Miracle-dz/UniFAR-FTUs huggingface dataset:
Deterministic quantum state transfer between remote qubits in cavities

Performing a faithful transfer of an unknown quantum state is a key challenge for enabling quantum networks. The realization of networks with a small number of quantum links is now actively pursued, which calls for an assessment of different state transfer methods to guide future design decisions. Here, we theoretically investigate quantum state transfer between two distant qubits, each in a cavity, connected by a waveguide, e.g., an optical fiber. We evaluate the achievable success probabilities of state transfer for two different protocols: standard wave packet shaping and adiabatic passage. The main loss sources are transmission losses in the waveguide and absorption losses in the cavities. While special cases studied in the literature indicate that adiabatic passages may be beneficial in this context, it remained an open question under which conditions this is the case and whether their use will be advantageous in practice. We answer these questions by providing a full analysis, showing that state transfer by adiabatic passage—in contrast to wave packet shaping—can mitigate the effects of undesired cavity losses, far beyond the regime of coupling to a single waveguide mode and the regime of lossless waveguides, as was proposed so far. Furthermore, we show that the photon arrival probability is in fact bounded in a trade-off between losses due to non-adiabaticity and due to coupling to off-resonant waveguide modes. We clarify that neither protocol can avoid transmission losses and discuss how the cavity parameters should be chosen to achieve an optimal state transfer.
Co-attending Free-form Regions and Detections with Multi-modal Multiplicative Feature Embedding for Visual Question Answering

Recently, the Visual Question Answering (VQA) task has gained increasing attention in artificial intelligence. Existing VQA methods mainly adopt the visual attention mechanism to associate the input question with corresponding image regions for effective question answering. The free-form region based and the detection-based visual attention mechanisms are mostly investigated, with the former ones attending free-form image regions and the latter ones attending pre-specified detection-box regions. We argue that the two attention mechanisms are able to provide complementary information and should be effectively integrated to better solve the VQA problem. In this paper, we propose a novel deep neural network for VQA that integrates both attention mechanisms. Our proposed framework effectively fuses features from free-form image regions, detection boxes, and question representations via a multi-modal multiplicative feature embedding scheme to jointly attend question-related free-form image regions and detection boxes for more accurate question answering. The proposed method is extensively evaluated on two publicly available datasets, COCO-QA and VQA, and outperforms state-of-the-art approaches. Source code is available at https://github.com/lupantech/dual-mfa-vqa.
The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons. ” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes that occur during development that may be observable even in actual neural systems where these changes are convoluted with changes in synaptic connectivity and intrinsic neural plasticity.
Approximation Algorithms for Dominating Set in Disk Graphs

AbstractWe consider the problem of ﬁnding a lowest cost dominating set in a given disk graph containingn disks. The problem has been extensively studied on subclasses of disk graphs, yet the best knownapproximation for disk graphs has remained O(logn) – a bound that is asymptotically no better thanthe general case. We improve the status quo in two ways: for the unweighted case, we show how toobtain a PTAS using the framework recently proposed (independently) by Mustafa and Ray [16] and byChan and Har-Peled [4]; for the weighted case where each input disk has an associated rational weightwith the objective of ﬁnding a minimum cost dominating set, we give a randomized algorithm thatobtains a dominating set whose weight is within a factor 2 O(log ∗ n) of a minimum cost solution, with highprobability – the technique follows the framework proposed recently by Varadarajan [19]. 1 Introduction For a set D of n disks in the Euclidean plane, deﬁne an intersection graph, G = (V,E), thus: V = D;{u,v} ∈ E ⇔ disk(u)∩disk(v) 6= ∅. G is called a
Epitaxy and Microstructure Evolution in Metal Additive Manufacturing

Metal additive manufacturing (AM) works on the principle of incremental layer-by-layer material consolidation, facilitating the fabrication of objects of arbitrary complexity through the controlled melting and resolidification of feedstock materials by using high-power energy sources. The focus of metal AM is to produce complex-shaped components made of metals and alloys to meet demands from various industrial sectors such as defense, aerospace, automotive, and biomedicine. Metal AM involves a complex interplay between multiple modes of energy and mass transfer, fluid flow, phase change, and microstructural evolution. Understanding the fundamental physics of these phenomena is a key requirement for metal AM process development and optimization. The effects of material characteristics and processing conditions on the resulting epitaxy and microstructure are of critical interest in metal AM. This article reviews various metal AM processes in the context of fabricating metal and alloy parts through epitaxial.
A Neural-Network-Based Controller for Piezoelectric-Actuated Stick–Slip Devices

