Miracle-dz/UniFAR-FTUs huggingface dataset:
Web-based Database Courses E-Learning Application

This study was focused on the development of a web e-learning application for the database courses taken by Information Technology (IT) students at the College of Information and Communications Technology (CICT) of Bulacan State University (BulSU). The research methodology used in this project was the cross-sectional developmental approach. The Agile Software Development methodology was followed phase by phase, up to the development phase, to develop the system. It was used to produce the desired output rapidly while allowing users to go back through phases without finishing the whole cycle. The goal of this study was to create a web application that teaches Structured Query Language (SQL) in both the MySQL and SQL Server approaches. The application contains quizzes and examinations to allow self-assessment of learning. Additionally, an Entity Relationship Diagram (ERD) simulation was included to provide ERD creations in a drag-and-drop fashion. This study was evaluated using ISO/IEC 25010 software quality evaluation criteria. The study's overall mean was 4.24, 4.41, and 4.33, all with the descriptive meaning of"Very Good,"which showed that the system performed its necessary functions as perceived by students, faculty members, and experts, respectively. In summary, the e-learning web application for database courses was fully developed. Moreover, the entity-relationship diagram was integrated well within the system and is accessible to the users. Lastly, respondents evaluated the developed web application using the ISO/IEC 25010 with an overall descriptive interpretation of"Very Good. "For future developments of the study, an administrator panel may be developed to manage users and do other administrative tasks. Lastly, higher-order thinking skills questions on assessments and quizzes may be included.
Predictive Control with Learning-Based Terminal Costs Using Approximate Value Iteration

Stability under model predictive control (MPC) schemes is frequently ensured by terminal ingredients. Employing a (control) Lyapunov function as the terminal cost constitutes a common choice. Learning-based methods may be used to construct the terminal cost by relating it to, for instance, an infinite-horizon optimal control problem in which the optimal cost is a Lyapunov function. Value iteration, an approximate dynamic programming (ADP) approach, refers to one particular cost approximation technique. In this work, we merge the results of terminally unconstrained predictive control and approximate value iteration to draw benefits from both fields. A prediction horizon is derived in dependence on different factors such as approximation-related errors to render the closed-loop asymptotically stable further allowing a suboptimality estimate in comparison to an infinite horizon optimal cost. The result extends recent studies on predictive control with ADP-based terminal costs, not requiring a local initial stabilizing controller. We compare this controller in simulation with other terminal cost options to show that the proposed approach leads to a shorter minimal horizon in comparison to previous results.
Locally Frobenius algebras and Hopf algebras

We develop a theory of \emph{locally Frobenius algebras} which are colimits of certain directed systems of Frobenius algebras. A major goal is to obtain analogues of the work of Moore \&Peterson and Margolis on \emph{nearly Frobenius algebras} and \emph{$P$-algebras} which was applied to graded Hopf algebras such as the Steenrod algebra for a prime. Such locally Frobenius algebras are coherent and in studying their modules we are naturally led to focus on coherent and finite dimensional modules. Indeed, the category of coherent modules over locally Frobenius algebra $A$ is abelian with enough projectives and injectives since $A$ is injective relative to the coherent modules; however it only has finite limits and colimits. The finite dimensional modules also form an abelian category but finite dimensional modules are never coherent. The minimal ideals of a locally Frobenius algebra are precisely the ones which are isomorphic to coherent simple modules; in particular it does not contain a copy of any finite dimensional simple module so it is not a Kasch algebra. We discuss possible versions of stable module categories for such algebras. We also discuss possible monoidal structures on module categories of a locally Frobenius Hopf algebra: for example tensor products of coherent modules turn out to be pseudo-coherent. Examples of locally Frobenius Hopf algebras include group algebras of locally finite groups, already intensively studied in the literature.
Learning Transition Operators From Sparse Space-Time Samples

