In this paper, we introduce a new benchmark dataset named IPN Hand with sufficient size, variety, and real-world elements able to train and evaluate deep neural networks. This dataset contains more than 4,000 gesture samples and 800,000 RGB frames from 50 distinct subjects. We design 13 different static and dynamic gestures focused on interaction with touchless screens. We especially consider the scenario when continuous gestures are performed without transition states, and when subjects perform natural movements with their hands as non-gesture actions. Gestures were collected from about 30 diverse scenes, with real-world variation in background and illumination. With our dataset, the performance of three 3D-CNN models is evaluated on the tasks of isolated and continuous real-time HGR. Furthermore, we analyze the possibility of increasing the recognition accuracy by adding multiple modalities derived from RGB frames, i.e., optical flow and semantic segmentation, while keeping the real-time performance of the 3D-CNN model. Our empirical study also provides a comparison with the publicly available nvGesture (NVIDIA) dataset. The experimental results show that the state-of-the-art ResNext-101 model decreases about 30% accuracy when using our real-world dataset, demonstrating that the IPN Hand dataset can be used as a benchmark, and may help the community to step forward in the continuous HGR. Our dataset and pre-trained models used in the evaluation are publicly available at this https URL.

Cosmic rays with energies up to a few PeV are known to be accelerated within the Milky Way. Traditionally, it has been presumed that supernova remnants were the main source of very-high-energy cosmic rays but theoretically it is difficult to get protons to PeV energies and observationally there simply is no evidence to support the remnants as sources of hadrons with energies above a few tens of TeV. One possible source of protons with those energies is the Galactic Center region. Here we report observations of 1-100 TeV gamma rays coming from the 'Cygnus Cocoon', which is a superbubble surrounding a region of OB2 massive star formation. These gamma rays are likely produced by 10-1000 TeV freshly accelerated CRs originating from the enclosed star forming region Cygnus OB2. Hitherto it was not known that such regions could accelerate particles to these energies. The measured flux is likely originated by hadronic interactions. The spectral shape and the emission profile of the Cocoon changes from GeV to TeV energies, which reveals the transport of cosmic particles and historical activity in the superbubble.

We present experimental results of a homopolar disc dynamo constructed at CICATA-Querétaro in Mexico. The device consists of a flat, multi-arm spiral coil which is placed above a fast-spinning metal disc and connected to the latter by sliding liquid-metal electrical contacts. Theoretically, self-excitation of the magnetic field is expected at the critical magnetic Reynolds number Rm~45, which corresponds to a critical rotation rate of about 10 Hz. We measured the magnetic field above the disc and the voltage drop on the coil for the rotation rate up to 14 Hz, at which the liquid metal started to leak from the outer sliding contact. Instead of the steady magnetic field predicted by the theory we detected a strongly fluctuating magnetic field with a strength comparable to that of Earth's magnetic field which was accompanied by similar voltage fluctuations in the coil. These fluctuations seem to be caused by the intermittent electrical contact through the liquid metal. The experimental results suggest that the dynamo with the actual electrical resistance of liquid metal contacts could be excited at the rotation rate of around 21 Hz provided that the leakage of liquid metal is prevented.

Quantum Fisher Information (QFI) can be used to quantify how sensitive a quantum state reacts to changes in its variational parameters, making it a natural diagnostic for algorithms such as the Quantum Approximate Optimization Algorithm (QAOA). We perform a systematic QFI analysis of QAOA for Max-Cut on cyclic and complete graphs with $N = 4 - 10$ qubits. Two mixer families are studied, RX-only and hybrid RX-RY, with depths $p = 2, 4, 6$ and $p = 3, 6, 9$, respectively, and with up to three entanglement stages implemented through cyclic- or complete-entangling patterns. Complete graphs consistently yield larger QFI eigenvalues than cyclic graphs; none of the settings reaches the Heisenberg limit ($4N^2$), but several exceed the linear bound ($4N$). Introducing entanglement primarily redistributes QFI from diagonal to off-diagonal entries: non-entangled circuits maximize per-parameter (diagonal) sensitivity, whereas entangling layers increase the covariance fraction and thus cross-parameter correlations, with diminishing returns beyond the first stage. Leveraging these observations, we propose, as a proof of concept, a QFI-Informed Mutation (QIm) heuristic that sets mutation probabilities and step sizes from the normalized diagonal QFI. On 7- and 10-qubit instances, QIm attains higher mean energies and lower variance than equal-probability and random-restart baselines over 100 runs, underscoring QFI as a lightweight, problem-aware preconditioner for QAOA and other variational quantum algorithms.

