The present work comprehensively reviews electron hydrodynamics in graphene, highlighting both experimental observations and theoretical developments. Key experimental signatures such as negative vicinity resistance, Poiseuille flow, and significant violation of the Wiedemann-Franz (WF) law have been discussed, with special emphasis on Lorenz ratio measurements. In the theoretical direction, recent efforts have focused on developing hydrodynamic frameworks for calculating the thermodynamic and transport coefficients of electrons in graphene. The present work has briefly addressed the theoretical framework adopted by our group.

We analyze neutrino oscillations from the perspective of quantum complementarity by extending the familiar wave-particle duality into a exact triality relation among visibility, predictability, and entanglement. Using a density-matrix approach, we construct explicit measures of these quantities in both two- and three-flavor frameworks and show that they satisfy an exact conservation-like identity throughout oscillation evolution. In this picture, visibility corresponds to interference strength, predictability to flavor imbalance, and entanglement to the reduction of purity due to flavor correlations. We demonstrate that neutrino oscillations naturally exhibit regimes of maximal entanglement, vanishing coherence, and entanglement monogamy constraining flavor correlations. Our results establish that long-baseline neutrino oscillations realize the triality relation in an energy-dependent way, with T2K probing vacuum-like coherence entanglement balance and DUNE revealing the decisive role of matter e ects in redistributing the quantum information budget.

Detecting deepfakes involving face-swaps presents a significant challenge, particularly in real-world scenarios where anyone can perform face-swapping with freely available tools and apps without any technical knowledge. Existing deepfake detection methods rely on facial landmarks or inconsistencies in pixel-level features and often struggle with face-swap deepfakes, where the source face is seamlessly blended into the target image or video. The prevalence of face-swap is evident in everyday life, where it is used to spread false information, damage reputations, manipulate political opinions, create non-consensual intimate deepfakes (NCID), and exploit children by enabling the creation of child sexual abuse material (CSAM). Even prominent public figures are not immune to its impact, with numerous deepfakes of them circulating widely across social media platforms. Another challenge faced by deepfake detection methods is the creation of datasets that encompass a wide range of variations, as training models require substantial amounts of data. This raises privacy concerns, particularly regarding the processing and storage of personal facial data, which could lead to unauthorized access or misuse. Our key idea is to identify these style discrepancies to detect face-swapped images effectively without accessing the real facial image. We perform comprehensive evaluations using multiple datasets and face-swapping methods, which showcases the effectiveness of SafeVision in detecting face-swap deepfakes across diverse scenarios. SafeVision offers a reliable and scalable solution for detecting face-swaps in a privacy preserving manner, making it particularly effective in challenging real-world applications. To the best of our knowledge, SafeVision is the first deepfake detection using style features while providing inherent privacy protection.

Deploying vertical bifacial PV modules can play a significant role in agrivoltaics, fencing walls, noise barriers, building integrated photovoltaics (BIPV), solar PV for electric vehicles, and many other applications. This research work presents the performance comparison of vertical bifacial photovoltaic (VBPV) modules facing East-West (E-W) and South-North (S-N) directions. Also, the VBPV modules are compared with vertical and tilted south-facing monofacial PV modules. Six PV modules (monofacial and bifacial) were installed at the rooftop of IIT Bhilai academic building, Raipur (21.16° N, 81.65° E), India, and studied for a year from May 2022 to April 2023. The results show that the E-W facing VBPV module gives two production peaks, one in the morning and another in the evening, as compared to the single notable rise at midday observed for a monofacial module. From a series of experiments, 19 days of data were collected over the one-year period from May 2022 to April 2023, with specific inclusion of important days like solstices and equinoxes. In addition, the energy generation results are compared with PVsyst simulations, while also addressing the limitations of the PVsyst simulation of vertical PV modules. E-W bifacial generation is higher than S-N bifacial and south-facing monofacial modules from February to April. The VBPV modules in E-W and S-N orientations present a promising opportunity for expanding the agrivoltaics sector in tropical and sub-tropical countries, like India. This has huge implications for addressing the sustainable development goals by simultaneously contributing to sustainable land management, green energy generation, energy security and water conservation in the vast geo-climatic expanse of tropics.