We consider the nonlinear inverse problem of learning a transition operator $\mathbf{A}$ from partial observations at different times, in particular from sparse observations of entries of its powers $\mathbf{A},\mathbf{A}^2,\cdots,\mathbf{A}^{T}$. This Spatio-Temporal Transition Operator Recovery problem is motivated by the recent interest in learning time-varying graph signals that are driven by graph operators depending on the underlying graph topology. We address the nonlinearity of the problem by embedding it into a higher-dimensional space of suitable block-Hankel matrices, where it becomes a low-rank matrix completion problem, even if $\mathbf{A}$ is of full rank. For both a uniform and an adaptive random space-time sampling model, we quantify the recoverability of the transition operator via suitable measures of incoherence of these block-Hankel embedding matrices. For graph transition operators these measures of incoherence depend on the interplay between the dynamics and the graph topology. We develop a suitable non-convex iterative reweighted least squares (IRLS) algorithm, establish its quadratic local convergence, and show that, in optimal scenarios, no more than $\mathcal{O}(rn \log(nT))$ space-time samples are sufficient to ensure accurate recovery of a rank-$r$ operator $\mathbf{A}$ of size $n \times n$. This establishes that spatial samples can be substituted by a comparable number of space-time samples. We provide an efficient implementation of the proposed IRLS algorithm with space complexity of order $O(r n T)$ and per-iteration time complexity linear in $n$. Numerical experiments for transition operators based on several graph models confirm that the theoretical findings accurately track empirical phase transitions, and illustrate the applicability and scalability of the proposed algorithm.
Mapping the optical intensity distribution in photonic crystals using a near-field scanning optical microscope

A near-field optical microscope (NSOM) is used in collection mode to map the optical intensity distribution inside channel glass photonic crystal samples. The intensity distribution at wavelengths spanning the fundamental transmission minimum (i.e., the fundamental photonic band gap) was studied. The dependence of the NSOM signal on tip-sample separation allows us to confirm that the NSOM is probing the evanescent tail of the light confined to the crystal. At a given wavelength, NSOM images of low-index-contrast unetched samples, consisting of glass cylinders in a matrix of a different glass material, show bands of high and low optical intensity. The spacings of these bands are comparable to the cylinder separation, but display a nontrivial wavelength dependence. We show that these results can be explained by interference between incoming and reflected light inside the photonic crystal. Based on this, we can then determine dispersion relations of the photonic crystal modes directly from the NSOM images. Our experimental results are consistent with the calculated dispersion relations of the lowest order photonic crystal modes with no adjustable parameters. In addition we show an example where the NSOM images reflect the variation in coupling due to sample features rather than the guided mode structures. Based on these results, we discuss possible improvements in photonic crystal design for NSOM studies.
Game Implementation: What Are the Obstructions?

In many applications, we want to influence the decisions of independent agents by designing incentives for their actions. We revisit a fundamental problem in this area, called GAME IMPLEMENTATION: Given a game in standard form and a set of desired strategies, can we design a set of payment promises such that if the players take the payment promises into account, then all undominated strategies are desired? Furthermore, we aim to minimize the cost, that is, the worst-case amount of payments. We study the tractability of computing such payment promises and determine more closely what obstructions we may have to overcome in doing so. We show that GAME IMPLEMENTATION is NP-hard even for two players, solving in particular a long-standing open question and suggesting more restrictions are necessary to obtain tractability results. We thus study the regime in which players have only a small constant number of strategies and obtain the following. First, this case remains NP-hard even if each player’s utility depends only on three others. Second, we repair a flawed efficient algorithm for the case of both small number of strategies and small number of players. Among further results, we characterize sets of desired strategies that can be implemented at zero cost as a generalization of Nash equilibria.
Haagerup bound for quaternionic Grothendieck inequality

