Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial-measurement–induced entanglement dynamics in a Josephson-junction system

We study the continuous-partial-measurement–induced entanglement dynamics of two phase qubits in a superconducting loop coupled by capacity. It is shown that entanglement does not change monotonously during the measurement process, and is robust against small fluctuations of the tunneling probability. We find entanglement sudden birth induced by continuous partial measurement for an initially separable state. Furthermore, we show that with the help of continuous partial measurement an almost pure state can be generated from a totally mixed state.
Nonclinical Testing Evaluation of Liposomes as Drug Delivery Systems

Various marketed drugs, as well as many in-development, have utilized liposomes, vesicles composed of one or more phospholipid bilayers, as a drug delivery system, often with the statement that they are “non-toxic” materials. This paper examined safety testing considerations and reviewed nonclinical packages used to support the safe clinical use and marketing of drugs using a liposomal drug delivery system, including liposome-only study findings. It was found that most experience has come from use of an established drug (especially in the oncology field) in a liposome formulation with known excipients. From this knowledge, it is proposed that the minimal package of studies (using an oncology indication as an example) needed to support clinical entry should include in vivo pharmacology in selected mouse xenograft models, pharmacokinetic characterization showing enhanced kinetics or disposition and including tumor exposure evaluation along with repeat-dose toxicity testing in one species. It was also found that the liposomes used in drug delivery systems are not truly “non-toxic” materials. However, the majority of findings in toxicity testing relate to macrophage processing of large amounts of lipid material, with no human known safety consequence. Of note, however, are cases of hypersensitivity for some PEGylated liposome forms which translate to the clinic.
Electronic structure of KTiOAsO4, a novel material for non-linear optical applications

A high-quality KTiOAsO4 (KTA) single crystal has been successfully synthesized employing the high temperature solution growth technique with rotating and pooling. The XPS valence-band spectra have been measured for pristine and the 1.5 keV Ar+ ion-bombarded thin (001)KTA plate cut from the crystal part that was without any optical inhomogeneities or domain boundaries. The present XPS measurements have revealed the existence of two O 2s subbands on the XPS spectrum of the pristine (001)KTA surface. It has been established that 1.5 keV Ar+ ion bombardment of the (001)KTA surface causes the complete elimination of the O 2s sub-band related to oxygen atoms involved in the formation of Ti–O–As bonds in KTA. In addition, the XPS results reveal that such a treatment leads to a significant decrease of the relative intensity of the XPS As5+ 3d core-level spectrum and causes the formation of the additional As0 3d core-level spectrum in the topmost layer of the (001)KTA surface. The experimental data are compared to the results of the first-principles band-structure calculations of KTA.
Design of High Speed 8-bit Vedic Multiplier using Brent Kung Adders

One of the primary purposes of a digital signal processing system is multiplication. The multiplier’s performance affects the DSP system’s overall performance. Therefore, it is crucial to create an effective and quick multiplier implementation design. Vedic mathematics can be used to simplify complex computations so that they are easier to perform verbally. Urdhva Triyambakam is the multiplication algorithm used in Vedic math. In this paper, we employing Brent Kung adder to enhance the Vedic multiplier’s performance. The Urdhva Tiryagbhyam sutra is being used in place of other multiplication strategies since it applies to all instances of algorithms for N x N bit numbers and produces the least amount of latency. Four 4-bit vedic multipliers, two 8-bit Brent Kung adders, one 4-bit Brent Kung adder, and an OR gate are used to create an 8-bit vedic multiplier. A 4-bit vedic multiplier is created similarly by combining four 2-bit vedic multipliers, two 4-bit Brent Kung Adders, one 2-bit Brent Kung Adder, and one OR gate. These four-bit vedic multipliers are then combined to form an eight-bit vedic multiplier. After that, Xilinx Vivado Software is used to simulate and synthesis the 8 x 8 Vedic Multiplier, which was coded in Verilog HDL. The proposed Vedic Multiplier is outperformed in terms of speed when compared to related works.
Avoiding Dynamic Obstacles with Real-time Motion Planning using Quadratic Programming for Varied Locomotion Modes