Piezoelectric-actuated stick–slip device (PASSD) is a highly promising equipment that is composed of one end-effector, one piezoelectric actuator (PEA) and one driving object adhered to the PEA. Since the end-effector can slip on the surface of the driving object, the PASSD is capable of realizing the macrolevel motion with the microlevel precision. Due to the following two reasons: the complicated relative motion between the end-effector and the driving object, and the inherent hysteresis nonlinearity in the PEA, the ultraprecision displacement control of the end-effector of PASSDs raises a real challenge, which is rarely reported in the literature. Toward solving this challenge, a neural-network-based controller is proposed in this paper. First, a neural-network-based model is proposed to capture the relative motion between the end-effector and the driving object. Second, a neural-network-based inversion model is developed to online calculate the desired position of the PEA under the predesigned reference of the end-effector. Third, a dynamic linearized neural-network-based model predictive control method, which can effectively handle the hysteresis nonlinearity, is employed to implement the displacement control of the PEA, which finally results in an overall high-precision controller of the end-effector. Finally, a PASSD prototype has been implemented and tested through experimental studies to demonstrate the effectiveness of the proposed approach.
Intrusion Detection on System Call Graphs

Cyber attacks such as ransomware can do great damage. Intrusion detection systems can help to detect those attacks. Especially with anomaly detection methods, it is possible to detect previous unknown attacks. In this paper, we present a graph-based approach in combination with existing methods trying to increase recognition rates and reduce false alarm rates. Our Hypotheses: By taking the inherent structure of the underlying data into account, it is possible to gain more insights compared to other known methods. The modern ADFA-LD dataset was used for the evaluation, which reflects the operation in a modern operating system. Compared to the Stide approach we demonstrate that a graph-based approach can keep pace.
Toward a Robust Security Paradigm for Bluetooth Low Energy-Based Smart Objects in the Internet-of-Things

Bluetooth Low Energy (BLE) has emerged as one of the most promising technologies to enable the Internet-of-Things (IoT) paradigm. In BLE-based IoT applications, e.g., wearables-oriented service applications, the Bluetooth MAC addresses of devices will be swapped for device pairings. The random address technique is adopted to prevent malicious users from tracking the victim’s devices with stationary Bluetooth MAC addresses and accordingly the device privacy can be preserved. However, there exists a tradeoff between privacy and security in the random address technique. That is, when device pairing is launched and one device cannot actually identify another one with addresses, it provides an opportunity for malicious users to break the system security via impersonation attacks. Hence, using random addresses may lead to higher security risks. In this study, we point out the potential risk of using random address technique and then present critical security requirements for BLE-based IoT applications. To fulfill the claimed requirements, we present a privacy-aware mechanism, which is based on elliptic curve cryptography, for secure communication and access-control among BLE-based IoT objects. Moreover, to ensure the security of smartphone application associated with BLE-based IoT objects, we construct a Smart Contract-based Investigation Report Management framework (SCIRM) which enables smartphone application users to obtain security inspection reports of BLE-based applications of interest with smart contracts.
HoloFace: Augmenting Human-to-Human Interactions on HoloLens

We present HoloFace, an open-source framework for face alignment, head pose estimation and facial attribute retrieval for Microsoft HoloLens. HoloFace implements two state-of-the-art face alignment methods which can be used interchangeably: one running locally and one running on a remote backend. Head pose estimation is accomplished by fitting a deformable 3D model to the landmarks localized using face alignment. The head pose provides both the rotation of the head and a position in the world space. The parameters of the fitted 3D face model provide estimates of facial attributes such as mouth opening or smile. Together the above information can be used to augment the faces of people seen by the HoloLens user, and thus their interaction. Potential usage scenarios include facial recognition, emotion recognition, eye gaze tracking and many others. We demonstrate the capabilities of our framework by augmenting the faces of people seen through the HoloLens with various objects and animations.
Infantile amnesia: a neurogenic hypothesis.