In the ever-evolving landscape of natural language processing and information retrieval, the need for robust and domain-specific entity linking algorithms has become increasingly apparent. It is crucial in a considerable number of fields such as humanities, technical writing and biomedical sciences to enrich texts with semantics and discover more knowledge. The use of Named Entity Disambiguation (NED) in such domains requires handling noisy texts, low resource settings and domain-specific KBs. Existing approaches are mostly inappropriate for such scenarios, as they either depend on training data or are not flexible enough to work with domain-specific KBs. Thus in this work, we present an unsupervised approach leveraging the concept of Group Steiner Trees (GST), which can identify the most relevant candidates for entity disambiguation using the contextual similarities across candidate entities for all the mentions present in a document. We outperform the state-of-the-art unsupervised methods by more than 40\% (in avg.) in terms of Precision@1 across various domain-specific datasets.

In this paper, we study collision-free graph exploration in an anonymous pot labeled network. Two identical mobile agents, starting from different nodes in $G$ have to explore the nodes of $G$ in such a way that for every node $v$ in $G$, at least one mobile agent visits $v$ and no two agents are in the same node in any round and stop. The agents know the size of the graph but do not know its topology. If an agent arrives in the one-hop neighborhood of the other agent, both agents can detect the presence of the other agent but have no idea at which neighboring node the other agent resides. The agents may wake up in different rounds An agent, after waking up, has no knowledge about the wake-up time of the other agent. We study the problem of collision-free exploration where some pebbles are placed by an Oracle at the nodes of the graph to assist the agents in achieving collision-free exploration. The Oracle knows the graph, the starting positions of the agents, and their wake-up schedule, and it places some pebbles that may be of different colors, at most one at each node. The number of different colors of the pebbles placed by the Oracle is called the {\it color index} of the corresponding pebble placement algorithm. The central question we study is as follows: "What is the minimum number $z$ such that there exists a collision-free exploration of a given graph with pebble placement of color index $z$?" For general graphs, we show that it is impossible to design an algorithm that achieves collision-free exploration with color index 1. We propose an exploration algorithm with color index 3. We also proposed a polynomial exploration algorithm for bipartite graphs with color index 2.

An intense transient magnetic field is produced in high energy heavy-ion collisions mostly due to the spectator protons inside the two colliding nucleus. The magnetic field introduces anisotropy in the medium and hence the isotropic scalar transport coefficients become anisotropic and split into multiple components. Here we calculate the anisotropic transport coefficients shear, bulk viscosity, electrical conductivity, and the thermal diffusion coefficients for a multicomponent Hadron- Resonance-Gas (HRG) model for a non-zero magnetic field by using the Boltzmann transport equation in a relaxation time approximation (RTA). The anisotropic transport coefficient component along the magnetic field remains unaffected by the magnetic field, while perpendicular dissipation is governed by the interplay of the collisional relaxation time and the magnetic time scale, which is inverse of the cyclotron frequency. We calculate the anisotropic transport coefficients as a function of temperature and magnetic field using the HRG model. The neutral hadrons are unaffected by the Lorentz force and do not contribute to the anisotropic transports, we estimate within the HRG model the relative contribution of isotropic and anisotropic transports as a function of magnetic field and temperature. We also give an estimation of these anisotropic transport coefficients for the hadronic gas at finite baryon chemical potential.

Advancements in digital technologies make it easy to modify the content of digital images. Hence, ensuring digital images integrity and authenticity is necessary to protect them against various attacks that manipulate them. We present a Deep Learning (DL) based dual invisible watermarking technique for performing source authentication, content authentication, and protecting digital content copyright of images sent over the internet. Beyond securing images, the proposed technique demonstrates robustness to content-preserving image manipulations. It is also impossible to imitate or overwrite watermarks because the cryptographic hash of the image and the dominant features of the image in the form of perceptual hash are used as watermarks. We highlighted the need for source authentication to safeguard image integrity and authenticity, along with identifying similar content for copyright protection. After exhaustive testing, we obtained a high peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), which implies there is a minute change in the original image after embedding our watermarks. Our trained model achieves high watermark extraction accuracy and to the best of our knowledge, this is the first deep learning-based dual watermarking technique proposed in the literature.