We present a real-time motion planner that avoids multiple moving obstacles without knowing their dynamics or intentions. This method uses convex optimization to generate trajectories for linear plant models over a planning horizon (i.e. model-predictive control). While convex optimizations allow for fast planning, obstacle avoidance can be challenging to incorporate because Euclidean distance calculations tend to break convexity. By using a half-space convex relaxation, our planner reasons about an approximated distance-to-obstacle measure that is linear in its decision variables and preserves convexity. Further, by iteratively updating the relaxation over the planning horizon, the half-space approximation is improved, enabling nimble avoidance maneuvers. We further augment avoidance performance with a soft penalty slack-variable for-mulation that introduces a piecewise quadratic cost. As a proof of concept, we demonstrate the planner on double-integrator models in both single-agent and multi-agent tasks-avoiding multiple obstacles and other agents in 2D and 3D environments. We show extensions to legged locomotion by bipedally walking around obstacles in simulation using the Linear Inverted Pendulum Model (LIPM). We then present two sets of hardware experiments showing real-time obstacle avoid-ance with quadcopter drones: (1) avoiding a 10m/s swinging pendulum and (2) dodging a chasing drone.
Visual Odometry in HDR Environments by Using Spatially Varying Exposure Camera

The accuracy and robustness of visual odometry (VO) is significantly affected by the high dynamic range (HDR) environments, because traditional cameras have a limited dynamic range and inevitably miss information in both overexposed and underexposed areas. To overcome the above challenge, we use an spatially varying exposure (SVE) camera, which captures four images with different exposure levels simultaneously. Then, we propose a VO pipeline that leverages the advantages of the SVE camera. Specifically, we extract ORB features from four images in parallel firstly instead of fusing four images, then perform merging and filtering to provide more robust features. We demonstrate that the proposed system outperforms comparable state-of-the-art methods in terms of robustness and accuracy. The real-time performance of the proposed system is also guaranteed due to the elaborate design of the parallel algorithm.
Precise measurement of the static electric field ionization rate for resolved hydrogen Stark substates

We report the first precise measurements of the ionization rate GAMMA/sub I/(F) in the tunneling regime for resolved n = 30, 40 Stark substates of hydrogen. Excellent agreement is obtained with nonrelativistic, numerical calculations. No effects of wave function mixing induced by relativistic or other interactions were observed. We consider and give an experimental upper limit for the magnitude of the H-D isotopic shift in GAMMA/sub I/(F).
A high-resolution map of the Orion Nebula region at far-infrared wavelengths

It is shown that the Kleinmann-Low nebula continues to radiate like a 70-deg-K blackbody at 69 microns, as it does at shorter wavelengths, but that most of the 69-micron flux from Ori A (M42) comes from an extended emission that roughly follows the free-free radio emission. Nearby sources also detected include the nebula M43 and the molecular cloud OMC-2.
Detection of reappearing targets in forward-looking infrared video sequences

Abstract. Often sensor ego-motion or fast target movement causes the target to temporarily go out of the field-of-view leading to reappearing target detection problem in target tracking applications. Since the target goes out of the current frame and re-enters at a later frame, the re-entering location and variations in rotation, scale, and other three-dimensional orientations of the target are not known, thus complicating the detection and tracking of reappearing targets. A new training-based target detection algorithm has been developed using tuned basis functions (TBFs). The detection algorithm uses target and background information, extracted from training samples, to detect possible candidate target images. The detected candidate target images are then introduced into the second algorithm, called clutter rejection module, to determine the target re-entering frame and location of the target. The second algorithm has been designed using the spatial domain correlation-based template matching (TM) technique. If the target re-enters the current frame, the target coordinates are detected and tracking algorithm is initiated. The performance of the proposed TBF-TM-based reappearing target detection algorithm has been tested using real-world forward-looking infrared video sequences.
The Roll of Artificial Intelligence in Teaching Pedagogy: Challenges and Prospects for Sustainable Growth in Education

All the youngsters, nowadays, are not keen to developing their knowledge. Moreover, the students are easily addicted to mobile phones. So, the performance level decreases and reduces their thinking ability as well technology has grown, all the answers are now available in the internet. If they search since the internet, they get their answers directly but without knowing the base from how it came. Therefore, everyone is trying to reach the upcoming technologies without strong in their basics. Hence, a model is created with the help of toolkit (like courseware, etc.) where the technology behind this is AI. This toolkit (packaged to use with computers) comes up with different subjects (subjects were designed based on the year of study). After a concept is thought to an individual or if a concept is learned individually, this toolkit works as a tester and gives the performance and feedback on the level of their understanding. On analyzing the results, the perspective of each individual is identified. The data is passed to ANN, each level of students is happened and the metrics have shown an efficiency of 94.3%. The result produced for each individual is given to an AI model. According to the perspective of each individual, automatic questions are generated by the AI model to increase their level of knowledge and the way of thinking. It helps every individual to be strong in their concepts and no need for searching the answers for every question. Therefore, it is understood that the proposed model has higher efficiency 95% in training the students and teaching the concepts as well.
Stress And Coping Strategies Of Mothers Of Preterm Infants Admitted In Neonatal Intensive Care Unit

