Miracle-dz/UniFAR-FTUs huggingface dataset:
PARTIAL DIFFERENTIAL INEQUALITIES

ContentsIntroductionChapter 1. Variational elliptic inequalities ? 1.1. First example ? 1.2. Variational inequalities ? 1.3. Variational inequalities and many-valued operators ? 1.4. Examples ? 1.5. The method of penalization ? 1.6. The method of regularization ? 1.7. Interpretation of variational inequalities and multipliers ? 1.8. Regularity ? 1.9. Comparison and convexity properties of solutions Chapter 2. Parabolic evolution inequalities (I) ? 2.1. First examples ? 2.2. The variational formulation. The strong solution ? 2.3. The variational formulation. The weak solution ? 2.4. Examples ? 2.5. The method of elliptic regularization ? 2.6. The method of penalization ? 2.7. Some generalizations ? 2.8. Quasi-stationary problems ? 2.9. Regularity ? 2.10. Remarks Chapter 3. Parabolic evolution inequalities (II). The flow of a non-Newtonian fluid ? 3.1. Statement of the problem ? 3.2. The main results ? 3.3. The principle of the proof Chapter 4. Inequalities of the second order with respect to? t ? 4.1. First example ? 4.2. The general variational problem ? 4.3. Examples ? 4.4. Methods for existence proofs Chapter 5. Variational inequalities for Maxwell operators ? 5.1. Statement of the problem ? 5.2. Results Chapter 6. Other problems ? 6.1. Coupled phenomena ? 6.2. A problem with a decay ? 6.3. Behaviour at infinity. Almost periodic solution Chapter 7. General remarks on the numerical approximation of solutions of inequalities ? 7.1. Discrete schemes for evolution inequalities ? 7.2. Analysis of stability ? 7.3. The solution of stationary problems Chapter 8. Singular layer phenomena ? 8.1. Generalities ? 8.2. An example Chapter 9. Open problems References
IDIoT: Multimodal Framework for Ubiquitous Identification and Assignment of Human-carried Wearable Devices

IoT (Internet of Things) devices, such as network-enabled wearables, are carried by increasingly more people throughout daily life. Information from multiple devices can be aggregated to gain insights into a person’s behavior or status. For example, an elderly care facility could monitor patients for falls by combining fitness bracelet data with video of the entire class. For this aggregated data to be useful to each person, we need a multi-modality association of the devices’ physical ID (i.e., location, the user holding it, visual appearance) with a virtual ID (e.g., IP address/available services). Existing approaches for multi-modality association often require intentional interaction or direct line-of-sight to the device, which is infeasible for a large number of users or when the device is obscured by clothing. We present IDIoT, a calibration-free passive sensing approach that fuses motion sensor information with camera footage of an area to estimate the body location of motion sensors carried by a user. We characterize results across three baselines to highlight how different fusing methodology results better than earlier IMU-vision fusion algorithms. From this characterization, we determine IDIoT is more robust to errors such as missing frames or miscalibration that frequently occur in IMU-vision matching systems.
Recursive Subtyping for All

Recursive types and bounded quantification are prominent features in many modern programming languages, such as Java, C#, Scala or TypeScript. Unfortunately, the interaction between recursive types, bounded quantification and subtyping has shown to be problematic in the past. Consequently, defining a simple foundational calculus that combines those features and has desirable properties, such as decidability, transitivity of subtyping, conservativity and a sound and complete algorithmic formulation has been a long time challenge. This paper presents an extension of kernel F≤, called F≤µ, with iso-recursive types. F≤ is a well-known polymorphic calculus with bounded quantification. In F≤µ we add iso-recursive types, and correspondingly extend the subtyping relation with iso-recursive subtyping using the recently proposed nominal unfolding rules. We also add two smaller extensions to F≤. The first one is a generalization of the kernel F≤ rule for bounded quantification that accepts equivalent rather than equal bounds. The second extension is the use of so-called structural folding/unfolding rules, inspired by the structural unfolding rule proposed by Abadi, Cardelli, and Viswanathan [1996]. The structural rules add expressive power to the more conventional folding/unfolding rules in the literature, and they enable additional applications. We present several results, including: type soundness; transitivity and decidability of subtyping; the conservativity of F≤µ over F≤; and a sound and complete algorithmic formulation of F≤µ. Moreover, we study an extension of F≤µ, called F≤≥µ, which includes lower bounded quantification in addition to the conventional (upper) bounded quantification of F≤. All the results in this paper have been formalized in the Coq theorem prover.
BLEselect