Machine translation (MT) is one of the main tasks in natural language processing whose objective is to translate texts automatically from one natural language to another. Nowadays, using deep neural networks for MT tasks has received great attention. These networks require lots of data to learn abstract representations of the input and store it in continuous vectors. This paper presents the first relatively large-scale Amharic-English parallel sentence dataset. Using these compiled data, we build bi-directional Amharic-English translation models by fine-tuning the existing Facebook M2M100 pre-trained model achieving a BLEU score of 37.79 in Amharic-English 32.74 in English-Amharic translation. Additionally, we explore the effects of Amharic homophone normalization on the machine translation task. The results show that the normalization of Amharic homophone characters increases the performance of Amharic-English machine translation in both directions.

We apply quantum model inspired on the classical Bayesian method also called mutual information to study the multipartite correlation in quantum images by using the flexible representation of quantum images (FRQI). This can be reflected by considering von Neumann entropy. The results are compared between two images of size $2\times 2$ and $8\times 8$ from different classical and quantum methods. We find that the classical joint entropy is invariant under transformation of change of color but the quantum entropy is sensitive to this change. It is shown that the total correlation $I_T$ could arrive to the double amount of the classical joint entropy.

The use of carbon nanotube (CNT) field-effect transistors (FETs) in microwave circuit design requires an appropriate, immediate and efficient description of their performance. This work describes a technique to extract the parameters of an electrical equivalent circuit for CNTFETs. The equivalent circuit is used to model the dynamic and noise performance at low- and high-frequency of different CNTFET technologies, considering extrinsic and intrinsic device parameters as well as the contact resistance. The estimation of the contact resistance at the metal/CNTs interfaces is obtained from a Y-function based extraction method. The noise model includes four noise sources: thermal noise, thermal channel noise, shot channel noise and flicker noise. The proposed model is compared with a compact model calibrated to hysteresis-free experimental data from a high-frequency multi-tube (MT)-CNTFET technology. Additionally, it has been applied to experimental data from another fabricated MT-CNTFET technology. The comparison in both cases shows a good agreement between reference data (simulation and experimental) and results from the proposed model. Low- and high-frequency noise projections of the fabricated reference device are further studied. Noise results from both studied technologies show that shot noise mainly contributes to the total noise due to the presence of Schottky barriers at contacts and along the channel.

We compile baselines, along with dataset split, for multimodal sentiment analysis. In this paper, we explore three different deep-learning based architectures for multimodal sentiment classification, each improving upon the previous. Further, we evaluate these architectures with multiple datasets with fixed train/test partition. We also discuss some major issues, frequently ignored in multimodal sentiment analysis research, e.g., role of speaker-exclusive models, importance of different modalities, and generalizability. This framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field.

The history of computing in Mexico can not be thought without the name of Prof. Harold V. McIntosh (1929-2015). For almost 50 years, in Mexico he contributed to the development of computer science with wide international recognition. Approximately in 1964, McIntosh began working in the Physics Department of the Advanced Studies Center (CIEA) of the National Polytechnic Institute (IPN), now called CINVESTAV. In 1965, at the National Center of Calculus (CeNaC), he was a founding member of the Master in Computing, first in Latin America. With the support of Mario Baez Camargo and Enrique Melrose, McIntosh continues his research of Martin-Baltimore Computer Center and University of Florida at IBM 709.