Background: Beginning throughout pregnancy is the beginning of the process of attaining new roles and transitioning into new roles that is known as parenting. The shift will be complete once the moms have reached a level of comfort and confidence in their ability to fulfil the role of mother. It is important that the time immediately after birth be an environment that is favorable to the development of maternal baby bonding so that the relationship between mothers and their infants can be improved. It is distressing for both the mother and the infant if the newborn is separated from the mother either at the time of birth or after the link has been established. Material & Methods: The stress levels and methods of dealing with stress utilized by moms of preterm infants referred to tertiary care centres were evaluated using a descriptive research methodology. There were a total of sixty moms of premature infants that took part in the study. The samples were collected using a technique called purposeful sampling. After receiving their informed consent, the researchers included in the study any parent who was willing to take part and who possessed either English or Marathi language skills. During the second through seventh days of admission, a validated questionnaire was used to assess the levels of stress as well as coping mechanisms. Data on demographic characteristics were gathered. Both the coping style and the level of parental stress were evaluated with the help of a parental stressor scale and a brief COPE questionnaire, respectively. The findings of this particular research project revealed that there were a total of 60 postnatal mothers who did not have a history of psychiatric disorder. The areas of newborn and appearance change, as well as changes in parental role, were found to produce the highest levels of stress for mothers. The results were disrupted by logical reasoning and supported by a condensed version of the COPE model. The examination of the data revealed that praying, developing a relationship with the infant, and coming to terms with the situation were the three most prominent themes. Conclusion: The fact that the mothers of babies who are being treated in the NICU are under severe stress is something that needs to be identified as early as possible in conjunction with the treatment of unwell newborns in order to provide holistic care to the family.
Conference summary: triggered star formation in a turbulent ISM

Abstract While the overall star formation rate in a galaxy appears to depend primarily on the gas mass and density, with the timescale for conversion of gas into stars given by the dynamical time, turbulence and explosions are still important for the process of star formation because they control the birth correlations in space and time. Most star formation appears triggered by some specific process, whether it is a galactic spiral shock, the expansion of a superbubble, the compression of a bright-rimmed globule, or some seemingly random compressive event in a supersonically turbulent flow. These processes give space and time sequences for star birth that are well observed. Many examples were given at this conference. Shocks are the link between large-scale but weak galactic processes and small-scale but strong final collapses. The rate limiting step is on the largest scale, where the dynamical time is slowest. Both gravitational instabilities and pressurized triggering seem to work on the same local dynamical time, making it difficult to tell that star formation is highly triggered when observing only galactic scales.
Passive Modeling of Interconnects Using Sum of Squares Partial Fraction Expansions

Vector fitting has been successfully applied for signal and power integrity design of interconnects and power distribution networks. It is based on a partial fraction expansion of rational functions, resulting in a well-conditioned algorithm applicable to large size problems. A major shortcoming of vector fitting is that the resulting model in many cases will not be passive. In this paper we introduce a sum-of-squares (SOS) partial fraction passivity enforcement algorithm for fitting residues of a passive network. We demonstrate that this new algorithm provides a necessary and sufficient condition for passivity enforcement of scalar networks.
Domain Generalization in Medical Image Segmentation via Meta-Learners

Automatic and semi-automatic radiological image segmentation can help physicians in the processing of real-world medical data for several tasks such as detection/diagnosis of diseases and surgery planning. Current segmentation methods based on neural networks are highly data-driven, often requiring hundreds of laborious annotations to properly converge. The generalization capabilities of traditional supervised deep learning are also limited by the insufficient variability present in the training dataset. One very proliferous research field that aims to alleviate this dependence on large numbers of labeled data is Meta-Learning. Meta-Learning aims to improve the generalization capabilities of traditional supervised learning by training models to learn in a label efficient manner. In this tutorial we present an overview of the literature and proposed ways of merging this body of knowledge with deep segmentation architectures to produce highly adaptable multi-task meta-models for few-shot weakly-supervised semantic segmentation. We introduce a taxonomy to categorize Meta-Learning methods for both classification and segmentation, while also discussing how to adapt potentially any few-shot meta-learner to a weakly-supervised segmentation task.
On stability and convergence of finite element approximations of biot's consolidation problem

Stability and convergence analysis of finite element approximations of Biot's equations governing quasistatic consolidation of saturated porous media are, discussed. A family of decay functions, parametrized by the number of time steps, is derived for the fully discrete backward Euler–Galerkin formulation, showing that the pore-pressure oscillations, arising from an unstable approximation of the incompressibility constraint on the initial condition, decay in time. Error estimates holding over the unbounded time domain for both semidiscrete and fully discrete formulations are presented, and a post-processing technique is employed to improve the pore-pressure accuracy.
Multi-View Robust Tensor-Based Subspace Clustering

