous method with a trust-region technique based on a differential-algebraic dynamical system to solve the linearly equality-constrained optimization problem. It also analyzes the global convergence property of the semi-implicit continuous method in an infinity integrated interval other than the traditional convergence analysis of numerical methods for ordinary differential equations in a finite integrated interval. Finally, the promising numerical results are also presented.
FastPET: Near Real-Time PET Reconstruction from Histo-Images Using a Neural Network

Direct reconstruction of positron emission tomography (PET) data using deep neural networks is a growing field of research. Initial results are promising, but often the networks are complex, memory utilization inefficient, produce relatively small 2D image slices (e.g., 128x128), and low count rate reconstructions are of varying quality. This paper proposes FastPET, a novel direct reconstruction convolutional neural network that is architecturally simple, memory space efficient, works for non-trivial 3D image volumes and is capable of processing a wide spectrum of PET data including low-dose and multi-tracer applications. FastPET uniquely operates on a histo-image (i.e., image-space) representation of the raw data enabling it to reconstruct 3D image volumes 67x faster than Ordered subsets Expectation Maximization (OSEM). We detail the FastPET method trained on whole-body and low-dose whole-body data sets and explore qualitative and quantitative aspects of reconstructed images from clinical and phantom studies. Additionally, we explore the application of FastPET on a neurology data set containing multiple different tracers. The results show that not only are the reconstructions very fast, but the images are high quality and lower noise than iterative reconstructions.
Intersubband Transition Engineering in the Conduction Band of Asymmetric Coupled Ge/SiGe Quantum Wells

n-type Ge/SiGe asymmetric coupled quantum wells represent the building block of a variety of nanoscale quantum devices, including recently proposed designs for a silicon-based THz quantum cascade laser. In this paper, we combine structural and spectroscopic experiments on 20-module superstructures, each featuring two Ge wells coupled through a Ge-rich SiGe tunnel barrier, as a function of the geometry parameters of the design and the P dopant concentration. Through a comparison of THz spectroscopic data with numerical calculations of intersubband optical absorption resonances, we demonstrated that it is possible to tune, by design, the energy and the spatial overlap of quantum confined subbands in the conduction band of the heterostructures. The high structural/interface quality of the samples and the control achieved on subband hybridization are promising starting points towards a working electrically pumped light-emitting device.
Two Huge Title and Keyword Generation Corpora of Research Articles

Recent developments in sequence-to-sequence learning with neural networks have considerably improved the quality of automatically generated text summaries and document keywords, stipulating the need for even bigger training corpora. Metadata of research articles are usually easy to find online and can be used to perform research on various tasks. In this paper, we introduce two huge datasets for text summarization (OAGSX) and keyword generation (OAGKX) research, containing 34 million and 23 million records, respectively. The data were retrieved from the Open Academic Graph which is a network of research profiles and publications. We carefully processed each record and also tried several extractive and abstractive methods of both tasks to create performance baselines for other researchers. We further illustrate the performance of those methods previewing their outputs. In the near future, we would like to apply topic modeling on the two sets to derive subsets of research articles from more specific disciplines.
Energy Efficiency Optimization for Downlink Massive MIMO With Statistical CSIT

We investigate energy efficiency (EE) optimization for single-cell massive multiple-input multiple-output (MIMO) downlink transmission with only statistical channel state information (CSI) available at the base station. We first show that beam domain transmission is favorable for energy efficiency in the massive MIMO downlink, by deriving a closed-form solution for the eigenvectors of the optimal transmit covariance matrix. With this conclusion, the EE optimization problem is reduced to a real-valued power allocation problem, which is much easier to tackle than the original large-dimensional complex matrix-valued precoding design problem. We further propose an iterative water-filling-structured beam domain power allocation algorithm with low complexity and guaranteed convergence, exploiting the techniques from sequential optimization, fractional optimization, and random matrix theory. Numerical results demonstrate the near-optimal performance of our proposed statistical CSI aided EE optimization approach.
Stem cell lineage survival as a noisy competition for niche access