This work focuses on the study of the spectral problem for Dirac materials immersed in position-dependent magnetic and electric fields. To achieve this, the system of differential equations satisfied by the eigenfunction components of the Hamiltonian has been decoupled, and the solutions for some specific cases have been analyzed using Heun functions, which provide us with a quantization relation and allow us to determine the solutions for the bound states.

The High Altitude Water Cherenkov (HAWC) observatory is an array of large water Cherenkov detectors sensitive to gamma rays and hadronic cosmic rays in the energy band between 100 GeV and 100 TeV. The observatory will be used to measure high-energy protons and cosmic rays via detection of the energetic secondary particles reaching the ground when one of these particles interacts in the atmosphere above the detector. HAWC is under construction at a site 4100 meters above sea level on the northern slope of the volcano Sierra Negra, which is located in central Mexico at 19 degrees N latitude. It is scheduled for completion in 2014. In this paper we estimate the sensitivity of the HAWC instrument to point-like and extended sources of gamma rays. The source fluxes are modeled using both unbroken power laws and power laws with exponential cutoffs. HAWC, in one year, is sensitive to point sources with integral power-law spectra as low as 5x10^-13 cm^-2 sec^-1 above 2 TeV (approximately 50 mCrab) over 5 sr of the sky. This is a conservative estimate based on simple event parameters and is expected to improve as the data analysis techniques are refined. We discuss known TeV sources and the scientific contributions that HAWC can make to our understanding of particle acceleration in these sources.

In this paper, we study the Schrödinger equation with Dunkl derivative for a free particle confined in a cylindrical potential well. We consider both the finite and infinite height cases. The Dunkl formalism introduces reflection operators that modify the structure of the Hamiltonian and affect the parity of the solutions. By working in cylindrical coordinates, we obtain exact analytical expressions for the radial and axial wavefunctions in terms of Bessel functions. The energy spectrum and the solutions are classified according to the eigenvalues of the reflection operators in the three coordinates. We analyze in detail the conditions under which the wavefunctions acquire definite parity and discuss the resulting constraints on the Dunkl parameters.

Carbon nanoonions are novel carbon nanoestructures that have potential applications in fields like electronics and chemical catalysis. Here we report a very simple but effective method of purifying carbon nanoonions produced by submerged arc discharge in water based on the water-toluene liquid-liquid extraction. Purified and non-purified samples were characterized by atomic force microscopy, high resolution transmission electronic microscopy and Brunauer-Emmett-Teller gas adsorption isotherms method. Microscopy results showed a good purification and allowed the assessment of the particles diameter distribution. Specific surface area was measured showing a great increment from (14.7 +- 0.3) m2/g for the non-purified sample to (170 +- 3) m2/g for the purified sample. Average particles diameter was also assessed from the adsorption isotherms; the diameter values obtained by the three techniques were in good agreement being between 20 to 30 nm.

The Chern-Simons theory defined on a 3-dimensional manifold with boundary is written as a two-dimensional field theory defined only on the boundary of the three-manifold. The resulting theory is, essentially, the pullback to the boundary of a symplectic structure defined on the space of auxiliary fields in terms of which the connection one-form of the Chern-Simons theory is expressed when solving the condition of vanishing curvature. The counting of the physical degrees of freedom living in the boundary associated to the model is performed using Dirac's canonical analysis for the particular case of the gauge group SU(2). The result is that the specific model has one physical local degree of freedom. Moreover, the role of the boundary conditions on the original Chern- Simons theory is displayed and clarified in an example, which shows how the gauge content as well as the structure of the constraints of the induced boundary theory is affected.

