Miracle-dz/UniFAR-FTUs huggingface dataset:
An approach to the measurement of shunt resistance of individual subcells in thin‐film tandem devices

In this paper, the selective illumination approach is adopted to separately extract the shunt resistance of the individual subcells belonging to a tandem cell. The method relies on simple theoretical considerations and is based on the measurement of the current–voltage characteristic of the tandem cell by alternately keeping one of the subcells under dark conditions. Numerical simulations are employed to support the reliability of the technique, which is experimentally tested on micromorph devices deposited onto glass covered by a V‐shaped transparent conducting oxide and subject to different thermal treatments. Copyright © 2013 John Wiley & Sons, Ltd.
Preparation of Activated Carbon of Lignin from Straw Pulping by Chemical Activation Using Potassium Carbonate

Activated carbons were prepared through chemical activation of lignin from straw pulping precursor using potassium carbonate as the chemical agent. Effects of activated temperature, K2CO3 /lignin ratio and the activated time on the yield, Iodine number of activated carbon were investigated. Experimental results indicated that the optimum conditions were as follow: activated temperature 800°C, K3CO3 (40% concentration) /lignin ratio 5: l, activated time 50min. These conditions allowed us to obtain a BET surface area of 1104 m2/g, including the external or non-microporous surface of 417 m2/g，Amount of methylene blue adsorption, Iodine number and the yield of activated carbon prepared under optimum conditions were 10.6mL/0.lg,1310 mg/g and 19.75%, respectively.
Okamoto Transformations relating Equivariant Instanton Bundles via Painleve VI

We compute explicit solutions Λ m of the Painlevé VI (PVI) differential equation from equivariant instanton bundles Em corresponding to Yang-Mills instantons with “quadrupole symmetry. ” This is based on a generalization of Hitchin’s logarithmic connection to vector bundles with an SL2(C) action. We then identify explicit Okamoto transformation which play the role of “creation operators” for construction Λ m from the “ground state” Λ 0 , suggesting that the equivariant instanton bundles Em might similarly be related to the trivial “ground state” E0.
Fluctuation-driven transitions in localized insulators: Intermittent metallicity and path chaos precede delocalization

We study how interacting localized degrees of freedom are aﬀected by slow thermal ﬂuctuations that change the eﬀective local disorder. We compute the time-averaged (annealed) conductance in the insulating regime and ﬁnd three distinct insulating phases, separated by two transitions. The ﬁrst occurs between a non-resonating insulator and an intermittent metal . The average conductance is always dominated by rare temporal ﬂuctuations. However, in the intermittent metal, they are so strong that the system becomes metallic for an exponentially small fraction of the time. A second transition occurs within that phase. At stronger disorder, there is a single optimal path providing the dominant contribution to the conductance at all times, but closer to delocalization, a transition to a phase with ﬂuctuating paths occurs. This last phase displays the quantum analogon of conﬁgurational chaos in glassy systems in that thermal ﬂuctuations induce signiﬁcant changes of the dominant decay channels. While in the insulator the annealed conductance is strictly bigger than the conductance with typical, frozen disorder, we show that the threshold to delocalization is insensitive to whether or not thermal ﬂuctuations are admitted. This rules out a potential bistability, at ﬁxed disorder, of a localized phase with suppressed internal ﬂuctuations and a delocalized, internally ﬂuctuating phase.
Improving semi-groups bounds with resolvent estimates

The purpose of this paper is to revisit the proof of the Gearhardt-Pr\"uss-Hwang-Greiner theorem for a semigroup $S(t)$, following the general idea of the proofs that we have seen in the literature and to get an explicit estimate on $\Vert S(t) \Vert$ in terms of bounds on the resolvent of the generator. A first version of this paper was presented by the two authors in ArXiv (2010) together with applications in semi-classical analysis and a part of these results has been published later in two books written by the authors. Our aim is to present new improvements, partially motivated by a paper of D. Wei. On the way we discuss optimization problems confirming the optimality of our results.
Enhancing VMAF through New Feature Integration and Model Combination

VMAF is a machine learning based video quality assessment method, originally designed for streaming applications, which combines multiple quality metrics and video features through SVM regression. It offers higher correlation with subjective opinions compared to many conventional quality assessment methods. In this paper we propose enhancements to VMAF through the integration of new video features and alternative quality metrics (selected from a diverse pool) alongside multiple model combination. The proposed combination approach enables training on multiple databases with varying content and distortion characteristics. Our enhanced VMAF method has been evaluated on eight HD video databases, and consistently outperforms the original VMAF model (0.6.1) and other benchmark quality metrics, exhibiting higher correlation with subjective ground truth data.
Understanding the origin of information-seeking exploration in probabilistic objectives for control