We present here several versions of the Grothendieck inequality over the skew ﬁeld of quaternions: The ﬁrst one is the standard Grothendieck inequality for rectangular matrices, and two additional inequalities for self-adjoint matrices, as introduced by the ﬁrst and the last authors in a recent paper. We give several results on “conic Grothendieck inequality”: as Nesterov π/ 2-Theorem, which corresponds to the cones of positive semideﬁnite matrices; the Goemans–Williamson inequality, which corresponds to the cones of weighted Laplacians; the diagonally dominant matrices. The most challenging technical part of this paper is the proof of the analog of Haagerup result that the inverse of the hypergeometric function x 2 F 1 (cid:0) 12 , 12 ; 3; x 2 (cid:1) has ﬁrst positive Taylor coeﬃcient and all other Taylor coeﬃcients are nonpositive.
A Novel Framework for Decentralized Dynamic Resource Allocation Using Voronoi Tessellations

In this work, we approach the problem of resource allocation in a team of agents through the framework of Centroidal Voronoi Tessellations. CVTs provide a natural way to embed a desired global trend in the team through probability distributions, and in one-dimensional spaces, CVTs offer an inherent line structure allowing for a simple communication graph and scalability. We first consider the amount of resource to be allocated to be a constant and provide an analytical solution to such static resource allocation problem by embedding the allocation constraint within the distribution through a system of nonlinear equations. Using the solution of such a constrained CVT minimization problem as an initialization step, we propose a decentralized dynamic resource allocation solution that employs a one-step update when the desired distribution is Gaussian. We introduce a"civility model"for negotiations between the agents to allow for flexibility in local preferences and maintaining robustness against local disturbances. We demonstrate the effectiveness of the proposed method by considering the application of demand-response in smart grids through the problem of power allocation in a group of building thermal loads.
On the power of nonstandard quantum oracles

We study how the choices made when designing an oracle affect the complexity of quantum property testing problems defined relative to this oracle. We encode a regular graph of even degree as an invertible function $f$, and present $f$ in different oracle models. We first give a one-query QMA protocol to test if a graph encoded in $f$ has a small disconnected subset. We then use representation theory to show that no classical witness can help a quantum verifier efficiently decide this problem relative to an in-place oracle. Perhaps surprisingly, a simple modification to the standard oracle prevents a quantum verifier from efficiently deciding this problem, even with access to an unbounded witness.
Propaganda and Misinformation on Facebook and Twitter during the Russian Invasion of Ukraine

Online social media represent an oftentimes unique source of information, and having access to reliable and unbiased content is crucial, especially during crises and contentious events. We study the spread of propaganda and misinformation that circulated on Facebook and Twitter during the first few months of the Russia-Ukraine conflict. By leveraging two large datasets of millions of social media posts, we estimate the prevalence of Russian propaganda and low-credibility content on the two platforms, describing temporal patterns and highlighting the disproportionate role played by superspreaders in amplifying unreliable content. We infer the political leaning of Facebook pages and Twitter users sharing propaganda and misinformation, and observe they tend to be more right-leaning than the average. By estimating the amount of content moderated by the two platforms, we show that only about 8-15% of the posts and tweets sharing links to Russian propaganda or untrustworthy sources were removed. Overall, our findings show that Facebook and Twitter are still vulnerable to abuse, especially during crises: we highlight the need to urgently address this issue to preserve the integrity of online conversations.
Real-Time Trust Prediction in Conditionally Automated Driving Using Physiological Measures

