Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Machine Learning and Transformer-based Approaches for Deceptive Text Classification: A Comparative Analysis

Deceptive text classification is a critical task in natural language processing that aims to identify deceptive o fraudulent content. This study presents a comparative analysis of machine learning and transformer-based approaches for deceptive text classification. We investigate the effectiveness of traditional machine learning algorithms and state-of-the-art transformer models, such as BERT, XLNET, DistilBERT, and RoBERTa, in detecting deceptive text. A labeled dataset consisting of deceptive and non-deceptive texts is used for training and evaluation purposes. Through extensive experimentation, we compare the performance metrics, including accuracy, precision, recall, and F1 score, of the different approaches. The results of this study shed light on the strengths and limitations of machine learning and transformer-based methods for deceptive text classification, enabling researchers and practitioners to make informed decisions when dealing with deceptive content.
Vacuum radiation versus shortcuts to adiabaticity

The nonadiabatic dynamic of the electromagnetic field triggers photons generation from the quantum vacuum. Shortcuts to adiabaticity, instead, are protocols that mimic the field's adiabatic dynamic in a finite time. Here, we show how the counterdiabatic term of the transitionless tracking algorithm cancels out, exactly, the term responsible for the photon production in the dynamical Casimir effect. This result suggests that the energy of producing photons out of the vacuum is related to the energetic cost of the shortcut. Furthermore, if the system operates under a quantum thermodynamic cycle, we confirm the equivalence between the adiabatic and nonadiabatic work outputs. Finally, our study reveals that identifying these unreported observations can only be possible using the so-called effective Hamiltonian approach.
Sublimation of refractory minerals in the gas envelopes of accreting rocky planets

Protoplanets growing within the protoplanetary disk by pebble accretion acquire hydrostatic gas envelopes. Due to accretion heating, the temperature in these envelopes can become high enough to sublimate refractory minerals which are the major components of the accreted pebbles. Here we study the sublimation of different mineral species and determine whether sublimation plays a role during the growth by pebble accretion. For each snapshot in the growth process, we calculate the envelope structure and sublimation temperature of a set of mineral species representing different levels of volatility. Sublimation lines are determined using and equilibrium scheme for the chemical reactions responsible for destruction and formation of the relevant minerals. We find that the envelope of the growing planet reaches temperatures high enough to sublimate all considered mineral species when the mass is larger than 0.4 Earth masses. The sublimation lines are located within the gravitionally bound envelope of the planet. We make a detailed analysis of the sublimation of FeS at around 720 K, beyond which the mineral is attacked by H2 to form gaseous H2S and solid Fe. We calculate the sulfur concentration in the planet under the assumption that all sulfur released as H2S is lost from the planet by diffusion back to the protoplanetary disk. Our calculated values are in good agreement with the slightly depleted sulfur abundance of Mars, while the model overpredicts the extensive sulfur depletion of Earth by a factor of approximately 2. We show that a collision with a sulfur-rich body akin to Mars in the moon-forming impact lifts the Earth's sulfur abundance to approximately 10% of the solar value for all impactor masses above 0.05 Earth masses.
Robust Lifelong Indoor LiDAR Localization Using the Area Graph

Lifelong indoor localization in a given map is the basis for navigation of autonomous mobile robots. In this letter, we address the problem of robust localization in cluttered indoor environments like office spaces and corridors using 3D LiDAR point clouds in a given Area Graph, which is a hierarchical, topometric semantic map representation that uses polygons to demark areas such as rooms, corridors or buildings. This representation is very compact, can represent different floors of buildings through its hierarchy and provides semantic information that helps with localization, like poses of doors and glass. In contrast to this, commonly used map representations, such as occupancy grid maps or point clouds, lack these features and require frequent updates in response to environmental changes (e.g. moved furniture), unlike our approach, AGLoc, which matches against lifelong architectural features such as walls and doors. For that we apply filtering to remove clutter from the 3D input point cloud and then employ further scoring and weight functions for localization. Given a broad initial guess from WiFi and barometer localization, our experiments show that our global localization and the weighted point to line ICP pose tracking perform very well, even when compared to localization and SLAM algorithms that use the current, feature-rich cluttered map for localization.
Alexa, play with robot: Introducing the First Alexa Prize SimBot Challenge on Embodied AI

The Alexa Prize program has empowered numerous university students to explore, experiment, and showcase their talents in building conversational agents through challenges like the SocialBot Grand Challenge and the TaskBot Challenge. As conversational agents increasingly appear in multimodal and embodied contexts, it is important to explore the affordances of conversational interaction augmented with computer vision and physical embodiment. This paper describes the SimBot Challenge, a new challenge in which university teams compete to build robot assistants that complete tasks in a simulated physical environment. This paper provides an overview of the SimBot Challenge, which included both online and offline challenge phases. We describe the infrastructure and support provided to the teams including Alexa Arena, the simulated environment, and the ML toolkit provided to teams to accelerate their building of vision and language models. We summarize the approaches the participating teams took to overcome research challenges and extract key lessons learned. Finally, we provide analysis of the performance of the competing SimBots during the competition.
Substitution for Non-Wellfounded Syntax with Binders