The exploration-exploitation trade-off is central to the description of adaptive behaviour in fields ranging from machine learning, to biology, to economics. One popular approach to solving this trade-off has been to propose that agents possess an intrinsic ’exploratory drive’ whereby agent’s are driven to maximize the information they gain about the world through action, as well as the more traditional utility maximization objective. In this paper we mathematically investigate the nature of such objectives and demonstrate that the combination of utility maximizing and information-seeking behaviour arises from the minimization of a class of objectives which we call divergence objectives. We propose a dichotomy in the objective functions underlying adaptive behaviour between evidence objectives, which correspond to well-known reward or utility maximizing objectives and our novel divergence objectives which instead seek to minimize the divergence between the agent’s expected and desired distribution over futures and which give rise to information-seeking exploration. This new class of divergence objectives provides the mathematical foundation for a much richer understanding of the exploratory components of adaptive and intelligent action, beyond simply greedy utility maximization, and can also provide a mathematically elegant explanation for anomalous behaviour often observed in psychophysical and choice tasks such as probability matching.
A Survey of Video Game Testing

Video-game projects are notorious for having day- one bugs, no matter how big their budget or team size. The quality of a game is essential for its success. This quality could be assessed and ensured through testing. However, to the best of our knowledge, little is known about video-game testing. In this paper, we want to understand how game developers perform game testing. We investigate, through a survey, the academic and gray literature to identify and report on existing testing processes and how they could automate them. We found that game developers rely, almost exclusively, upon manual play-testing and the testers' intrinsic knowledge. We conclude that current testing processes fall short because of their lack of automation, which seems to be the natural next step to improve the quality of games while maintaining costs. However, the current game-testing techniques may not generalize to different types of games.
Face Images as Jigsaw Puzzles: Compositional Perception of Human Faces for Machines Using Generative Adversarial Networks

An important goal in human-robot-interaction (HRI) is for machines to achieve a close to human level of face perception. One of the important differences between machine learning and human intelligence is the lack of compositionality. This paper introduces a new scheme to enable generative adversarial networks to learn the distribution of face images composed of smaller parts. This results in a more flexible machine face perception and easier generalization to outside training examples. We demonstrate that this model is able to produce realistic high-quality face images by generating and piecing together the parts. Additionally, we demonstrate that this model learns the relations between the facial parts and their distributions. Therefore, the specific facial parts are interchangeable between generated face images.
Drone-as-a-Service Composition Under Uncertainty

We propose an uncertainty-aware service approach to provide drone-based delivery services called Drone-as-a-Service (DaaS) effectively. Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts. The proposed DaaS service approach consists of three components: scheduling, route-planning, and composition. First, we develop a DaaS scheduling model to generate DaaS itineraries through a Skyway network. Second, we propose an uncertainty-aware DaaS route-planning algorithm that selects the optimal Skyways under weather uncertainties. Third, we develop two DaaS composition techniques to select an optimal DaaS composition at each station of the planned route. A spatiotemporal DaaS composer first selects the optimal DaaSs based on their spatiotemporal availability and drone capabilities. A predictive DaaS composer then utilises the outcome of the first composer to enable fast and accurate DaaS composition using several Machine Learning classification methods. We train the classifiers using a new set of spatiotemporal features which are in addition to other DaaS QoS properties. Our experiments results show the effectiveness and efficiency of the proposed approach.
LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management

To avoid serious diabetic complications, people with type 1 diabetes must keep their blood glucose levels (BGLs) as close to normal as possible. Insulin dosages and carbohydrate consumption are important considerations in managing BGLs. Since the 1960s, models have been developed to forecast blood glucose levels based on the history of BGLs, insulin dosages, carbohydrate intake, and other physiological and lifestyle factors. Such predictions can be used to alert people of impending unsafe BGLs or to control insulin flow in an artificial pancreas. In past work, we have introduced an LSTM-based approach to blood glucose level prediction aimed at “what-if” scenarios, in which people could enter foods they might eat or insulin amounts they might take and then see the effect on future BGLs. In this work, we invert the “what-if” scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future. Leveraging a recent state-of-the-art model for time series forecasting, we then derive a novel architecture for the same recommendation task, in which the two LSTM chain is used as a repeating block inside a deep residual architecture. Experimental evaluations using real patient data from the OhioT1DM dataset show that the new integrated architecture compares favorably with the previous LSTM-based approach, substantially outperforming the baselines. The promising results suggest that this novel approach could potentially be of practical use to people with type 1 diabetes for self-management of BGLs.
Covariant phase space for gravity with boundaries: Metric versus tetrad formulations