High-entropy oxides (HEOs) offer a unique platform for exploring the thermodynamic interaction between configurational entropy and enthalpy in stabilizing complex solid solutions. In this study, a series of rock-salt structured oxides with varying configurational entropy, ranging from binary to multi-cation systems, to elucidate the competing roles of enthalpy and entropy in phase stabilization is investigated. Compositions including (Ni$_{0.8}$Cu$_{0.2}$)O to(NiCuZnCoMg)$_{0.9}$A$_{0.1}$O (A = Li, Na, K) were synthesized and their stuctural, microstructural and thermal properties have been discussed. X-ray diffraction combined with thermal cycling confirms that even a medium configurational entropy ($\sim$ 0.95R) can induce single-phase behavior stabilized by configurational entropy ($\Delta S_{conf}$), challenging the traditional threshold of $1.5\,R$. High-resolution TEM and EDS mapping reveal nanocrytalline features and homogeneous elemental distribution respectively, while XPS analysis confirms divalent oxidation states. A strong coupling between high configurational entropy with thermal conductivity ($\kappa$) has been observed. First, a sharp decrease in $\kappa$ with increasing $\Delta S_{conf}$ is seen and then decomposed samples (while cooling) show high $\kappa$, demonstrating the role of $\Delta S_{conf}$ on $\kappa$. Furthermore, Li-doped compositions exhibit improved thermoelectric performance, with a maximum figure of merit ($zT$) of $\sim$0.15 at 1173K\, driven by low thermal conductivity and favorable carrier transport. The results highlight that configurational entropy, even at intermediate values, plays a significant role in stabilizing disordered single-phase oxides and tailoring phonon transport.

We propose a minimal extension of the Standard Model with right-handed neutrinos, governed by a non-holomorphic $A_{4}$ modular flavor symmetry. Within this model framework, the light neutrino masses are generated via the popular type-I seesaw mechanism in which the structure of the Dirac neutrino Yukawa couplings is decided by nonholomorphic modular forms. Unlike conventional flavor models with ad hoc flavon fields, the structure of Dirac and Majorana mass matrices is entirely determined by a modulus parameter $\tau$. We construct the predictive mass matrices for charged leptons, Dirac neutrinos, and right-handed Majorana neutrinos and show the compatibility with neutrino oscillation data by an appropriate choice of input model parameters. We find that our $\chi^2$ analysis of neutrino masses and mixing gives excellent agreement with current neutrino oscillation observables by taking normal hierarchical pattern of light neutrinos. We present numerical analysis of two sets of benchmark points explaining neutrino masses while generating the correct amount of baryon asymmetry via thermal leptogenesis. We estimate numerically the values of CP-asymmetry and examine the evolution of the lepton asymmetry by studying Boltzman equations by considering both strong and washout regimes with CP-asymmetry parameter in the range $|\varepsilon_{1}| \sim 10^{-4}$--$10^{-8}$. The model predicts an effective Majorana mass in the few meV range, below current experimental bounds but within reach of next-generation $0\nu\beta\beta$ searches. The key feature of non-holomorphic $A_4$ modular symmetry naturally accommodates non-zero neutrino masses and mixings, minimizes the Yukawa arbitrariness, and establishes a direct connection between high-scale leptogenesis with low-energy neutrino observable parameters, thereby the model provides a testable link between neutrino flavor physics and cosmology.

With the advent of new IEEE 802.11ax (WiFi 6) devices, enabling security is a priority. Since previous versions were found to have security vulnerabilities, to fix the most common security flaws, the WiFi Protected Access 3 (WPA3) got introduced. Although WPA3 is an improvement over its predecessor in terms of security, recently it was found that WPA3 has a few security vulnerabilities as well. In this paper, we have mentioned the previously known vulnerabilities in WPA3 and WPA2. In addition to that, we have created our own dataset based on WPA3 attacks (Section III). We have proposed a two-stage solution for the detection of an intrusion in the network. The two-stage approach will help ease computational processing burden of an AP and WLAN Controller. First, AP will perform a lightweight simple operation for some duration (say 500ms) at certain time interval. Upon discovering any abnormality in the flow of traffic an ML-based solution at the controller will detect the type of attack. Our approach is to utilize resources on AP as well as the back-end controller with certain level of optimization. We have achieved over 99% accuracy in attack detection using an ML-based solution. We have also publicly provided our code and dataset for the open-source research community, so that it can contribute for future research work.