In this era of technology advancement, huge amount of data is collected from different disciplines. This data needs to be stored, processed and analyzed to understand its nature. Networks or graphs arise to model real-world systems in the different fields. Early work in network theory adopted simple graphs to model systems where the system’s entities and interactions among them are modeled as nodes and static, single-type edges, respectively. However, this representation is considered limited when the system’s entities interact through different sources. Multi-view networks have recently attracted attention due to its ability to consider the different interactions between entities explicitly. An important tool to understand the structure of multi-view networks is community detection. Community detection or clustering reveals the significant communities in the network which provides dimensionality reduction and a better understanding of the network. In this paper, a new robust clustering algorithm is proposed to detect the community structure in multi-view networks. In particular, the proposed approach constructs a 3-mode tensor from the normalized adjacency matrices that represent the different views. The constructed tensor is decomposed into a self-representation and error components where the extracted self-representation tensor is used to detect the community structure of the multi-view network. Moreover, a common subspace is computed among all views where the contribution of each view to the common subspace is optimized. The proposed method is applied to several real-world data sets and the results show that the proposed method achieves the best performance compared to other state-of-the-art algorithms.
Aminolevulinic acid-based metallic nanoparticles: Applications in Agriculture

Aminolevulinic acid (ALA) is a plant growth regulator and essential precursor for chlorophyll biosynthesis; besides, its photodynamic activity can be used to exterminate larvae and microorganisms in plants and soil. Metallic nanoparticles have unique physicochemical properties and antimicrobial, antiviral, and antifungal activities, and in agriculture, their application as nanopesticides has been proposed. In this study, silver and gold nanoparticles capped/stabilized with aminolevulinic acid (ALAAgNPs and ALAAuNPs) were synthesized by the photoreduction method and characterized by UV-Vis, transmission electron microscopy and Zeta potential. The potential applications of ALANPs as biostimulants and toxicity were evaluated on stalks of E. densa, an aquatic, freshwater perennial plant, by measuring the fluorescence of chlorophyll extracted from the plants after incubation with nanoparticles. The antimicrobial activity of ALANPs against Gram-negative, Gram-positive, and Candida albicans were obtained. The results suggested that ALANPs could simultaneously be suitable for applications as nanopesticides and nanostimulants.
Comparison of microscopy and metabarcoding to identify pollen used by the critically endangered rusty patched bumble bee, Bombus affinis

Taxonomic analysis of pollen collected by bees can provide insights into their host plant use, providing information about the plant species selected for targeted conservation strategies. The two main identification approaches used are morphological analysis of pollen samples affixed to microscope slides (i.e., microscopic palynology) and molecular analysis of samples. Both methods are widely used for freshly collected materials and have been compared in multiple studies, yet their application to archived samples remains to be explored. Archived samples may be particularly useful for the study of rare or protected species, particularly when historical foraging patterns are uncertain. We used both methods to analyse pollen collected by the endangered rusty patched bumble bee species, Bombus affinis Cresson, applied to museum‐archived specimens. Pollen samples were removed from the corbiculae of bumble bees originally collected in Michigan between 1914 and 1974. Samples included 24 rusty patched bumble bees each with large pollen loads on both corbiculae, allowing for pollen from the same bee to be analysed using both methods. DNA metabarcoding detected more plant taxa than light microscopy, and DNA barcoding also had higher taxonomic resolution when compared to taxa determined using light microscopy. In many instances, pollen could only be confidently identified to tribe or family with light microscopy. Discrepancy between methods decreased when taxa identified via DNA metabarcoding were binned into ecologically relevant groups corresponding to those identified using light microscopy. Although binning demonstrated smaller within‐method variance, there was still minimal correspondence between the two methods. Results indicate there are benefits and biases unique to each method and highlight the utility of binning taxonomic results to morphological or ecological groupings.
An investigation of RPL Trickle Timers for Multi-parent selections in Smart Grid

Smart grid technology is being utilized to control electric power, while AMI is an application that allows service providers and customers to interact. The devices will be constrained with power and memory. The problem was overcome by implementing an IETF-standard RPL protocol that includes routing graphing features. There are several strategies that may be used to improve the efficiency of routing algorithms, and their combinations are studied in this paper. The first is a trickle timer method, which regulates the spread of control messages to commence routing diagrams. The second is a multi-parent selection, which works by separating the data into smaller pieces and delivering them to the parent mentioned. Both strategies are for extending the node's life. However, because the trickle timer technique was not designed for multi-parent selection, we modified it to be suitable. As a result, the parent's network lifetime (NLT) is increased when working longer hours resulted in increased packet delivery rates.