We describe a generic construction of non-wellfounded syntax involving variable binding and its monadic substitution operation. Our construction of the syntax and its substitution takes place in category theory, notably by using monoidal categories and strong functors between them. A language is specified by a multi-sorted binding signature, say {\Sigma}. First, we provide sufficient criteria for {\Sigma} to generate a language of possibly infinite terms, through {\omega}-continuity. Second, we construct a monadic substitution operation for the language generated by {\Sigma}. A cornerstone in this construction is a mild generalization of the notion of heterogeneous substitution systems developed by Matthes and Uustalu; such a system encapsulates the necessary corecursion scheme for implementing substitution. The results are formalized in the Coq proof assistant, through the UniMath library of univalent mathematics.
Bounds on the rates of statistical divergences and mutual information

Statistical divergences are important tools in data analysis, information theory, and statistical physics, and there exist well known inequalities on their bounds. However, in many circumstances involving temporal evolution, one needs limitations on the rates of such quantities, instead. Here, several general upper bounds on the rates of some f-divergences are derived, valid for any type of stochastic dynamics (both Markovian and non-Markovian), in terms of information-like and/or thermodynamic observables. As special cases, the analytical bounds on the rate of mutual information are obtained, which may provide explicit and simple alternatives to the existing numerical algorithms for its estimation. The major role in all those limitations is played by temporal Fisher information, and some of them contain entropy production, suggesting a link with stochastic thermodynamics. Overall, the derived bounds can be applied to any complex network of interacting elements, where predictability of network dynamics is of prime concern.
FPGA Resource-aware Structured Pruning for Real-Time Neural Networks

Neural networks achieve state-of-the-art performance in image classification, speech recognition, scientific analysis and many more application areas. Due to the high computational complexity and memory footprint of neural networks, various compression techniques, such as pruning and quantization, have been proposed in literature. Pruning sparsifies a neural network, reducing the number of multiplications and memory. However, pruning often fails to capture properties of the underlying hardware, causing unstructured sparsity and load-balance inefficiency, thus bottlenecking resource improvements. We propose a hardware-centric formulation of pruning, by formulating it as a knapsack problem with resource-aware tensor structures. Evaluated on a range of tasks, including sub-microsecond particle classification at CERN’s Large Hadron Collider and fast image classification, the proposed method achieves reductions ranging between 55% and 92% in the DSP utilization and up to 81% in BRAM utilization.
KS-APR: Keyframe Selection for Robust Absolute Pose Regression

Markerless Mobile Augmented Reality (AR) aims to anchor digital content in the physical world without using specific 2D or 3D objects. Absolute Pose Regressors (APR) are end-to-end machine learning solutions that infer the device's pose from a single monocular image. Thanks to their low computation cost, they can be directly executed on the constrained hardware of mobile AR devices. However, APR methods tend to yield significant inaccuracies for input images that are too distant from the training set. This paper introduces KS-APR, a pipeline that assesses the reliability of an estimated pose with minimal overhead by combining the inference results of the APR and the prior images in the training set. Mobile AR systems tend to rely upon visual-inertial odometry to track the relative pose of the device during the experience. As such, KS-APR favours reliability over frequency, discarding unreliable poses. This pipeline can integrate most existing APR methods to improve accuracy by filtering unreliable images with their pose estimates. We implement the pipeline on three types of APR models on indoor and outdoor datasets. The median error on position and orientation is reduced for all models, and the proportion of large errors is minimized across datasets. Our method enables state-of-the-art APRs such as DFNetdm to outperform single-image and sequential APR methods. These results demonstrate the scalability and effectiveness of KS-APR for visual localization tasks that do not require one-shot decisions.
Protocols to measure the non-Abelian Berry phase by pumping a spin qubit through a quantum-dot loop

A quantum system constrained to a degenerate energy eigenspace can undergo a nontrival time evolution upon adiabatic driving, described by a non-Abelian Berry phase. This type of dynamics may provide logical gates in quantum computing that are robust against timing errors. A strong candidate to realize such holonomic quantum gates is an electron or hole spin qubit trapped in a spin-orbit-coupled semiconductor, whose twofold Kramers degeneracy is protected by time-reversal symmetry. Here, we propose and quantitatively analyze protocols to measure the non-Abelian Berry phase by pumping a spin qubit through a loop of quantum dots. One of these protocols allows to characterize the local internal Zeeman field directions in the dots of the loop. We expect a near-term realisation of these protocols, as all key elements have been already demonstrated in spin-qubit experiments. These experiments would be important to assess the potential of holonomic quantum gates for spin-based quantum information processing.
An Exploratory Study of V-Model in Building ML-Enabled Software: A Systems Engineering Perspective