In the late 19th Century, Sigmund Freud described the phenomenon in which people are unable to recall events from early childhood as infantile amnesia. Although universally observed, infantile amnesia is a paradox; adults have surprisingly few memories of early childhood despite the seemingly exuberant learning capacity of young children. How can these findings be reconciled? The mechanisms underlying this form of amnesia are the subject of much debate. Psychological/cognitive theories assert that the ability to maintain detailed, declarative-like memories in the long term correlates with the development of language, theory of mind, and/or sense of "self. " However, the finding that experimental animals also show infantile amnesia suggests that this phenomenon cannot be explained fully in purely human terms. Biological explanations of infantile amnesia suggest that protracted postnatal development of key brain regions important for memory interferes with stable long-term memory storage, yet they do not clearly specify which particular aspects of brain maturation are causally related to infantile amnesia. Here, we propose a hypothesis of infantile amnesia that focuses on one specific aspect of postnatal brain development--the continued addition of new neurons to the hippocampus. Infants (humans, nonhuman primates, and rodents) exhibit high levels of hippocampal neurogenesis and an inability to form lasting memories. Interestingly, the decline of postnatal neurogenesis levels corresponds to the emergence of the ability to form stable long-term memory. We propose that high neurogenesis levels negatively regulate the ability to form enduring memories, most likely by replacing synaptic connections in preexisting hippocampal memory circuits.
Parametric Analysis of Rolling–Sliding Line Contacts Under Boundary and Near Boundary Lubrication Conditions

Abstract This article reports a parametric analysis of rolling–sliding line contacts in boundary and near-boundary lubrication with relevance to the contacts in rotorcraft drive systems in loss of lubrication. A recently developed mathematical model for boundary lubrication with friction, temperature, and tribochemistry is used in the analysis. The parameters studied include radius of the line contact, surface hardness, boundary film shear strength, fluid–solid load sharing, system bulk temperature, load, speed, and slide-to-roll ratio. The contact condition is measured by the temperature and friction power intensity along with the boundary film integrity and mode of deformation. The results of the analysis led to a number of suggestions and elaborations listed in the Conclusion regarding various design considerations of the contacts in rotorcraft drive systems against loss of lubrication.
MAGNA: Maximizing Accuracy in Global Network Alignment

MOTIVATION Biological network alignment aims to identify similar regions between networks of different species. Existing methods compute node similarities to rapidly identify from possible alignments the high-scoring alignments with respect to the overall node similarity. But, the accuracy of the alignments is then evaluated with some other measure that is different than the node similarity used to construct the alignments. Typically, one measures the amount of conserved edges. Thus, the existing methods align similar nodes between networks hoping to conserve many edges (after the alignment is constructed! ). RESULTS Instead, we introduce MAGNA to directly 'optimize' edge conservation while the alignment is constructed, without decreasing the quality of node mapping. MAGNA uses a genetic algorithm and our novel function for 'crossover' of two 'parent' alignments into a superior 'child' alignment to simulate a 'population' of alignments that 'evolves' over time; the 'fittest' alignments survive and proceed to the next 'generation', until the alignment accuracy cannot be optimized further. While we optimize our new and superior measure of the amount of conserved edges, MAGNA can optimize any alignment accuracy measure, including a combined measure of both node and edge conservation. In systematic evaluations against state-of-the-art methods (IsoRank, MI-GRAAL and GHOST), on both synthetic networks and real-world biological data, MAGNA outperforms all of the existing methods, in terms of both node and edge conservation as well as both topological and biological alignment accuracy. AVAILABILITY Software: http://nd.edu/∼cone/MAGNA CONTACT: : tmilenko@nd.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Room temperature multiplexed gas sensing using chemical-sensitive 3.5-nm-thin silicon transistors

Multigas sensors on a chip with chemical-sensitive nanoscale silicon transistors. There is great interest in developing a low-power gas sensing technology that can sensitively and selectively quantify the chemical composition of a target atmosphere. Nanomaterials have emerged as extremely promising candidates for this technology due to their inherent low-dimensional nature and high surface-to-volume ratio. Among these, nanoscale silicon is of great interest because pristine silicon is largely inert on its own in the context of gas sensing, unless functionalized with an appropriate gas-sensitive material. We report a chemical-sensitive field-effect transistor (CS-FET) platform based on 3.5-nm-thin silicon channel transistors. Using industry-compatible processing techniques, the conventional electrically active gate stack is replaced by an ultrathin chemical-sensitive layer that is electrically nonconducting and coupled to the 3.5-nm-thin silicon channel. We demonstrate a low-power, sensitive, and selective multiplexed gas sensing technology using this platform by detecting H2S, H2, and NO2 at room temperature for environment, health, and safety in the oil and gas industry, offering significant advantages over existing technology. Moreover, the system described here can be readily integrated with mobile electronics for distributed sensor networks in environmental pollution mapping and personal air-quality monitors.
Four-probe thermal measurement of a carbon nanotube sheet