Significance What defines the number and dynamics of the stem cells that generate and renew biological tissues? Although several molecular markers have been described to predict stem cell potential, we propose a complementary approach that mathematically describes “stemness” as an emergent property arising from a stochastic competition for space. We predict from that competition the robust emergence of a region made of functional stem cells, as well as give simple predictions on lineage-survival probability. We test our results with data obtained from intravital live-imaging experiments in mammary gland development, existing data from kidney development, and from the self-renewal of the crypt to show that our framework can predict the number of functional stem cells and lineage-survival probability. Understanding to what extent stem cell potential is a cell-intrinsic property or an emergent behavior coming from global tissue dynamics and geometry is a key outstanding question of systems and stem cell biology. Here, we propose a theory of stem cell dynamics as a stochastic competition for access to a spatially localized niche, giving rise to a stochastic conveyor-belt model. Cell divisions produce a steady cellular stream which advects cells away from the niche, while random rearrangements enable cells away from the niche to be favorably repositioned. Importantly, even when assuming that all cells in a tissue are molecularly equivalent, we predict a common (“universal”) functional dependence of the long-term clonal survival probability on distance from the niche, as well as the emergence of a well-defined number of functional stem cells, dependent only on the rate of random movements vs. mitosis-driven advection. We test the predictions of this theory on datasets of pubertal mammary gland tips and embryonic kidney tips, as well as homeostatic intestinal crypts. Importantly, we find good agreement for the predicted functional dependency of the competition as a function of position, and thus functional stem cell number in each organ. This argues for a key role of positional fluctuations in dictating stem cell number and dynamics, and we discuss the applicability of this theory to other settings.
Goldilocks Neural Networks

We introduce the new "Goldilocks" class of activation functions, which non-linearly deform the input signal only locally when the input signal is in the appropriate range. The small local deformation of the signal enables better understanding of how and why the signal is transformed through the layers. Numerical results on CIFAR-10 and CIFAR-100 data sets show that Goldilocks networks perform better than, or comparably to SELU and RELU, while introducing tractability of data deformation through the layers.
LaProp: a Better Way to Combine Momentum with Adaptive Gradient

Identifying a divergence problem in Adam, we propose a new optimizer, LaProp, which belongs to the family of adaptive gradient descent methods. This method allows for greater flexibility in choosing its hyperparameters, mitigates the effort of fine tuning, and permits straightforward interpolation between the signed gradient methods and the adaptive gradient methods. We bound the regret of LaProp on a convex problem and show that our bound differs from the previous methods by a key factor, which demonstrates its advantage. We experimentally show that LaProp outperforms the previous methods on a toy task with noisy gradients, optimization of extremely deep fully-connected networks, neural art style transfer, natural language processing using transformers, and reinforcement learning with deep-Q networks. The performance improvement of LaProp is shown to be consistent, sometimes dramatic and qualitative.
Spectral multipliers for Schr\"odinger operators

We prove a sharp Hormander multiplier theorem for Schrodinger operators $H=-\Delta+V$ on $\mathbb{R}^n$. The result is obtained under certain condition on a weighted $L^\infty$ estimate, coupled with a weighted $L^2$ estimate for $H$, which is a weaker condition than that for nonnegative operators via the heat kernel approach. Our approach is elaborated in one dimension with potential $V$ belonging to certain critical weighted $L^1$ class. Namely, we assume that $\int (1+|x|) |V(x)|dx$ is finite and $H$ has no resonance at zero. In the resonance case we assume $\int (1+|x|^2) |V(x)| dx$ is finite.
A Review of Online Product Reviews

In the rapidly growing Internet of China, online shopping has spread to the daily lives of most people. Consumers need to consider the characteristics, quality, price, and other information of the product when making purchases on the Internet platform, and carefully select them to improve the satisfaction of shopping. Online product review is one of the sources of product feature information and is increasingly valued by online consumers. This article will review the literature of online product reviews from the generation of online product reviews, outcome variables, usefulness, and influencing factors. We will sort out current academic achievements, and look for future research directions. Follow-up researches should use scientific analysis techniques and methods to establish theoretical models, and explore the mechanism of usefulness of online product reviews.
A Novel Factor Essential for Unconventional Secretion of Chitinase Cts1