Spontaneous selection of IoT devices from the head-mounted device is key for user-centered pervasive interaction. BLEselect enables users to select an unmodified Bluetooth 5.1 compatible IoT device by nodding at, pointing at, or drawing a circle in the air around it. We designed a compact antenna array that fits on a pair of smart glasses to estimate the Angle of Arrival (AoA) of IoT and wrist-worn devices' advertising signals. We then developed a sensing pipeline that supports all three selection gestures with lightweight machine learning models, which are trained in real-time for both hand gestures. Extensive characterizations and evaluations show that our system is accurate, natural, low-power, and privacy-preserving. Despite the small effective size of the antenna array, our system achieves a higher than 90% selection accuracy within a 3 meters distance in front of the user. In a user study that mimics real-life usage cases, the overall selection accuracy is 96.7% for a diverse set of 22 participants in terms of age, technology savviness, and body structures.
Force-Regulated Spontaneous Conformational Changes of Integrins α5β1 and αVβ3

Force can modulate the properties and functions of macromolecules by inducing conformational changes, such as coiling/uncoiling, zipping/unzipping, and folding/unfolding. Here we compared force-modulated bending/unbending of two purified integrin ectodomains, α5β1 and αVβ3, using single-molecule approaches. Similar to previously characterized mechano-sensitive macromolecules, the conformation of α5β1 is determined by a threshold head-to-tail tension, suggesting a canonical energy landscape with a deep energy well that traps the integrin in the bent state until sufficient force tilts the energy landscape to accelerate transition to the extended state. By comparison, αVβ3 exhibits bi-stability even without force and can spontaneously transition between the bent and extended conformations in a wide range of forces without energy supplies. Molecular dynamics simulations revealed consecutive formation and disruption of 7 hydrogen bonds during αVβ3 bending and unbending, respectively. Accordingly, we constructed an energy landscape with hexa-stable intermediate states to break down the energy barrier separating the bent and extended states into smaller ones, making it possible for the thermal agitation energy to overcome them sequentially and to be accumulated and converted into mechanical work required for αVβ3 to bend against force. Our study elucidates the different inner workings of α5β1 and αVβ3 at the sub-molecular level, sheds lights on how their respectively functions are facilitated by their distinctive mechano-sensitivities, helps understand their signal initiation processes, and provides critical concepts and useful design principles for engineering of protein-based biomechanical nanomachines.
A Deep Generative Model for Trajectory Modeling and Utilization

Modern location-based systems have stimulated explosive growth of urban trajectory data and promoted many real-world applications, e.g. , trajectory prediction. However, heavy big data processing overhead and privacy concerns hinder trajectory acquisition and utilization. Inspired by regular trajectory distribution on transportation road networks, we propose to model trajectory data privately with a deep generative model and leverage the model to generate representative trajectories for downstream tasks or directly support these tasks ( e.g. , popularity ranking), rather than acquiring and processing the original big trajectory data. Nevertheless, it is rather challenging to model high-dimensional trajectories with time-varying yet skewed distribution. To address this problem, we model and generate trajectory sequence with judiciously encoded spatio-temporal features over skewed distribution by leveraging an important factor neglected by the literature - the underlying road properties ( e.g. , road types and directions), which are closely related to trajectory distribution. Specifically, we decompose trajectory into map-matched road sequence with temporal information and embed them to encode spatio-temporal features. Then, we enhance trajectory representation by encoding inherent route planning patterns from the underlying road properties. Later, we encode spatial correlations among edges and daily and weekly temporal periodicity information. Next, we employ a meta-learning module to generate trajectory sequence step by step by learning generalized trajectory distribution patterns from skewed trajectory data based on the well-encoded trajectory prefix. Last but not least, we preserve trajectory privacy by learning the model differential privately with clipping gradients. Experiments on real-world datasets show that our method significantly outperforms existing methods.
SkinnerMT: Parallelizing for Efficiency and Robustness in Adaptive Query Processing on Multicore Platforms