As advances are made in top-down nanofabrication and bottom-up syntheses of nanostructures, the characteristic length scales encountered in these structures are on the order of the mean free path of the heat carriers or smaller. Therefore, the thermal transport properties of these nanostructures can be different from the bulk counterparts. A number of experimental techniques have been developed for characterizing the size-dependent thermal transport properties of nanostructures. However, it is difficult to eliminate contact thermal resistance, an important error source, from the measurement results. Recently, a four-probe thermal measurement technique has been developed to measure the intrinsic thermal conductance of a suspended sample as well as isolate the values of contact resistance between the sample and measurement device. Here, the fabrication process of the four-probe measurement device is described. In addition, numerical heat conduction simulation is used to verify the analytical model of the measurement method. This method is further used to measure the thermal conductance of a carbon nanotube sheet. conductance of one line is and the average temperature rises of all four RTs obtained from the measured electrical resistances. This is repeated until each of the four lines has been used as the heater line. The measurements obtain sixteen sets of measured average temperature rise data normalized by the electrical heating rate in the heating line i , ( IV ) i , i = 1, 2, 3, 4. These data are used to obtain the thermal resistance ( R b,j , j = 1, 2, 3, 4) of each RT, the temperature rise (  c,j,i ) of the j th RT line at the contact point between the RT and the sample, and the heat flow rate ( Q j,i ) across each contact point. The obtained results are used to find the intrinsic thermal resistance ( R 2 ) of the sample as well as the values of the contact thermal resistance between the sample and the middle two RTs, R c,2 and R c,3 . This measurement method is powerful since it has the ability to measure the intrinsic thermal resistance of a sample without errors from contact thermal resistance.
Radiation-free quantification of head malformations in craniosynostosis patients from 3D photography

The evaluation of cranial malformations plays an essential role both in the early diagnosis and in the decision to perform surgical treatment for craniosynostosis. In clinical practice, both cranial shape and suture fusion are evaluated using CT images, which involve the use of harmful radiation on children. Three-dimensional (3D) photography offers noninvasive, radiation-free, and anesthetic-free evaluation of craniofacial morphology. The aim of this study is to develop an automated framework to objectively quantify cranial malformations in patients with craniosynostosis from 3D photography. We propose a new method that automatically extracts the cranial shape by identifying a set of landmarks from a 3D photograph. Specifically, it registers the 3D photograph of a patient to a reference template in which the position of the landmarks is known. Then, the method finds the closest cranial shape to that of the patient from a normative statistical shape multi-atlas built from 3D photographs of healthy cases, and uses it to quantify objectively cranial malformations. We calculated the cranial malformations on 17 craniosynostosis patients and we compared them with the malformations of the normative population used to build the multi-atlas. The average malformations of the craniosynostosis cases were 2.68 ± 0.75 mm, which is significantly higher (p<0.001) than the average malformations of 1.70 ± 0.41 mm obtained from the normative cases. Our approach can support the quantitative assessment of surgical procedures for cranial vault reconstruction without exposing pediatric patients to harmful radiation.
Towards Reliable (and Efficient) Job Executions in a Practical Geo-distributed Data Analytics System

Geo-distributed data analytics are increasingly common to derive useful information in large organisations. Naive extension of existing cluster-scale data analytics systems to the scale of geo-distributed data centers faces unique challenges including WAN bandwidth limits, regulatory constraints, changeable/unreliable runtime environment, and monetary costs. Our goal in this work is to develop a practical geo-distribued data analytics system that (1) employs an intelligent mechanism for jobs to efficiently utilize (adjust to) the resources (changeable environment) across data centers; (2) guarantees the reliability of jobs due to the possible failures; and (3) is generic and flexible enough to run a wide range of data analytics jobs without requiring any changes. To this end, we present a new, general geo-distributed data analytics system, HOUTU, that is composed of multiple autonomous systems, each operating in a sovereign data center. HOUTU maintains a job manager (JM) for a geo-distributed job in each data center, so that these replicated JMs could individually and cooperatively manage resources and assign tasks. Our experiments on the prototype of HOUTU running across four Alibaba Cloud regions show that HOUTU provides nearly efficient job performance as in the existing centralized architecture, and guarantees reliable job executions when facing failures.