Miracle-dz/UniFAR-FTUs huggingface dataset:
VR interaction for efficient virtual manufacturing: mini map for multi-user VR navigation platform

Over the past decade, the value and potential of VR applications in manufacturing have gained significant attention in accordance with the rise of Industry 4.0 and beyond. Its efficacy in layout planning, virtual design reviews, and operator training has been well-established in previous studies. However, many functional requirements and interaction parameters of VR for manufacturing remain ambiguously defined. One area awaiting exploration is spatial recognition and learning, crucial for understanding navigation within the virtual manufacturing system and processing spatial data. This is particularly vital in multi-user VR applications where participants' spatial awareness in the virtual realm significantly influences the efficiency of meetings and design reviews. This paper investigates the interaction parameters of multi-user VR, focusing on interactive positioning maps for virtual factory layout planning and exploring the user interaction design of digital maps as navigation aid. A literature study was conducted in order to establish frequently used technics and interactive maps from the VR gaming industry. Multiple demonstrators of different interactive maps provide a comprehensive A/B test which were implemented into a VR multi-user platform using the Unity game engine. Five different prototypes of interactive maps were tested, evaluated and graded by the 20 participants and 40 validated data streams collected. The most efficient interaction design of interactive maps is thus analyzed and discussed in the study.
DSBplot: Indels in DNA Double-strand Break Repair Experiments.

Double-strand breaks (DSBs) in DNA are naturally occurring destructive events in all organisms that may lead to genome instability. Cells employ various repair methods known as non-homologous end joining (NHEJ), microhomology mediated end joining (MMEJ), and homology-directed recombination (HDR). These repair processes may lead to DNA sequence variations (e.g., nucleotide insertions, deletions, and substitutions) at the location of the break. Studying DNA DSB repair processes often involves the use of high throughput sequencing assays to precisely quantify the sequence variations near the break with software tools. Often methods of assessing and visualizing these data have not taken into account the full complexity of the sequencing data, such as the frequency, type, and position of the sequence variations in a single comprehensive representation. Here we present a method that allows visualization of the overall variation pattern as well as comparison of these patterns among experimental setups.
Comparative study of clustering models for multivariate time series from connected medical devices

In healthcare, patient data is often collected as multivariate time series, providing a comprehensive view of a patient's health status over time. While this data can be sparse, connected devices may enhance its frequency. The goal is to create patient profiles from these time series. In the absence of labels, a predictive model can be used to predict future values while forming a latent cluster space, evaluated based on predictive performance. We compare two models on Withing's datasets, M AGMAC LUST which clusters entire time series and DGM${}^2$ which allows the group affiliation of an individual to change over time (dynamic clustering).
Time-reversal symmetry breaking in the chemosensory array: asymmetric switching and dissipation-enhanced sensing.

The Escherichia coli chemoreceptors form an extensive array that achieves cooperative and adaptive sensing of extracellular signals. The receptors control the activity of histidine kinase CheA, which drives a non-equilibrium phosphorylation-dephosphorylation reaction cycle for response regulator CheY. Recent single-cell FRET measurements revealed that kinase activity of the array spontaneously switches between active and inactive states, with asymmetric switching times that signify time-reversal symmetry breaking in the underlying dynamics. Here, we show that the asymmetric switching dynamics can be explained by a non-equilibrium lattice model, which considers both the dissipative reaction cycles of individual core units and the coupling between neighboring units. The model reveals that large dissipation and near-critical coupling are required to explain the observed switching dynamics. Microscopically, the switching time asymmetry originates from irreversible transition paths. The model shows that strong dissipation enables sensitive and rapid signaling response by relieving the speed-sensitivity trade-off, which can be tested by future single-cell experiments. Overall, our model provides a general framework for studying biological complexes composed of coupled subunits that are individually driven by dissipative cycles and the rich non-equilibrium physics within.
An algorithm to construct one-vertex triangulations of Heegaard splittings

Following work of Jaco and Rubinstein (2006), which (non-constructively) proved that any 3-manifold admits a one-vertex layered triangulation, we present an algorithm, with implementation using Regina, that constructs a generalised notion of layered triangulation from a Heegaard diagram. We show that work of Husz\'ar and Spreer (2019) extends to our construction: given a genus-$g$ Heegaard splitting, our algorithm generates a triangulation with cutwidth bounded above by $4g-2$. We also present findings from preliminary computer searches using our algorithm, demonstrating its usefulness.
Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians

Training recurrent neural networks (RNNs) remains a challenge due to the instability of gradients across long time horizons, which can lead to exploding and vanishing gradients. Recent research has linked these problems to the values of Lyapunov exponents for the forward-dynamics, which describe the growth or shrinkage of infinitesimal perturbations. Here, we propose gradient flossing, a novel approach to tackling gradient instability by pushing Lyapunov exponents of the forward dynamics toward zero during learning. We achieve this by regularizing Lyapunov exponents through backpropagation using differentiable linear algebra. This enables us to"floss"the gradients, stabilizing them and thus improving network training. We demonstrate that gradient flossing controls not only the gradient norm but also the condition number of the long-term Jacobian, facilitating multidimensional error feedback propagation. We find that applying gradient flossing prior to training enhances both the success rate and convergence speed for tasks involving long time horizons. For challenging tasks, we show that gradient flossing during training can further increase the time horizon that can be bridged by backpropagation through time. Moreover, we demonstrate the effectiveness of our approach on various RNN architectures and tasks of variable temporal complexity. Additionally, we provide a simple implementation of our gradient flossing algorithm that can be used in practice. Our results indicate that gradient flossing via regularizing Lyapunov exponents can significantly enhance the effectiveness of RNN training and mitigate the exploding and vanishing gradient problem.
Quantum-grade nanodiamonds for ultrabright spin detection in live cells

Optically accessible spin-active nanomaterials are promising as quantum nanosensors for probing biological samples. However, achieving bioimaging-level brightness and high-quality spin properties for these materials is challenging and hinders their application in quantum biosensing. Here, we demonstrate ultrabright fluorescent nanodiamonds (NDs) containing 0.6-1.3-ppm nitrogen-vacancy (NV) centers by spin-environment engineering via enriching spin-less 12C-carbon isotopes and reducing substitutional nitrogen spin impurities. The NDs, readily introduced into cultured cells, exhibited substantially narrow optically detected magnetic resonance (ODMR) spectra, requiring 16-times less microwave excitation power to give an ODMR depth comparable to that of conventional type-Ib NDs. They show average spin-relaxation times of T1 = 0.68 ms and T_2 = 1.6 us (1.6 ms and 2.7 us maximum) that were 5- and 11-fold longer than those of type-Ib, respectively. The bulk-like NV spin properties and bright fluorescence demonstrated in this study significantly improve the sensitivity of ND-based quantum sensors for biological applications.
Towards Scalable Generation of Realistic Test Data for Duplicate Detection

Due to the increasing volume, volatility, and diversity of data in virtually all areas of our lives, the ability to detect duplicates in potentially linked data sources is more important than ever before. However, while research is already intensively engaged in adapting duplicate detection algorithms to the changing circumstances, existing test data generators are still designed for small -- mostly relational -- datasets and can thus fulfill their intended task only to a limited extent. In this report, we present our ongoing research on a novel approach for test data generation that -- in contrast to existing solutions -- is able to produce large test datasets with complex schemas and more realistic error patterns while being easy to use for inexperienced users.
Malware Detection in IOT Systems Using Machine Learning Techniques

Malware detection in IoT environments necessitates robust methodologies. This study introduces a CNN-LSTM hybrid model for IoT malware identification and evaluates its performance against established methods. Leveraging K-fold cross-validation, the proposed approach achieved 95.5% accuracy, surpassing existing methods. The CNN algorithm enabled superior learning model construction, and the LSTM classifier exhibited heightened accuracy in classification. Comparative analysis against prevalent techniques demonstrated the efficacy of the proposed model, highlighting its potential for enhancing IoT security. The study advocates for future exploration of SVMs as alternatives, emphasizes the need for distributed detection strategies, and underscores the importance of predictive analyses for a more powerful IOT security. This research serves as a platform for developing more resilient security measures in IoT ecosystems.
3 0 A pr 2 01 6 Approximating Nash Equilibrium Via Multilinear Minimax

On the one hand we state Nash equilibrium (NE) as a formal theorem on multilinear forms and give a pedagogically simple proof, free of game theory terminology. On the other hand, inspired by this formalism, we prove a multilinear minimax theorem, a generalization of von Neumann’s bilinear minimax theorem. Next, we relate the two theorems by proving that the solution of a multilinear minimax problem, computable via linear programming, serves as an approximation to Nash equilibrium point, where its multilinear value provides an upper bound on a convex combination of expected payoffs. Furthermore, each positive probability vector once assigned to the set of players induces a diagonally-scaled multilinear minimax optimization with a corresponding approximation to NE. In summary, in this note we exhibit an infinity of multilinear minimax optimization problems each of which provides a polynomial-time computable approximation to Nash equilibrium point, known to be difficult to compute. The theoretical and practical qualities of these approximations are the subject of further investigations.
Prompt Fuzzing for Fuzz Driver Generation

