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In this work, we consider a static wormhole in Bopp-Podolsky electrodynamics and convert it into its rotating counterpart by reducing it into Morris-Thorne form. We further study the null geodesics and effective potential along with the shadows for inner and outer unstable orbits for specific choices of parameters. It is found that for some cases smooth shadow curves are formed and for a few cases, the shadows formed are cuspy. All parameters have a significant impact on the shadows except for the parameter $b$ when either $a$ or $Q$ are kept small. We also analyze the gravitational lensing in the strong regime, considering that the observer and the source are on opposite sides of the throat. For this situation, we explore in detail the behavior of the deflection angle, Einstein rings and lensing observables.

Large language models (LLMs) have become increasingly prevalent in our daily lives, leading to an expectation for LLMs to be trustworthy -- - both accurate and well-calibrated (the prediction confidence should align with its ground truth correctness likelihood). Nowadays, fine-tuning has become the most popular method for adapting a model to practical usage by significantly increasing accuracy on downstream tasks. Despite the great accuracy it achieves, we found fine-tuning is still far away from satisfactory trustworthiness due to "tuning-induced mis-calibration". In this paper, we delve deeply into why and how mis-calibration exists in fine-tuned models, and how distillation can alleviate the issue. Then we further propose a brand new method named Efficient Trustworthy Distillation (FIRST), which utilizes a small portion of teacher's knowledge to obtain a reliable language model in a cost-efficient way. Specifically, we identify the "concentrated knowledge" phenomenon during distillation, which can significantly reduce the computational burden. Then we apply a "trustworthy maximization" process to optimize the utilization of this small portion of concentrated knowledge before transferring it to the student. Experimental results demonstrate the effectiveness of our method, where better accuracy (+2.3%) and less mis-calibration (-10%) are achieved on average across both in-domain and out-of-domain scenarios, indicating better trustworthiness., Comment: EMNLP 2024

Modern applications are end-to-end encrypted to prevent data from being read or secretly modified. 5G tech nology provides ubiquitous access to these applications without compromising the application-specific performance and latency goals. In this paper, we empirically demonstrate that 5G radio communication becomes the side channel to precisely infer the user's applications in real-time. The key idea lies in observing the 5G physical and MAC layer interactions over time that reveal the application's behavior. The MAC layer receives the data from the application and requests the network to assign the radio resource blocks. The network assigns the radio resources as per application requirements, such as priority, Quality of Service (QoS) needs, amount of data to be transmitted, and buffer size. The adversary can passively observe the radio resources to fingerprint the applications. We empirically demonstrate this attack by considering four different categories of applications: online shopping, voice/video conferencing, video streaming, and Over-The-Top (OTT) media platforms. Finally, we have also demonstrated that an attacker can differentiate various types of applications in real-time within each category., Comment: 9 pages, 8 figures, 2 tables. This paper has been accepted for publication by the 21st IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS 2024)

Aging and chronic conditions affect older adults' daily lives, making early detection of developing health issues crucial. Weakness, common in many conditions, alters physical movements and daily activities subtly. However, detecting such changes can be challenging due to their subtle and gradual nature. To address this, we employ a non-intrusive camera sensor to monitor individuals' daily sitting and relaxing activities for signs of weakness. We simulate weakness in healthy subjects by having them perform physical exercise and observing the behavioral changes in their daily activities before and after workouts. The proposed system captures fine-grained features related to body motion, inactivity, and environmental context in real-time while prioritizing privacy. A Bayesian Network is used to model the relationships between features, activities, and health conditions. We aim to identify specific features and activities that indicate such changes and determine the most suitable time scale for observing the change. Results show 0.97 accuracy in distinguishing simulated weakness at the daily level. Fine-grained behavioral features, including non-dominant upper body motion speed and scale, and inactivity distribution, along with a 300-second window, are found most effective. However, individual-specific models are recommended as no universal set of optimal features and activities was identified across all participants.