Machine learning (ML) components are being added to more and more critical and impactful software systems, but the software development process of real-world production systems from prototyped ML models remains challenging with additional complexity and interdisciplinary collaboration challenges. This poses difficulties in using traditional software lifecycle models such as waterfall, spiral, or agile models when building \textit{ML-enabled systems}. In this research, we apply a Systems Engineering lens to investigate the use of V-Model in addressing the interdisciplinary collaboration challenges when building ML-enabled systems. By interviewing practitioners from software companies, we established a set of 8 propositions for using V-Model to manage interdisciplinary collaborations when building products with ML components. Based on the propositions, we found that despite requiring additional efforts, the characteristics of V-Model align effectively with several collaboration challenges encountered by practitioners when building ML-enabled systems. We recommend future research to investigate new process models that leverage the characteristics of V-Model such as the system decomposition, clear system boundary, and consistency of Validation \&Verification (V\&V) for building ML-enabled systems.
Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance

As data shift or new data become available, updating clinical machine learning models may be necessary to maintain or improve performance over time. However, updating a model can introduce compatibility issues when the behavior of the updated model does not align with user expectations, resulting in poor user-model team performance. Existing compatibility measures depend on model decision thresholds, limiting their applicability in settings where models are used to generate rankings based on estimated risk. To address this limitation, we propose a novel rank-based compatibility measure, $C^R$, and a new loss function that aims to optimize discriminative performance while encouraging good compatibility. Applied to a case study in mortality risk stratification leveraging data from MIMIC, our approach yields more compatible models while maintaining discriminative performance compared to existing model selection techniques, with an increase in $C^R$ of $0.019$ ($95\%$ confidence interval: $0.005$, $0.035$). This work provides new tools to analyze and update risk stratification models used in clinical care.
LETTER Galactic Bulge Preferred Over Dark Matter for the Galactic Center Gamma-Ray Excess

An anomalous gamma-ray excess emission has been found in Fermi Large Area Telescope data 1 covering the centre of the Galaxy 2,3 . Several theories have been proposed for this ‘Galactic Centre Excess’. They include self-annihilation of dark matter particles 4 , an unresolved population of millisecond pulsars 5 , an unresolved population of young pulsars 6 , or a series of burst events 7 . Here we report on a new analysis that exploits hydrodynamical modelling to register the position of interstellar gas associated with diffuse Galactic gamma-ray emission. We ﬁnd evidence that the Galactic Centre Excess gamma rays are statistically better described by the stellar over-density in the Galactic bulge and the nuclear stellar bulge, rather than a spherical excess. Given its non-spherical nature, we argue that the Galactic Centre Excess is not a dark matter phenomenon but rather associated with the stellar population of the Galactic bulge and nuclear bulge.
Nonlinear dynamics of femtosecond laser interaction with the central nervous system in zebrafish

We report on the photodamage mechanism underlying the highly nonlinear interaction of femtosecond pulses at 1030 nm with the central nervous system tissue in living zebrafish (Danio rerio). Our study identifies two different cavitation regimes. The damage is confined and well-controlled at low repetition rates due to plasma-based ablation and sudden local temperature rise. At high repetition rates, the damage becomes collateral due to plasma-mediated photochemistry and is not influenced by the thermal accumulation of consecutive laser pulses. Furthermore, we investigate and quantify the role of fluorescence labels with linear and nonlinear absorption pathways in optical breakdown. To verify our findings, we examined cell death and cellular responses to tissue damage, including the recruitment of fibroblasts and immune cells at different time points post-irradiation. These findings contribute to advancing the emerging nonlinear optical microscopy techniques and provide a strategy for inducing deep-tissue, precise, and localized injuries using near-infrared femtosecond laser pulses
Micro-compartmentalized strand displacement reactions with a random pool background

Toehold-mediated strand displacement (TMSD) is a widely used process in dynamic DNA nanotechnology, which has been applied for the actuation of molecular devices, in biosensor applications, and for DNA-based molecular computation. Similar processes also occur in a biological context, when RNA strands invade secondary structures or duplexes of other RNA or DNA molecules. Complex reaction environments—inside cells or synthetic cells—potentially contain a large number of competing nucleic acid molecules that transiently bind to the components of the strand displacement reaction of interest and thus slow down its kinetics. Here, we investigate the kinetics of TMSD reactions compartmentalized into water-in-oil emulsion droplets—in both the presence and absence of a random sequence background—using a droplet microfluidic ‘stopped flow’ set-up. The set-up enables one to determine the kinetics within thousands of droplets and easily vary experimental parameters such as the stoichiometry of the TMSD components. While the average kinetics in the droplets coincides precisely with the bulk behaviour, we observe considerable variability among the droplets. This variability is partially explained by the encapsulation procedure itself, but appears to be more pronounced in reactions involving a random pool background.
Deep Hashing Neural Networks for Hyperspectral Image Feature Extraction