SkinnerMT is an adaptive query processing engine, specialized for multi-core platforms. SkinnerMT features different strategies for parallel processing that allow users to trade between average run time and performance robustness. First, SkinnerMT supports execution strategies that execute multiple query plans in parallel, thereby reducing the risk to find near-optimal plans late and improving robustness. Second, SkinnerMT supports data-parallel processing strategies. Its parallel multi-way join algorithm is sensitive to the assignment from tuples to threads. Here, SkinnerMT uses a cost-based optimization strategy, based on runtime feedback. Finally, SkinnerMT supports hybrid processing methods, mixing parallel search with data-parallel processing. The experiments show that parallel search increases robustness while parallel processing increases average-case performance. The hybrid approach combines advantages from both. Compared to traditional database systems, SkinnerMT is preferable for benchmarks where query optimization is hard. Compared to prior adaptive processing baselines, SkinnerMT exploits parallelism better.
Cocoon stars in the Large Magellanic Cloud

Near-infrared (JKH) photometry of 13 of the 29 IRAS sources toward the northern LMC that were suggested as possible dust-embedded asymptotic giant branch stars by Reid, Tinney, and Mould (1984) is presented. Two prove to be more luminous LMC red supergiants, while one appears to be a substantially more distant extragalactic object. The remaining 10 are identified as LMC 'cocoon' stars, with most having flux distributions similar to highly reddened Galactic OH/IR sources and bolometric luminosities in the range of -5 to -6. The present estimates of the mass-loss rates imply lifetimes of less than 100,000 yr.
Quantum-Confinement Effects on Binding Energies and Optical Properties of Excitons in Quantum Dots

Quantum-confinement effects on the binding energy and the linear optical susceptibility of excitons in quantum dots are studied. It is found that the binding energy and the linear optical susceptibility are sensitive to the barrier height and the dot size. For an infinite barrier, the binding energy of excitons decreases monotonically with the increasing dot radius, and the absorption intensity has almost the same amplitude with the increasing photon energy. For a finite barrier, the binding energy has a maximum value with the increasing dot radius, and the absorption intensity damps rapidly with the increasing photon energy. The effective mass ratio is also found to have an influence on the binding energy. The results could be confirmed by future experiments on excitons in quantum dots.
Biodiversity Assessment in a forest in an Oil Palm Plantation at lahad Datu

Malaysia has become one of the most important countries that produce and supply the world’s palm oil. The oil palm industry has emphasised the need for the preservation of small forest patches within oil palm plantations to sustain various species of fauna; this is a new conservation strategy for developing biodiversity-friendly oil palm plantations. The effort in documenting the species of flora and fauna in such forest patches in oil palm plantations is an action needed to display species richness, diversity and distribution within the area. Assessment of fauna species was conducted through various sampling methods in a forest patch estimated to be less than 0.30% of the total oil palm planted area of 1986 hectares at MPOB Research Station, Lahad Datu, Sabah. From the 132 species recorded, a total of 22 species of mammals (16.67%), 45 species of birds (34.09%), 11 species of amphibians (8.33%), six species of reptiles (4.54%), four species of fishes (3.03%), and 44 morpho-species of arthropods (33.33%) were identified. The fauna species identified in this forest patch provides a baseline for biodiversity assessment in other oil palm plantations. Therefore, effective sustainable oil palm management can be proven to be implemented.
A 3.4-4.1GHz Broadband GaN Doherty Power Amplifier Module for 5G massive-MIMO Base-Stations