The visible universe is filled with different types of plasma media in the form of stars, nebulas and other forms of excited gases. These matter fields have a high influence on the gravity and are likely to be present around the black holes due to the immense gravitational attraction. Since a plasma medium affects the speed of light, therefore we investigated the null geodesics and various optical features around the rotating black hole in Kalb-Ramond gravity immersed in plasma medium. Various plasma distributions are considered to develop a comparative study for their influence on unstable null circular orbits, shadows and evaporation rate of the black hole in the presence of a plasma medium. Moreover, the shadow results are also compared with Event Horizon Telescope data for M78* and Sgr A* in order to estimate the parametric bounds for which the rotating black hole in Kalb-Ramond gravity is considered either M87* or Sgr A* under the different values of plasma parameters. From this analysis, we also found the distribution of plasma that has a significant impact on the above mentioned features and is most likely to be present around M87* and Sgr A*., Comment: 30 pages, 14 figures

We study the optical properties of spacetime around a novel regular black hole (BH) in general relativity (GR) coupled to nonlinear electrodynamics (NED), which is asymptotically flat. First, we study the angular velocity and Lyapunov exponent in unstable photon circular orbits in the novel spherically symmetric BH spacetime. Later, the rotating regular BH solution is obtained using the Newmann-Janis algorithm, and the event horizon properties of the BH are determined. We analyze the effective potential for the circular motion of photons in the spacetime of the novel rotating BH. Also, we analyze the photon sphere around the novel BH and its shadow using celestial coordinates. We obtain that an increase of the BH spin and charge as well as NED field nonlinearity parameters causes an increase in the distortion parameter of the BH shadow, while, the area of the shadow and its oblateness decrease. Moreover, we also obtain the constraint values for the BH charge and the nonlinearity parameters using Event Horizon Telescope data from shadow sizes of supermassive BHs Sgr A* and M87*. Finally, the emission rate of BH evaporation through Hawking radiation is also studied.

This article deals with the study of some properties of the static and rotating black holes in Kalb-Ramond gravity in four dimensional spacetime. First, we discuss the action of the corresponding theory and the static black hole metric. Then we investigate the light sphere for the static black hole by using the Hamiltonian formalism and the corresponding linear radius of the shadow, angular velocity and Lyapunov exponent. For the rotating black hole, we discuss the horizon structure. Moreover, we study the effective potential to discuss the structure of null sphere and unstable circular null orbits around the rotating black hole. The properties such as energy emission rate and distortion are calculated and analyzed by using the numerical data for the shadows calculated by appropriately chosen parametric values for two different angular locations of the observer off the equatorial plane. We also obtain the constraints on the black hole parameters by comparing the shadow sizes of the black hole in Kalb-Ramond gravity and the supermassive black holes M87* and Sgr A*. Finally, we investigate the effect of mass, energy, angular momentum and the black hole parameters on the center of mass energy of two colliding particles that are accelerated in the vicinity of the black hole., Comment: 29 pages, 15 figures

Software systems are increasingly relying on deep learning components, due to their remarkable capability of identifying complex data patterns and powering intelligent behaviour. A core enabler of this change in software development is the availability of easy-to-use deep learning libraries. Libraries like PyTorch and TensorFlow empower a large variety of intelligent systems, offering a multitude of algorithms and configuration options, applicable to numerous domains of systems. However, bugs in those popular deep learning libraries also may have dire consequences for the quality of systems they enable; thus, it is important to understand how bugs are identified and fixed in those libraries. Inspired by a study of Jia et al., which investigates the bug identification and fixing process at TensorFlow, we characterize bugs in the PyTorch library, a very popular deep learning framework. We investigate the causes and symptoms of bugs identified during PyTorch's development, and assess their locality within the project, and extract patterns of bug fixes. Our results highlight that PyTorch bugs are more like traditional software projects bugs, than related to deep learning characteristics. Finally, we also compare our results with the study on TensorFlow, highlighting similarities and differences across the bug identification and fixing process.