Subcellular targeting of proteins is essential to orchestrate cytokinesis in eukaryotic cells. During cell division of Ustilago maydis, for example, chitinases must be specifically targeted to the fragmentation zone at the site of cell division to degrade remnant chitin and thus separate mother and daughter cells. Chitinase Cts1 is exported to this location via an unconventional secretion pathway putatively operating in a lock-type manner. The underlying mechanism is largely unexplored. Here, we applied a forward genetic screen based on UV mutagenesis to identify components essential for Cts1 export. The screen revealed a novel factor termed Jps1 lacking known protein domains. Deletion of the corresponding gene confirmed its essential role for Cts1 secretion. Localization studies demonstrated that Jps1 colocalizes with Cts1 in the fragmentation zone of dividing yeast cells. While loss of Jps1 leads to exclusion of Cts1 from the fragmentation zone and strongly reduced unconventional secretion, deletion of the chitinase does not disturb Jps1 localization. Yeast-two hybrid experiments suggest that the two proteins interact. In essence, we identified a novel component of unconventional secretion that functions in the fragmentation zone to enable export of Cts1. We hypothesize that Jps1 acts as an anchoring factor, supporting the proposed novel lock-type mechanism of unconventional secretion.
Impact of Mind–Body Medicine Professional Skills Training on Healthcare Professional Burnout

Background Healthcare professional burnout has reached epidemic proportions, with downstream effects on personal and patient health and on our institutions. Solutions lie in the domains of work culture, operational efforts, and personal strategies. Objectives To evaluate the impact of a 5-day mind–body medicine professional training program on burnout and quality of life. Methods We conducted pre- and postevaluation of a mind–body medicine skills training for healthcare professionals on 6 wellness domains using 2 validated instruments: the Maslach Burnout Inventory and the Professional Quality of Life Survey. Results There was a statistically significant improvement in changes in emotional exhaustion, depersonalization, personal accomplishment, compassion satisfaction, burnout, and secondary traumatic stress which was sustained at 12 months. Largest relative improvements occurred in emotional exhaustion and depersonalization, 22% and 21%, respectively. Conclusion In addition to providing an important patient care skill set, mind–body medicine training may be an effective way to mitigate burnout and improve healthcare professional well-being.
FROM HARDSHIP TO LEADERSHIP: THE SUCCESS STORIES OF PHILIPPINE SCHOOL DOHA STUDENT LEADERS

Mellisa Montayre, Chris Daniel A. Collado, Carlos Albert P. Meguizo, Francine Mari C. Dionisio, Hannah Corine T. Lameg, Trixie Mae P. Munoz and Rhiezel A. Pumarejo . Manuscript Info Abstract . Manuscript History Received: 25 November 2019 Final Accepted: 27 December 2019 Published: January 2020
Effect of temperature on the silylation of nanocrystalline cellulose from oil palm empty fruit bunch with 3-aminopropyltriethoxysilane

Nanocrystalline cellulose (NCC) was obtained through the oxidation reaction of oil palm empty fruit bunch (OPEFB) pulp and 4-acetamido- 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO) followed by ultrasonication. The aim of this study the effects of temperature on the silylation of nanocrystalline cellulose (NCC) using OPEFB-NCC and 3-aminopropyl-triethoxysilane (APS). The reaction temperatures use in the silylation process were 60, 70, 80, 90 and 100°C. FTIR of the distinctive peaks resulting from the silylation process at 810 cm−1, broad peaks at 1028-1157 cm−1 and at 2928 cm−1 were used as the basis for investigating the influence of temperature on the silylation reaction. In addition, a visual examination of the final product was also carried out. Based on the FTIR analyses and aided by visual assessment, it can be deduced that a medium temperature of 80°C is the optimum silylation reaction temperature.
Determinants of Efficiency: Evidences from Financially Distress Firms in Malaysia

In this paper, we analyse a panel data of 190 financially distressed firms to determine which the firm-specific variable is reliably important in explaining the level of efficiency in the financial distress firms in Malaysia. Optimal model selection procedure, together with the panel data analysis technique are used to determine the most optimal model to explain the level of efficiency in the financial distress firm. In addition to that, this paper finds the evidence suggesting the relevance of inventory turnover to current assets, account receivable , and inventory turnover, which implies the importance of these factors in determining the level of efficiency in the financial distress firm. Other variables , such as firm’s size, return on assets, working capital, total asset turnover, liquidity, tangible asset s, intangible assets, and investment opportunities have no significant impacts on the status of firms in the financial distress. This paper uses random effects (RE) models to analyse the variables used and the result is positive towards the relationship on three variables chosen which are ITCA, ARQ, and
A Cyber-Physical Residential Energy Management System via Virtualized Packets