Writing high-quality fuzz drivers is time-consuming and requires a deep understanding of the library. However, the performance of the state-of-the-art automatic fuzz driver generation techniques leaves a lot to be desired. Fuzz drivers, which are learned from consumer code, can reach deep states but are restricted to their external inputs. On the other hand, interpretative fuzzing can explore most APIs but requires numerous attempts in a vast search space. We propose PromptFuzz, a coverage-guided fuzzer for prompt fuzzing that iteratively generates fuzz drivers to explore undiscovered library code. To explore API usage in fuzz drivers during prompt fuzzing, we proposed several key techniques: instructive program generation, erroneous program sanitization, coverage-guided prompt mutation, and constrained fuzzer fusion. We implemented PromptFuzz and evaluated its effectiveness on 14 real-world libraries, comparing it against OSS-Fuzz and the state-of-the-art fuzz driver generation solution (i.e., Hopper). The experiment results demonstrate that the fuzz drivers generated by PromptFuzz achieve higher branch coverage that is 1.61 times greater than that of OSS-Fuzz and 1.67 times greater than that of Hopper. In addition, the fuzz drivers generated by PromptFuzz successfully detect 33 true bugs out of a total of 44 crashes, which were previously unknown, and 27 of these bugs have been confirmed by the respective communities.
Detection of evolutionary shifts in variance under an Ornsten-Uhlenbeck model

1. Abrupt environmental changes can lead to evolutionary shifts in not only mean (optimal value), but also variance of descendants in trait evolution. There are some methods to detect shifts in optimal value but few studies consider shifts in variance. 2. We use a multi-optima and multi-variance OU process model to describe the trait evolution process with shifts in both optimal value and variance and provide analysis of how the covariance between species changes when shifts in variance occur along the path. 3. We propose a new method to detect the shifts in both variance and optimal values based on minimizing the loss function with L1 penalty. We implement our method in a new R package, ShiVa (Detection of evolutionary shifts in variance). 4. We conduct simulations to compare our method with the two methods considering only shifts in optimal values (l1ou; PhylogeneticEM). Our method shows strength in predictive ability and includes far fewer false positive shifts in optimal value compared to other methods when shifts in variance actually exist. When there are only shifts in optimal value, our method performs similarly to other methods. We applied our method to the cordylid data, ShiVa outperformed l1ou and phyloEM, exhibiting the highest log-likelihood and lowest BIC.
Data-Driven Template-Free Invariant Generation

Automatic verification of concurrent programs faces state explosion due to the exponential possible interleavings of its sequential components coupled with large or infinite state spaces. An alternative is deductive verification, where given a candidate invariant, we establish inductive invariance and show that any state satisfying the invariant is also safe. However, learning (inductive) program invariants is difficult. To this end, we propose a data-driven procedure to synthesize program invariants, where it is assumed that the program invariant is an expression that characterizes a (hopefully tight) over-approximation of the reachable program states. The main ideas of our approach are: (1) We treat a candidate invariant as a classifier separating states observed in (sampled) program traces from those speculated to be unreachable. (2) We develop an enumerative, template-free approach to learn such classifiers from positive and negative examples. At its core, our enumerative approach employs decision trees to generate expressions that do not over-fit to the observed states (and thus generalize). (3) We employ a runtime framework to monitor program executions that may refute the candidate invariant; every refutation triggers a revision of the candidate invariant. Our runtime framework can be viewed as an instance of statistical model checking, which gives us probabilistic guarantees on the candidate invariant. We also show that such in some cases, our counterexample-guided inductive synthesis approach converges (in probability) to an overapproximation of the reachable set of states. Our experimental results show that our framework excels in learning useful invariants using only a fraction of the set of reachable states for a wide variety of concurrent programs.
Generalized Approach for the Perturbative Dynamical Braneworld in $D$ dimensions

In this paper, we propose an approach to derive the brane cosmology in the $D$-dimensional braneworld model. We generalize the"bulk-based"approach by treating the 4-brane as a small perturbation to the $D$-dimensional spherically symmetric spacetime. The linear corrections from a static 4-brane to the metric are derived from the linearized perturbation equations, while the nonlinear corrections are found by a parameterization of the perturbed metric solution. We use a time-dependent generalization to give the nonlinearly perturbed metric solution for the dynamical braneworld model, and analyse the stability of the model under the motion of the 4-brane. Through the fine tuning, we can recover the Friedmann equations for the universe with and without an effective cosmological constant. More importantly, the de Sitter expansion of the universe can be reproduced.
TuPy-E: detecting hate speech in Brazilian Portuguese social media with a novel dataset and comprehensive analysis of models