We use covariant phase space methods to study the metric and tetrad formulations of General Relativity in a manifold with boundary and compare the results obtained in both approaches. Proving their equivalence has been a long-lasting problem that we solve here by using the cohomological approach provided by the relative bicomplex framework. This setting provides a clean and ambiguity-free way to describe the solution spaces and associated symplectic structures. We also compute several relevant charges in both schemes and show that they are equivalent, as expected.
The electrostatic free energy of polyelectrolyte solutions. II. Fully stretched macromolecules

(1) The electrostatic internal energy of a solution of rodlike, ionized polymolecules is calculated by an integration which makes use of the molecular potentials evaluated previously. (2) The electrostatic free energy is calculated by a charging process for which the reference state is a discharged solution of the same concentration as that of the charged solution. (3) A series of graphs is presented for the numerical evaluation of the internal and free energies of salt-free polyelectrolyte solutions as a function of concentration, degree of ionization, and temperature. (4) The osmotic pressure of stretched polyelectrolytes is calculated from the free energy expression. The calculated osmotic coefficients are practically independent of molecular weight and polyelectrolyte concentration, and are determined by the degree of ionization. A comparison with the measurements of Kern on salt-free polyacrylic acid at higher degrees of ionization (for which the macromolecules are stretched) gives a very satisfactory agreement. (5) An expression for the light scattering by polyelectrolyte solutions is derived from the equation for osmotic pressure and discussed briefly.
TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack

Network intrusion attacks are a known threat. To detect such attacks, network intrusion detection systems (NIDSs) have been developed and deployed. These systems apply machine learning models to high-dimensional vectors of features extracted from network traffic to detect intrusions. Advances in NIDSs have made it challenging for attackers, who must execute attacks without being detected by these systems. Prior research on bypassing NIDSs has mainly focused on perturbing the features extracted from the attack traffic to fool the detection system, however, this may jeopardize the attack’s functionality. In this work, we present TANTRA, a novel end-to-end Timing-based Adversarial Network Traffic Reshaping Attack that can bypass a variety of NIDSs. Our evasion attack utilizes a long short-term memory (LSTM) deep neural network (DNN) which is trained to learn the time differences between the target network’s benign packets. The trained LSTM is used to set the time differences between the malicious traffic packets (attack), without changing their content, such that they will “behave” like benign network traffic and will not be detected as an intrusion. We evaluate TANTRA on eight common intrusion attacks and three state-of-the-art NIDS systems, achieving an average success rate of 99.99% in network intrusion detection system evasion. We also propose a novel mitigation technique to address this new evasion attack.
Multi-Link Vehicular Wireless Channel Modelling: Impact of Large Obstructing Vehicles

Multi-node channel sounding simultaneously captures multiple channel transfer functions in complex scenarios. It ensures that measurement conditions are identical for all observed links. This is particularly beneﬁcial in analyzing highly-dynamic vehicular communication scenarios, where the measurement conditions and wireless channel statistics change rapidly. We present the ﬁrst fully mobile multi-node vehicular wireless channel sounding results. We analyze the impact of a double-decker bus on the channel statistics in two measurement scenarios. The measurement data is used to calibrate an OpenStreetMap geometry-based stochastic channel model and include a more accurate modeling for large vehicles, absent from previous models. We validate our model using a link-level emulation, demonstrating a good match with measurement data. Finally, we propose a relaying scenario and test it on link-level by comparing the time-variant error rates of measured links
TAG: Transformer Attack from Gradient

—Although federated learning has increasingly gained attention in terms of effectively utilizing local devices for data privacy enhancement, recent studies show that publicly shared gradients in the training process can reveal the private training images (gradient leakage) to a third-party in computer vision. We have, however, no systematic understanding of the gradient leakage mechanism on the Transformer based language models. In this paper, as the ﬁrst attempt, we formulate the gradient attack problem on the Transformer-based language models and propose a gradient attack algorithm, TAG, to reconstruct the local training data. We develop a set of metrics to evaluate the effectiveness of the proposed attack algorithm quantitatively. Experimental results on Transformer, TinyBERT 4 , TinyBERT 6 , BERT BASE , and BERT LARGE using GLUE benchmark show that TAG works well on more weight distributions in reconstructing training data and achieves 1.5 × recover rate and 2.5 × ROUGE-2 over prior methods without the need of ground truth label. TAG can obtain up to 90 % data by attacking gradients in CoLA dataset. In addition, TAG has a stronger adversary on large models, small dictionary size, and small input length. We hope the proposed TAG will shed some light on the privacy leakage problem in Transformer-based NLP models.
SEMISTABLE DEGENERATIONS OF Q-FANO GROUP COMPACTIFICATIONS