This paper presents a 3.4-4.1 GHz broadband two-stage GaN Doherty Power Amplifier (DPA) Module for 5G massive MIMO Base-Stations. In order to achieve high efficiency, broadband and compact DPA module in a multi-chip configuration, new design method to realize an impedance inverting network in DPA by Tee-type transmission line network with parasitic capacitance absorption even in the presence of series components between auxiliary power amplifier and combining node is proposed. The proposed method is applied to a compact two-stage GaN DPA module with effective area of 76mm2. Measurements showed that the DPA module achieved PAE of 42.0-44.6%, gain of 28.6-31.0dB and Pout of 40.3-40.8dBm over 700MHz for 3.4-4.1GHz with ACLR less than -47dBc under 20MHz modulation signal with PAPR of 7.5dB, after DPD. The proposed DPA module has the most wideband characteristic ever reported among DPAs in 3-4GHz band, demonstrating that the proposed design method is effective.
IRS-aided Communications Without Channel State Information Relying on Deep Reinforcement Learning

An intelligent reflecting surface (IRS), which comprises numerous passive elements, is considered a promising technology for smart wireless communication. However, the passive characteristics of an IRS render the explicit estimation of the channel state information to appropriately adjust its reflection coefficient challenging. This study proposes a deep reinforcement learning-based algorithm that learns the precoding vector of the base station (BS) and the IRS phase shift from the wireless environment to address this problem. We develop a beam-pattern-based learning framework for this algorithm that indirectly maps the wireless environment to the phase shift to manage the large state-action space caused by the multiple elements of the BS and IRS. Based on the simulation results, the proposed algorithm can learn from the environment and establish a transmission strategy that improves the user's transmission rate. Furthermore, the results validate that the proposed algorithm based on the beam pattern learning framework is more efficient and scalable to the number of IRS elements compared to the method that directly builds a mapping to the phase shift.
Reduced Order Modeling of a Heat Exchanger with a Stacking Ensemble to reduce Computational Inefficiencies

Reduced Order Modeling is a technique for reducing the computational complexity of a model while preserving the expected fidelity within a controlled error. One of the techniques used to create a Reduced Order Model (ROM) is Artificial Neural Networks (ANN). A successful approach to reducing the variance of ANN model prediction is to train multiple models instead of a single model and to combine the predictions from these models, which is commonly called Ensemble learning. When the predictions from the multiple models are combined using another regression model, it is called Stacking ensemble. This paper studies the effectiveness of using Genetic programming algorithm in taking the outputs of each model as input and attempting to learn how to best combine the input predictions to make a better output prediction.The above-mentioned approach is used to create a ROM for a crossflow heat exchanger steady-state component. There are 6 inputs parameters namely Cold & Hot inlet temperature, Cold & Hot outlet pressure and Cold & Hot inlet flow. There are four outputs namely Hot & Cold outlet temperature and Hot & Cold inlet pressure. A multi-input single output (MISO) ROM is created for each of the outputs. There are 3 different configurations of ANNs used to cover a good range of the Hyperparameter values. The output from each of the ANNs is then combined using Genetic Programming Algorithm. The Overall model has an R2 value of above 95% for each of the outputs. The ROM thus created can run simulations at a much faster rate. The ROM of the HX component is a black box and can be shared with third party without any concerns over propriety information loss.
Adaptive tracking control of unknown state target for CPSs subjected to cyberattacks

In this study, an adaptive controller for Cyber-Physical Systems(CPSs) subjected to cyberattacks is proposed to track a class of target system with unknown inputs and states. Firstly, an estimation strategy is proposed to adaptively estimate the unknown inputs and states of target on the basis of finite impulse response (FIR) filter. Secondly, due to the existence of cyberattacks in CPSs, an adaptive tracking control scheme with designed compensation signal is constructed to stabilize the system performance which might be damaged by attacks, where Lyapunov function related to adaptive estimated factor is adopted to prove that the CPSs are able to track the target under attacks. In the last part, a numerical experiment is implemented to demonstrate the validity of the theoretical results.
Port Scanning Mitigation Strategies for Penetration Testing: Blue Team Perspective