Gender bias in artificial intelligence (AI) and natural language processing has garnered significant attention due to its potential impact on societal perceptions and biases. This research paper aims to analyze gender bias in Large Language Models (LLMs) with a focus on multiple comparisons between GPT-2 and GPT-3.5, some prominent language models, to better understand its implications. Through a comprehensive literature review, the study examines existing research on gender bias in AI language models and identifies gaps in the current knowledge. The methodology involves collecting and preprocessing data from GPT-2 and GPT-3.5, and employing in-depth quantitative analysis techniques to evaluate gender bias in the generated text. The findings shed light on gendered word associations, language usage, and biased narratives present in the outputs of these Large Language Models. The discussion explores the ethical implications of gender bias and its potential consequences on social perceptions and marginalized communities. Additionally, the paper presents strategies for reducing gender bias in LLMs, including algorithmic approaches and data augmentation techniques. The research highlights the importance of interdisciplinary collaborations and the role of sociological studies in mitigating gender bias in AI models. By addressing these issues, we can pave the way for more inclusive and unbiased AI systems that have a positive impact on society.

Numerous neutrino experiments have confirmed the phenomenon of neutrino oscillation, providing direct evidence of the quantum mechanical nature of neutrinos. In this work, we investigate the entanglement properties of neutrino flavor states within the framework of three-flavor neutrino oscillation using two major entanglement measures: entanglement of formation (EOF) and concurrence, utilizing the DUNE experimental setup. Our findings indicate that the maximally entangled state appears between $\nu_{\mu}$ and $\nu_{\tau}$ whereas, $\nu_{e}$ behaves as a nearly separable state. To further explore the nature of bipartite entanglement, we introduce the concept of the monogamy of entanglement, which allows us to investigate the distinction between genuine three-flavor entanglement and bipartite entanglement. Our analysis confirms that the three-flavor neutrino system forms a bipartite entanglement structure, adhering to the Coffman-Kundu-Wootters (CKW) inequality. Additionally, we implement a minimization procedure to find the best-fit values of the oscillation parameters that correspond to the concurrence minima at the two specific energy points where the concurrence reaches its lowest values. Using these best-fit values, we probe three fundamental unknowns in neutrino oscillation: CP violation sensitivity, neutrino mass hierarchy, and the octant issue of $\theta_{23}$, across two distinct energy points. Our results manifest that while the best-fit values obtained through concurrence minimization show slightly reduced sensitivity to CP violation compared to current best-fit values, they exhibit greater sensitivity to the mass hierarchy. Furthermore, the study reveals a maximal mixing angle for the atmospheric sector.

Deep learning techniques have gained a lot of traction in the field of NLP research. The aim of this paper is to predict the age and gender of an individual by inspecting their written text. We propose a supervised BERT-based classification technique in order to predict the age and gender of bloggers. The dataset used contains 681284 rows of data, with the information of the blogger's age, gender, and text of the blog written by them. We compare our algorithm to previous works in the same domain and achieve a better accuracy and F1 score. The accuracy reported for the prediction of age group was 84.2%, while the accuracy for the prediction of gender was 86.32%. This study relies on the raw capabilities of BERT to predict the classes of textual data efficiently. This paper shows promising capability in predicting the demographics of the author with high accuracy and can have wide applicability across multiple domains.

We consider a minimal renormalizable non-supersymmetric $E_6$ Grand Unified Theory using fundamental representation $27$ for fermions and scalars. The scalar with adjoint representation ${78}$ is also taken for direct breaking of $E_{6}$ to SM. The proposed model, guided by TeV-scale vector-like fermions and scalar leptoquark offer successful gauge unification even in the absence of any intermediate symmetry. Embedded with threshold corrections, it is shown to be compatible with the present experimental limit on proton decay lifetime. The unique feature of the model shows that, the GUT threshold corrections to the unification mass, is controlled by superheavy gauge bosons only, thereby minimising the uncertainty of the GUT predictions. The scalar leptoquark and vector-like fermions residing in $27$ representation can explain flavor physics anomalies like $R_{D^{(\ast)}}$ as reported by the LHCb collaboration and the muon anomalous magnetic moment reported by the recent muon $g-2$ experiment at Fermilab. The model can also predict a sub-eV scale neutrino at one-loop level via exchange of $W$ and $Z$ gauge bosons through MRIS mechanism.