Trust calibration poses a significant challenge in the interaction between drivers and automated vehicles (AVs) in the context of human-automation collaboration. To effectively calibrate trust, it becomes crucial to accurately measure drivers’ trust levels in real time, allowing for timely interventions or adjustments in the automated driving. One viable approach involves employing machine learning models and physiological measures to model the dynamic changes in trust. This study introduces a technique that leverages machine learning models to predict drivers’ real-time dynamic trust in conditional AVs using physiological measurements. We conducted the study in a driving simulator where participants were requested to take over control from automated driving in three conditions that included a control condition, a false alarm condition, and a miss condition. Each condition had eight takeover requests (TORs) in different scenarios. Drivers’ physiological measures were recorded during the experiment, including galvanic skin response (GSR), heart rate (HR) indices, and eye-tracking metrics. Using five machine learning models, we found that eXtreme Gradient Boosting (XGBoost) performed the best and was able to predict drivers’ trust in real time with an f1-score of 89.1% compared to a baseline model of $K$ -nearest neighbor classifier of 84.5%. Our findings provide good implications on how to design an in-vehicle trust monitoring system to calibrate drivers’ trust to facilitate interaction between the driver and the AV in real time.
Towards Real-World Video Denosing: A Practical Video Denosing Dataset and Network

To facilitate video denoising research, we construct a compelling dataset, namely,"Practical Video Denoising Dataset"(PVDD), containing 200 noisy-clean dynamic video pairs in both sRGB and RAW format. Compared with existing datasets consisting of limited motion information, PVDD covers dynamic scenes with varying and natural motion. Different from datasets using primarily Gaussian or Poisson distributions to synthesize noise in the sRGB domain, PVDD synthesizes realistic noise from the RAW domain with a physically meaningful sensor noise model followed by ISP processing. Moreover, we also propose a new video denoising framework, called Recurrent Video Denoising Transformer (RVDT), which can achieve SOTA performance on PVDD and other current video denoising benchmarks. RVDT consists of both spatial and temporal transformer blocks to conduct denoising with long-range operations on the spatial dimension and long-term propagation on the temporal dimension. Especially, RVDT exploits the attention mechanism to implement the bi-directional feature propagation with both implicit and explicit temporal modeling. Extensive experiments demonstrate that 1) models trained on PVDD achieve superior denoising performance on many challenging real-world videos than on models trained on other existing datasets; 2) trained on the same dataset, our proposed RVDT can have better denoising performance than other types of networks.
Acoustic microstreaming for droplet breakup in a microflow-focusing device

This study reports on the breaking up of droplets which can be manipulated with acoustic fields. The oscillation of vortex in a breaking droplet is observed. The droplet size is dependent on the flow-rate combination of the two fluids as well as the frequency and power of the acoustic actuation. Acoustic microstreaming flow is observed in the dispersed phase at the cross-junction of the device. The microstreaming flow causes a stratified vortex flow structure within the dispersed phase. Two stratified vortex centers at the side poles of the droplet are found.
Audio-Visual Activity Guided Cross-Modal Identity Association for Active Speaker Detection

Active speaker detection in videos addresses associating a source face, visible in the video frames, with the underlying speech in the audio modality. The two primary sources of information to derive such a speech-face relationship are i) visual activity and its interaction with the speech signal and ii) co-occurrences of speakers' identities across modalities in the form of face and speech. The two approaches have their limitations: the audio-visual activity models get confused with other frequently occurring vocal activities, such as laughing and chewing, while the speakers' identity-based methods are limited to videos having enough disambiguating information to establish a speech-face association. Since the two approaches are independent, we investigate their complementary nature in this work. We propose a novel unsupervised framework to guide the speakers' cross-modal identity association with the audio-visual activity for active speaker detection. Through experiments on entertainment media videos from two benchmark datasets–the AVA active speaker (movies) and Visual Person Clustering Dataset (TV shows)–we show that a simple late fusion of the two approaches enhances the active speaker detection performance.
Elastic, Piezoelectric Coefficients, and Internal Frictions of a Single Alpha-Quartz Crystal Determined by Partial-Electrode Electromechanical Impedance Spectroscopy