Port scanning technique is one of the most common kinds of system enumeration for hackers for active scanning. Active scanning is the initial attack of hackers to survey the system and then gather information based on open ports etc. After the information is ready, the hackers prepare an attack based on the available information. This often leads to loss of data or service for an enterprise or an individual. It might even be monetary loss. Penetration testing is like quality analysis where the pentesters carry out relative tasks to secure the system. So that attacks like port scanning are rendered useless for the hackers against the system. Taking defensive measurements against attacks are referred to as blue team activities. This paper covers the attack of port scanning and the defensive measures that can be taken against the port scanning attack to secure an enterprise or individual system.
Quick Comparison of the Cogging Torque Severity in Permanent Magnet Synchronous Machines with Segmented Stator Core

This paper presents the determination of a numerical indicator that allows the quick comparison of the cogging torque in permanent magnet synchronous machines (PMSMs) with both segmented stator and conventional stator structures, considering different pole/slot combinations. By means of two-dimensional finite element simulations of 18 different machine designs, it was proven that comparison indicators used for conventional PMSMs cannot be implemented for PMSMs with segmented stator core. Instead, the proposed indicator considers the change of periodicity in PMSMs with segmented stator core and enables two relevant phases of the design stage of the PMSMs: the effective comparison of the magnitude of the torque cogging for different pole/slot combinations on segmented machines; and the comparison between PMSMs with conventional stator and segmented stator.
Peak Demand Optimization of Commercial Buildings Based on Energy Storage Systems

Large and stochastic peak energy demands of buildings contribute to grid inefficiency. Here, we present an optimization algorithm for behind-the-meter peak shaving with energy storage systems. We employ a Markov decision process to execute charging and discharging decisions on sub-hourly time scales according to the on-site energy profile and the size of the energy storage system. The algorithm minimizes the peak load drawn from the grid to maximize savings for the asset owner and efficiency gains for the grid network. Our technology-agnostic solution can serve battery energy storage systems and lesser-studied use cases including pumped hydro storage and municipal water systems. We demonstrate how the algorithm can inform on sizing energy storage systems to best serve a given building. With these contributions, we provide an AI platform to optimize energy storage assets and smart grid systems.
Guidelines for systematic functional decomposition in model-based systems engineering

The increasing complexity in today’s products require an ever more systematic product development process in which the use of model-based systems engineering (MBSE) plays an increasingly decisive role, especially against the background of cross-disciplinary collaboration. Having evaluated the actual state of the art, many companies still have true difficulties when modeling the functional architecture with the help of actual approaches. Thus, a new procedure with concrete guiding rules and control criteria to strategically define and decompose functions for the two perspectives a greenfield and brownfield environment is theoretically introduced and partly applied to a practical example.
Honeypot-Based Cyber Deception Against Malicious Reconnaissance via Hypergame Theory

Malicious reconnaissance is a critical step for attackers to collect sufficient network knowledge and choose valuable targets for intrusion. Defensive deception (DD) is an essential strategy against threats by misleading attackers' observations and beliefs. Honeypots are widely used for cyber deception that aims to confuse attackers and waste their resources and efforts. Defenders may use low-interaction honeypots or high-interaction honeypots. In this paper, we consider a hybrid honeypot system that balances the use of the two levels of honeypot complexity, where high-interaction honeypots are more capable of deceiving skilled attackers than low-interaction honeypots. We present a two-player hypergame model that characterizes how a defender should deploy low and high-interaction honeypots to defend the network against malicious reconnaissance activities. We model the tradeoff of each player and characterize their best strategies within a hypergame framework that considers the imperfect knowledge of each player toward their opponent. Finally, our numerical results validate the effectiveness of the proposed honeypot system.