Let G be a compex reductive group and M be a Fano compactification of G. In this paper, we first express the H-invariant of an arbitrary equivariant R-special test configuration on M in terms of combinatory data. Then based on [18], we compute out the semistable limit of a K-unstable Fano G-compactification. We further show that for the two K-unstable Fano SO4(C)-compactifications, the corresponding semistable limits are indeed the limit spaces of the normalized Kähler-Ricci flow.
Designing and Implementing e-justice Systems: An Information Systems Approach to Regional Trial Court Case Docket Management in Northern Mindanao, Philippines

Computer-based information systems for case management are still at an early stage of adoption in many trial courts in the Philippines. In most cases, information system implemented is the case docket using the official record book on which cases are written and inventory of cases and reports are generated. This is a standalone system that often face data processing, data security and case management challenges. However, there are examples of Information systems in overcoming these pitfalls and producing innovative solutions that surpass data management practices in in many trial courts in the country. One such case is the Regional Trial Court Branch 23 of Cagayan de Oro City in Northern Mindanao, Philippines. A project named Web-based Case Docket Information System (WCDIS) has been designed and developed for the court branch. This system uses a framework known as System Development Life Cycle (SDLC) which is a guide for the design and development. This paper also discusses the key system functionalities and the implementation methodology, including both the benefits and shortcomings of this approach, with the goal of applying lessons learned for future installations. Foremost among the successes of this project is its ability to increase efficiency and reliability in completing court transactions.
Policy search with rare significant events: Choosing the right partner to cooperate with

This paper focuses on a class of reinforcement learning problems where significant events are rare and limited to a single positive reward per episode. A typical example is that of an agent who has to choose a partner to cooperate with, while a large number of partners are simply not interested in cooperating, regardless of what the agent has to offer. We address this problem in a continuous state and action space with two different kinds of search methods: a gradient policy search method and a direct policy search method using an evolution strategy. We show that when significant events are rare, gradient information is also scarce, making it difficult for policy gradient search methods to find an optimal policy, with or without a deep neural architecture. On the other hand, we show that direct policy search methods are invariant to the rarity of significant events, which is yet another confirmation of the unique role evolutionary algorithms has to play as a reinforcement learning method.
Strong interlayer charge transfer due to exciton condensation in an electrically isolated GaAs quantum well bilayer

We introduce a design of electrically isolated “floating” bilayer GaAs quantum wells (QW) in which application of a large gating voltage controllably and highly reproducibly induces charges that remain trapped in the bilayer after removal of the gating voltage. At smaller gate voltages, the bilayer is fully electrically isolated from external electrodes by thick insulating barriers. This design permits full control of the total and differential densities of two coupled 2D electron systems. The floating bilayer design provides a unique approach for studying systems inaccessible by simple transport measurements. It also provides the ability to measure the charge transfer between the layers, even when the in-plane resistivities of the 2D systems diverge. We measure the capacitance and inter-layer tunneling spectra of the QW bilayer with independent control of the top and bottom layer electron densities. Our measurements display strongly enhanced inter-layer tunneling current at v = 1, a signature of exciton condensation of a strongly interlayer-correlated bilayer system. With fully tunable densities of individual layers, the floating bilayer QW system provides a versatile platform to access previously unavailable information on the quantum phases in electron bilayer systems. Weakly coupled quantum Hall (QH) bilayer systems display a variety of exotic phases as the filling factor of the two layers vary [1–3]. Under quantizing magnetic fields, decreasing the separation distance of two layers to become comparable to the mean interparticle spacing can drive the double layer system into phases not possible in single layers [4–6]. When the individual layers are in strongly intra-layer correlated states such as fractional quantum Hall phases, the additional interlayer coupling can play a non-trivial role that modifies the properties of the phases or
Surface‐relief diffraction gratings' optimization for plasmonic enhancements in thin‐film solar cells

Methodical and intensive surface plasmon (SP) excitation trials were carried out on various dielectric‐metal interfaces to optimize plasmonic photocurrent enhancements in organic P3HT‐PCBM photovoltaic thin films. The SPs were optically excited via the diffraction grating method using single, crossed, and parallel grating schemes, with trials yielding optimal grating and film thickness parameters. Photocurrent enhancements up to 355% were demonstrated with TM‐polarized incident light on single and parallel grating structures, while both TM and TE‐polarized incident light enhancements were present on crossed grating structures. When compared with the photocurrent enhancements seen on single gratings, those seen on parallel gratings were comparable in magnitude but were shown over a broader optical band. This broadening of the optical band was due to the simultaneous SP excitations by the two superimposed gratings in the parallel scheme. Copyright © 2012 John Wiley & Sons, Ltd.