Complexity has been a recurrent research topic in cellular automata because they represent systems where complex behaviors emerge from simple local interactions. A significant amount of previous research has been conducted proposing instances of complex cellular automata; however, most of the proposed methods are based on a careful search or a meticulous construction of evolution rules. This paper presents the emergence of complex behaviors based on reversible cellular automata. In particular, this paper shows that reversible cellular automata represent an adequate framework to obtain complex behaviors adding only new random states. Experimental results show that complexity can be obtained from reversible cellular automata appending a proportion of about two times more states at random than the original number of states in the reversible automaton. Thus, it is possible to obtain complex cellular automata with dozens of states. Complexity appears to be commonly obtained from reversible cellular automata, and using other operations such as permutations of states or row and column permutations in the evolution rule. The relevance of this paper is to present that reversibility can be a useful structure to implement complex behaviors in cellular automata.

Extended gamma-ray emission around isolated pulsars at TeV energies, also known as TeV halos, have been found around a handful of middle-aged pulsars. The halos are significantly more extended than their pulsar wind nebulae but much smaller than the particle diffusion length in the interstellar medium. The origin of TeV halos is unknown. Interpretations invoke either local effects related to the environment of a pulsar or generic particle transport behaviors. The latter scenario predicts that TeV halos would be a universal phenomena for all pulsars. We searched for extended gamma-ray emission around 36 isolated middle-aged pulsars identified by radio and gamma-ray facilities using 2321 days of data from the High-Altitude Water Cherenkov (HAWC) Observatory. Through a stacking analysis comparing TeV flux models against a background-only hypothesis, we identified TeV halo-like emission at a significance level of $5.10\,\sigma$. Our results imply that extended TeV gamma-ray halos may commonly exist around middle-aged pulsars. This reveals a previously unknown feature about pulsars and opens a new window to identify the pulsar population that is invisible to radio, x-ray, and GeV gamma-ray observations due to magnetospheric configurations.

The emergence of complex behaviors in cellular automata is an area that has been widely developed in recent years with the intention to generate and analyze automata that produce space-moving patterns or gliders that interact in a periodic background. Frequently, this type of automata has been found through either an exhaustive search or a meticulous construction of the evolution rule. In this study, the specification of cellular automata with complex behaviors was obtained by utilizing randomly generated specimens. In particular, it proposed that a cellular automaton of $n$ states should be specified at random and then extended to another automaton with a higher number of states so that the original automaton operates as a periodic background where the additional states serve to define the gliders. Moreover, this study presented an explanation of this method. Furthermore, the random way of defining complex cellular automata was studied by using mean-field approximations for various states and local entropy measures. This specification was refined with a genetic algorithm to obtain specimens with a higher degree of complexity. With this methodology, it was possible to generate complex automata with hundreds of states, demonstrating that randomly defined local interactions with multiple states can construct complexity.

In recent years, Islamophobia has gained significant traction across Western societies, fueled by the rise of digital communication networks. This paper performs a large-scale analysis of specialized, semi-coded Islamophobic terms such as (muzrat, pislam, mudslime, mohammedan, muzzies) floated on extremist social platforms, i.e., 4Chan, Gab, Telegram, etc. Many of these terms appear lexically neutral or ambiguous outside of specific contexts, making them difficult for both human moderators and automated systems to reliably identify as hate speech. First, we use Large Language Models (LLMs) to show their ability to understand these terms. Second, Google Perspective API suggests that Islamophobic posts tend to receive higher toxicity scores than other categories of hate speech like Antisemitism. Finally, we use BERT topic modeling approach to extract different topics and Islamophobic discourse on these social platforms. Our findings indicate that LLMs understand these Out-Of-Vocabulary (OOV) slurs; however, further improvements in moderation strategies and algorithmic detection are necessary to address such discourse effectively. Our topic modeling also indicates that Islamophobic text is found across various political, conspiratorial, and far-right movements and is particularly directed against Muslim immigrants. Taken altogether, we performed one of the first studies on Islamophobic semi-coded terms and shed a global light on Islamophobia.