In this work, all independent elastic coefficients, piezoelectric coefficients, and internal frictions of a single alpha-quartz crystal are determined at room temperature using our recently proposed partial-electrode electromechanical impedance spectroscopy (PE-EMIS). In PE-EMIS, the rectangular parallelepiped quartz sample with two small partial electrodes fabricated on a corner is self-excited/sensed. The conductance spectrum (equivalent to the acoustic resonance spectrum) measured by an impedance analyzer under a free boundary condition is noiseless, allowing the first 100 eigenmodes ranging from 50 to 310 kHz to be accurately fit. To avoid mode misidentification, the off-plane displacement distributions of the quartz sample under different eigenmodes are determined using a scanned-laser vibrometer. The resonance spectrum measured by a traditional sandwich-like resonant ultrasound spectroscopy (RUS) apparatus is also presented for comparison, and the results show that the clamping force in sandwich-like RUS shifts the sample’s resonance frequencies, causing 13% and 75% overestimations for the piezoelectric coefficients e11 and e14, respectively. In comparison with the RUS, our proposed PE-EMIS is more effective and convenient and will be widely used for characterization of piezoelectric crystals.
Random Copolymer inverse design system orienting on Accurate discovering of Antimicrobial peptide-mimetic copolymers

Antimicrobial resistance is one of the biggest health problem, especially in the current period of COVID-19 pandemic. Due to the unique membrane-destruction bactericidal mechanism, antimicrobial peptide-mimetic copolymers are paid more attention and it is urgent to find more potential candidates with broad-spectrum antibacterial efficacy and low toxicity. Artificial intelligence has shown significant performance on small molecule or biotech drugs, however, the higher-dimension of polymer space and the limited experimental data restrict the application of existing methods on copolymer design. Herein, we develop a universal random copolymer inverse design system via multi-model copolymer representation learning, knowledge distillation and reinforcement learning. Our system realize a high-precision antimicrobial activity prediction with few-shot data by extracting various chemical information from multi-modal copolymer representations. By pre-training a scaffold-decorator generative model via knowledge distillation, copolymer space are greatly contracted to the near space of existing data for exploration. Thus, our reinforcement learning algorithm can be adaptive for customized generation on specific scaffolds and requirements on property or structures. We apply our system on collected antimicrobial peptide-mimetic copolymers data, and we discover candidate copolymers with desired properties.
P(Expression|Grammar): Probability of deriving an algebraic expression with a probabilistic context-free grammar

Probabilistic context-free grammars have a long-term record of use as generative models in machine learning and symbolic regression. When used for symbolic regression, they generate algebraic expressions. We define the latter as equivalence classes of strings derived by grammar and address the problem of calculating the probability of deriving a given expression with a given grammar. We show that the problem is undecidable in general. We then present specific grammars for generating linear, polynomial, and rational expressions, where algorithms for calculating the probability of a given expression exist. For those grammars, we design algorithms for calculating the exact probability and efficient approximation with arbitrary precision.
ODPP: A Unified Algorithm Framework for Unsupervised Option Discovery based on Determinantal Point Process

—Learning rich skills through temporal abstractions without supervision of external rewards is at the frontier of Reinforcement Learning research. Existing works mainly fall into two distinctive categories: variational and Laplacian-based option discovery. The former maximizes the diversity of the discovered options through a mutual information loss but overlooks coverage of the state space, while the latter focuses on improving the coverage of options by increasing connectivity during exploration, but does not consider diversity. In this paper, we propose a uniﬁed framework that quantiﬁes diversity and coverage through a novel use of the Determinantal Point Process (DPP) and enables unsupervised option discovery explicitly optimizing both objectives. Speciﬁcally, we deﬁne the DPP kernel matrix with the Laplacian spectrum of the state transition graph and use the expected mode number in the trajectories as the objective to capture and enhance both diversity and coverage of the learned options. The proposed option discovery algorithm is extensively evaluated using challenging tasks built with Mu-joco and Atari, demonstrating that our proposed algorithm substantially outperforms SOTA baselines from both diversity-and coverage-driven categories. The codes are available at https://github.com/LucasCJYSDL/ODPP.