Rapid technological advancements have tremendously increased the data acquisition capabilities of remote sensing satellites. However, the data utilization efficiency in satellite missions is very low. This growing data also escalates the cost required for data downlink transmission and post-processing. Selective data transmission based on in-orbit inferences will address these issues to a great extent. Therefore, to decrease the cost of the satellite mission, we propose a novel system design for selective data transmission, based on in-orbit inferences. As the resolution of images plays a critical role in making precise inferences, we also include in-orbit super-resolution (SR) in the system design. We introduce a new image reconstruction technique and a unique loss function to enable the execution of the SR model on low-power devices suitable for satellite environments. We present a residual dense non-local attention network (RDNLA) that provides enhanced super-resolution outputs to improve the SR performance. SR experiments on Kaguya digital ortho maps (DOMs) demonstrate that the proposed SR algorithm outperforms the residual dense network (RDN) in terms of PSNR and block-sensitive PSNR by a margin of +0.1 dB and +0.19 dB, respectively. The proposed SR system consumes 48% less memory and 67% less peak instantaneous power than the standard SR model, RDN, making it more suitable for execution on a low-powered device platform.

We have studied the collisional time and relaxation time of a QGP(Quark-Gluon Plasma) by parameterizing them by temperature. From this parameterization we have obtained the decay rate parameterized by temperature which further helps us to calculate and compare the shear viscosity to entropy density ratio of a QGP with the KSS(Kovtun-Son-Starinets) result.

We propose a minimal Type-I Dirac seesaw which accommodates a thermal scalar dark matter (DM) candidate protected by a charge conjugation symmetry in dark sector $C_{\rm dark}$, without introducing any additional field beyond the ones taking part in the seesaw. A $Z_4$ symmetry is introduced to realise the tree level Dirac seesaw while the Majorana mass terms are prevented by an unbroken global lepton number symmetry. While the spontaneous $Z_4$ breaking together with electroweak symmetry breaking lead to the generation of light Dirac neutrino mass, it also results in the formation of domain walls. These cosmologically catastrophic walls can be made to annihilate away by introducing bias terms while also generating stochastic gravitational waves (GW) within reach of near future experiments like \texttt{LISA}, \texttt{BBO}, $\mu$-\texttt{ARES} etc. The scalar DM parameter space can be probed at direct and indirect search experiments. Light Dirac neutrinos also enhance the relativistic degrees of freedom $N_{\rm eff}$ within reach of future cosmic microwave background (CMB) experiments. The model can also explain the observed baryon asymmetry via Dirac leptogenesis.

The second Hot QCD Matter 2024 conference at IIT Mandi focused on various ongoing topics in high-energy heavy-ion collisions, encompassing theoretical and experimental perspectives. This proceedings volume includes 19 contributions that collectively explore diverse aspects of the bulk properties of hot QCD matter. The topics encompass the dynamics of electromagnetic fields, transport properties, hadronic matter, spin hydrodynamics, and the role of conserved charges in high-energy environments. These studies significantly enhance our understanding of the complex dynamics of hot QCD matter, the quark-gluon plasma (QGP) formed in high-energy nuclear collisions. Advances in theoretical frameworks, including hydrodynamics, spin dynamics, and fluctuation studies, aim to improve theoretical calculations and refine our knowledge of the thermodynamic properties of strongly interacting matter. Experimental efforts, such as those conducted by the ALICE and STAR collaborations, play a vital role in validating these theoretical predictions and deepening our insight into the QCD phase diagram, collectivity in small systems, and the early-stage behavior of strongly interacting matter. Combining theoretical models with experimental observations offers a comprehensive understanding of the extreme conditions encountered in relativistic heavy-ion and proton-proton collisions.

The geometrical representation of two-flavor neutrino oscillation represents the neutrino's flavor eigenstate as a magnetic moment-like vector that evolves around a magnetic field-like vector that depicts the Hamiltonian of the system. In the present work, we demonstrate the geometrical interpretation of neutrino in a vacuum in the presence of decay, which transforms this circular trajectory of neutrino into a helical track that effectively makes the neutrino system mimic a classical damped driven oscillator. We show that in the absence of the phase factor $\xi$ in the decay Hamiltonian, the neutrino exactly behaves like the system of nuclear magnetic resonance(NMR); however, the inclusion of the phase part introduces a $CP$ violation, which makes the system deviate from NMR. Finally, we make a qualitative discussion on under-damped, critically-damped, and over-damped scenarios geometrically by three different diagrams. In the end, we make a comparative study of geometrical picturization in vacuum, matter, and decay, which extrapolates the understanding of the geometrical representation of neutrino oscillation in a more straightforward way.