This paper proposes a cyber-physical system to manage flexible residential loads based on virtualized energy packets. Before being used, flexible loads need to request packets to an energy server, which may be granted or not. If granted, the energy server guarantees that the request will be fulfilled. Each different load has a specific consumption profile and user requirement. In the proposed case study, the residential consumers share a pool of energy resources that need to be allocated by the energy server whose aim is to minimize the imports related to such a group. The proposed solution shows qualitative advantages compared to the existing approaches in relation to computational complexity, fairness of the resource allocation outcomes and effectiveness in peak reduction. We demonstrate our solution based on three different representative flexible loads; namely, electric vehicles, saunas and dishwashers. The numerical results show the efficacy of the proposed solution for three different representative examples, demonstrating the advantages and drawbacks of different allocation rules.
Performance of Local Versus International Focus Malaysian-based Mutual Funds

In light of the liberalisation in Malaysia’s mutual fund industry, this study examines the performance of 180 internationally-focused against 191 locally-focused Malaysian-based funds using augmented Henriksson-Merton market timing model on daily data starting from January 1995 until December 2015. The results show that the funds generally outperform the market, despite the persistently perverse market timing ability. Although geographically disadvantaged, locally-focused funds prove to be superior to the internationally-focused funds both in terms of returns and risks owing to the informational advantage to better select the stocks from their own market. The results also reveal that size effect is imminent regardless of geographical focus, value investing is significant only among locally-focused funds, while momentum is marginally effective.
Duty to Warn: Antidepressant Black Box Suicidality Warning Is Empirically Justified

The United States Food and Drug Administration issued a Black Box warning in October 2004 after placebo-controlled trials of antidepressant medications found an increased risk of suicidal thoughts and behaviors among children and adolescents taking antidepressant medications relative to placebo. Subsequently, some researchers have concluded that the Black Box warning caused severe unintended consequences; specifically, they have argued that the warning led to reduced use of antidepressants among youth, which led to more suicides. In this paper, we critically examine research regarding the Black Box warning’s alleged deleterious consequences. One study claimed that controlled trials did not actually find an increased risk of suicidality among youth taking fluoxetine relative to those taking placebo, but its measure of suicidality is likely invalid. We found that ecological time series studies claiming that decreasing antidepressant prescriptions are linked to higher rates of suicide attempts or actual suicides among youth were methodologically weak. These studies exhibited shortcomings including: selective use of time points, use of only a short-term time series, lack of performing statistical analysis, not examining level of severity/impairment among participants, inability to control confounding variables, and/or use of questionable measures of suicide attempts. Further, while some time-series studies claim that increased antidepressant prescriptions are related to fewer youth suicides, more recent data suggests that increasing antidepressant prescriptions are related to more youth suicide attempts and more completed suicides among American children and adolescents. We also note that case-control studies show increased risk of suicide attempts and suicide among youth taking antidepressants, even after controlling for some relevant confounds. As clinical trials have the greatest ability to control relevant confounds, it is important to remember such trials demonstrated increased risk of suicidality adverse events among youth taking antidepressants. The Black Box warning is firmly rooted in solid data whereas attempts to claim the warning has caused harm are based on quite weak evidence.
Investigating the “Flow” Experience: Key Conceptual and Operational Issues

The “flow” experience (Csikszentmihalyi, 1975) has been the focus of a large body of empirical work spanning more than four decades. Nevertheless, advancement in understanding – beyond what Csikszentmihalyi uncovered during his initial breakthrough in 1975 – has been modest. In this conceptual analysis, it is argued that progress within the field has been impeded by a lack of consistency in how flow is operationalized, and that this inconsistency in part reflects an underlying confusion regarding what flow is. Flow operationalizations from papers published within the past 5 years are reviewed. Across the 42 reviewed studies, flow was operationalized in 24 distinct ways. Three specific points of inconsistency are then highlighted: (1) inconsistences in operationalizing flow as a continuous versus discrete construct, (2) inconsistencies in operationalizing flow as inherently enjoyable (i.e., “autotelic”) or not, and (3) inconsistencies in operationalizing flow as dependent on versus distinct from the task characteristics proposed to elicit it (i.e., the conditions/antecedents). After tracing the origins of these discrepancies, the author argues that, in the interest of conceptual intelligibility, flow should be conceptualized and operationalized exclusively as a discrete, highly enjoyable, “optimal” state of consciousness, and that this state should be clearly distinguished from the conditions proposed to elicit it. He suggests that more mundane instances of goal-directed engagement are better conceived and operationalized as variations in task involvement rather than variations in flow. Additional ways to achieve greater conceptual and operational consistency within the field are suggested.