Social media has become integral to human interaction, providing a platform for communication and expression. However, the rise of hate speech on these platforms poses significant risks to individuals and communities. Detecting and addressing hate speech is particularly challenging in languages like Portuguese due to its rich vocabulary, complex grammar, and regional variations. To address this, we introduce TuPy-E, the largest annotated Portuguese corpus for hate speech detection. TuPy-E leverages an open-source approach, fostering collaboration within the research community. We conduct a detailed analysis using advanced techniques like BERT models, contributing to both academic understanding and practical applications
Graph Learning in 4D: a Quaternion-valued Laplacian to Enhance Spectral GCNs

We introduce QuaterGCN, a spectral Graph Convolutional Network (GCN) with quaternion-valued weights at whose core lies the Quaternionic Laplacian, a quaternion-valued Laplacian matrix by whose proposal we generalize two widely-used Laplacian matrices: the classical Laplacian (defined for undirected graphs) and the complex-valued Sign-Magnetic Laplacian (proposed within the spectral GCN SigMaNet to handle digraphs with weights of arbitrary sign). In addition to its generality, QuaterGCN is the only Laplacian to completely preserve the (di)graph topology that we are aware of, as it can handle graphs and digraphs containing antiparallel pairs of edges (digons) of different weight without reducing them to a single (directed or undirected) edge as done by other Laplacians. Experimental results show the superior performance of QuaterGCN compared to other state-of-the-art GCNs, particularly in scenarios where the information the digons carry is crucial to successfully address the task at hand.
Culturally-Attuned Moral Machines: Implicit Learning of Human Value Systems by AI through Inverse Reinforcement Learning

Constructing a universal moral code for artificial intelligence (AI) is difficult or even impossible, given that different human cultures have different definitions of morality and different societal norms. We therefore argue that the value system of an AI should be culturally attuned: just as a child raised in a particular culture learns the specific values and norms of that culture, we propose that an AI agent operating in a particular human community should acquire that community's moral, ethical, and cultural codes. How AI systems might acquire such codes from human observation and interaction has remained an open question. Here, we propose using inverse reinforcement learning (IRL) as a method for AI agents to acquire a culturally-attuned value system implicitly. We test our approach using an experimental paradigm in which AI agents use IRL to learn different reward functions, which govern the agents' moral values, by observing the behavior of different cultural groups in an online virtual world requiring real-time decision making. We show that an AI agent learning from the average behavior of a particular cultural group can acquire altruistic characteristics reflective of that group's behavior, and this learned value system can generalize to new scenarios requiring altruistic judgments. Our results provide, to our knowledge, the first demonstration that AI agents could potentially be endowed with the ability to continually learn their values and norms from observing and interacting with humans, thereby becoming attuned to the culture they are operating in.
Social Bots: Detection and Challenges

While social media are a key source of data for computational social science, their ease of manipulation by malicious actors threatens the integrity of online information exchanges and their analysis. In this Chapter, we focus on malicious social bots, a prominent vehicle for such manipulation. We start by discussing recent studies about the presence and actions of social bots in various online discussions to show their real-world implications and the need for detection methods. Then we discuss the challenges of bot detection methods and use Botometer, a publicly available bot detection tool, as a case study to describe recent developments in this area. We close with a practical guide on how to handle social bots in social media research.
Eﬀicient Learning in Games

: We consider the problem of learning strategy selection in games. The theoretical solution to this problem is a distribution over strategies that responds to a Nash equilibrium of the game. When the payoff function of the game is not known to the participants, such a distribution must be approximated directly through repeated play. Full knowledge of the payoff function, on the other hand, restricts agents to be strictly rational. In this classical approach, agents are bound to a Nash equilibrium, even when a globally better solution is obtainable. In this paper, we present an algorithm that allows agents to capitalize on their very lack of information about the payoff structure. The principle we propose is that agents resort to the manipulation of their own payoffs, during the course of learning, to ﬁnd a “game” that gives them a higher payoff than when no manipulation occurs. In essence, the payoffs are considered an extension of the strategy set. At all times, agents remain rational vis-`a-vis the information available. In self-play, the algorithm affords a globally efﬁcient payoff (if it exists).
Finding occupant reference profiles using self-organising maps for thermal performance of buildings

Data mining techniques are a great ally for obtaining profiles to represent occupant's behaviour in buildings. Self-Organising Map (SOM) is an efficient tool that can be used for that purpose. This study aimed to find occupant reference profiles using SOM for building performance simulations purposes. The method consists of using SOM, computer simulations and data analysis. SOM best performance was obtained with an 8x15 hexagonal mesh, reaching 100% accuracy. Three profiles were obtained. The systematic procedure proposed proved to be suitable for clusters formation and may be replicated by other researchers using other data samples.