Recently, deep learning has been recognized as a powerful tool to extract hierarchical features of hyperspectral images (HSIs). The existing deep learning-based methods exploit label information of land classes as the supervised information to train deep networks. However, considering that HSIs exhibit very complex spectral–spatial characteristic, e.g., the large intraclass variations and small interclass variations, these semantic information (i.e., label information)-based deep networks may not effectively cope with the above problem. In this letter, we propose a novel deep model, named deep hashing neural network (DHNN), to learn similarity-preserving deep features (SPDFs) for HSI classification. First, a well-pretrained network is introduced to simultaneously extract features of a pair of input samples. Second, a novel hashing layer is inserted after the last fully connected layer to transfer the real-value features into binary features, which can significantly speed up the computation for feature distance. Then, a loss function is elaborately designed to minimize the feature distance of similar pairs and maximize the feature distance of dissimilar pairs in Hamming space. Finally, the SPDF extracted by propagating the samples through the trained DHNN are fed into a support vector machine (SVM) classifier for HSI classification. Experimental results on two real HSIs demonstrate that the proposed feature extraction method in conjunction with a linear SVM classifier outperforms other feature extraction methods and competitive classifiers.
Deep neural network for MIMO-SCMA detection

This article introduces deep learning into the multiple-input multiple-output (MIMO) sparse code multiple access (SCMA) system and proposes a MIMO-SCMA detection scheme based on deep neural networks (DNN) to improve bit error rate (BER) performance. The DNN learns the codebook of each user through channel feature learning on different transmission antennas. The fully connected DNN is designed as the decoder at the receiving end, which does not require traditional multi-antenna detection and multi-user detection, and can obtain user data with one decoding operation. The encoder and decoder are trained using an end-to-end training method. All learning models of the DNN are generated offline and the learned models are used for online testing. In this model, the received signal and channel coefficients are set as input data, and the label corresponding to the transmitted symbol is set as output data for offline learning. After offline learning is completed, the model can be deployed online with fixed weights and biases. Through simulation experiments, the proposed DNN encoder-decoder method can reduce the BER and computational complexity of the receiver in the MIMO-SCMA system.
Enhanced radar for object recognition based on GANs

Environmental sensing is an essential aspect of autonomous driving systems, with millimeter wave radar currently gaining attention in academic circles due to its unique physical properties that complement optical sensing techniques such as vision. Compared to cameras and LIDAR, millimeter wave radar is not limited by light and meteorological conditions, boasts high penetration capabilities, and can operate around the clock to identify objects. However, the larger wavelengths of millimeter wave signals present significant challenges such as sparse point clouds and multipath effects, resulting in lower accuracy in environmental sensing. To address this issue, this paper proposes a point cloud enhancement method based on a GAN-LSTM network that converts the sparse point cloud data into semantically informative RF images, thereby improving object recognition accuracy. The proposed method is evaluated on the CARRADA dataset, and the experimental results demonstrate an improvement in object classification accuracy by 7.86% compared to the current state-of-the-art methods. This approach can significantly enhance the accuracy of millimeter wave radar-based environmental sensing in autonomous driving systems, enabling safer and more reliable vehicle operation.
A wind vector prediction method based on LSTM algorithm

This paper proposes a wind vector prediction method based on long-short term memory neural network (LSTM). The correlation between wind speed and direction is analyzed from the perspective of feature engineering. The results show that they contain different feature information and can be used as input variables to train the model at the same time. On the other hand, the above analysis also provides a basis for selecting the time length of input variables. The wind vector is decomposed into two orthogonal one-dimensional variables of east-west and north-south wind speeds based on wind direction to prevent the complexity of the algorithm from being increased by multi-dimensional variables. The LSTM algorithm is used to train the prediction model for the wind speed in both directions, and finally the wind vector prediction data containing the wind speed and direction are restored. Without increasing the complexity of the algorithm, the information density contained in the model is increased. One month's second level data of a wind farm in Hebei and Gansu provinces are selected for verification. The results show that the proposed hybrid prediction algorithm can better capture the information about wind speed and direction, and the error range of wind speed and direction prediction is reduced to 1m/s and 5° respectively, with an accuracy rate of more than 90%