The rise of AI in telecommunications, from optimizing Radio Access Networks to managing user experience, has sharply increased data volumes and training demands. Telecom data is often noisy, high-dimensional, costly to store, process, and label. Despite Ai's critical role, standard workflows still assume all training samples contribute equally. On the other hand, next generation systems require AI models that are accurate, efficient, and sustainable.The paper questions the assumptions of equal importance by focusing on applying and analyzing the roles of individual samples in telecom training and assessing whether the proposed model optimizes computation and energy use. we perform sample-level gradient analysis across epochs to identify patterns of influence and redundancy in model learning. Based on this, we propose a sample importance framework thats electively prioritizes impactful data and reduces computation without compromising accuracy. Experiments on three real-world telecom datasets show that our method [reserves performance while reducing data needs and computational overhead while advancing the goals of sustainable AI in telecommunications.
Serial-parallel redundancy is a reliable way to ensure service and systems will be available in cloud computing. That method involves making copies of the same system or program, with only one remaining active. When an error occurs, the inactive copy can step in as a backup right away, this provides continuous performance and uninterrupted operation. This approach is called parallel redundancy, otherwise known as active-active redundancy, and its exceptional when it comes to strategy. It creates duplicates of a system or service that are all running at once. By doing this fault tolerance increases since if one copy fails, the workload can be distributed across any replica thats functioning properly. Reliability allocation depends on features in a system and the availability and fault tolerance you want from it. Serial redundancy or parallel redundancies can be applied to increase the dependability of systems and services. To demonstrate how well this concept works, we looked into fixed serial parallel reliability redundancy allocation issues followed by using an innovative hybrid optimization technique to find the best possible allocation for peak dependability. We then measured ou
Multimodal Large Language Models (MLLMs) have shown promising progress in understanding and analyzing video content. However, processing long videos remains a significant challenge constrained by LLM's context size. To address this limitation, we propose LongVU, a spatiotemporal adaptive compression mechanism thats reduces the number of video tokens while preserving visual details of long videos. Our idea is based on leveraging cross-modal query and inter-frame dependencies to adaptively reduce temporal and spatial redundancy in videos. Specifically, we leverage DINOv2 features to remove redundant frames that exhibit high similarity. Then we utilize text-guided cross-modal query for selective frame feature reduction. Further, we perform spatial token reduction across frames based on their temporal dependencies. Our adaptive compression strategy effectively processes a large number of frames with little visual information loss within given context length. Our LongVU consistently surpass existing methods across a variety of video understanding benchmarks, especially on hour-long video understanding tasks such as VideoMME and MLVU. Given a light-weight LLM, our LongVU also scales effe
Because of the availability of larger datasets and recent improvements in the generative model, more realistic Deepfake videos are being produced each day. People consume around one billion hours of video on social media platforms every day, and thats why it is very important to stop the spread of fake videos as they can be damaging, dangerous, and malicious. There has been a significant improvement in the field of deepfake classification, but deepfake detection and inference have remained a difficult task. To solve this problem in this paper, we propose a novel DEEPFAKE C-L-I (Classification-Localization-Inference) in which we have explored the idea of accelerating Quantized Deepfake Detection Models using FPGAs due to their ability of maximum parallelism and energy efficiency compared to generalized GPUs. In this paper, we have used light MesoNet with EFF-YNet structure and accelerated it on VCK5000 FPGA, powered by state-of-the-art VC1902 Versal Architecture which uses AI, DSP, and Adaptable Engines for acceleration. We have benchmarked our inference speed with other state-of-the-art inference nodes, got 316.8 FPS on VCK5000 while maintaining 93\% Accuracy.
We present temperature dependent switching measurements of the Co/Ni multilayered free element of 75 nm diameter spin-valve nanopillars. Angular dependent hysteresis measurements as well as switching field measurements taken at low temperature are in agreement with a model of thermal activation over a perpendicular anisotropy barrier. However, the statistics of switching (mean switching field and switching variance) from 20 K up to 400 K are in disagreement with a Néel-Brown model that assumes a temperature independent barrier height and anisotropy field. We introduce a modified Néel-Brown model thats fit the experimental data in which we take a $T^{3/2}$ dependence to the barrier height and the anisotropy field due to the temperature dependent magnetization and anisotropy energy.
Governments across the globe are facing challenging times to generate more revenue because of the economic slowdown and to balance their budgets. There is a growing need to find new ways of revenue generation as spending cuts and austerity measures dont go well with most sections of the society, especially during difficult economic times. Internet Today is seen more as a necessary commodity and nobody can deny the fact that the Internet has improved peoples life in an unprecedented way than any other technology in the past.The Internet as a commodity is taxed in many countries, but the Internet usage or the transactions carried out are something thats not taxed.
Generally, in any human field, a Smarandache Structure on a set A means a weak structure W on A such that there exists a proper subset B which is embedded with a stronger structure S. By proper subset one understands a set included in A, different from the empty set, from the unit element if any, and from A. These types of structures occur in our everyday's life, thats why we study them in this book. As an example: A non-empty set L is said to form a loop, if on L is defined a binary operation called product, denoted by '.' such that: for all a, b belonging to L we have a . b belonging to L (closure property); there exists an element e belonging to L such that a . e = e . a = a for all a belonging to L (e is the identity element of L); for every ordered pair (a, b) belonging to L x L there exists a unique pair (p, q) in L such that ap = b and qa = b. Whence: A Smarandache Loop (or S-Loop) is a loop L such that a proper subset M of L is a subgroup (with respect to the same induced operation).
The recognition of cursive script is regarded as a subtle task in optical character recognition due to its varied representation. Every cursive script has different nature and associated challenges. As Urdu is one of cursive language that is derived from Arabic script, thats why it nearly shares the same challenges and difficulties even more harder. We can categorized Urdu and Arabic language on basis of its script they use. Urdu is mostly written in Nastaliq style whereas, Arabic follows Naskh style of writing. This paper presents new and comprehensive Urdu handwritten offline database name Urdu-Nastaliq Handwritten Dataset (UNHD). Currently, there is no standard and comprehensive Urdu handwritten dataset available publicly for researchers. The acquired dataset covers commonly used ligatures that were written by 500 writers with their natural handwriting on A4 size paper. We performed experiments using recurrent neural networks and reported a significant accuracy for handwritten Urdu character recognition.
The pandemic threat of COVID-19 with more than 37 million cases in which about 5 percent entering critical stage characterized by cytokine storm and hyperinflammatory condition, the state more often leads to admission to intensive care unit with rapid mortality. Janus kinase enzymes of Jak-1, Jak-2, Jak-3, and Tyk2 seem to be good targets for inhibition by medications to control cytokine storm in this context. In the present work, the inhibitory properties of different analgesic drugs on these targets are studied to assess their ability for clinical application from different points of view. Our docking results indicated that naproxen, methadone, and amitriptyline considering their higher binding energy, lower energy variance, and higher hydrophobicity, seem to express more inhibitory effects on Janus kinase enzymes than thats for approved inhibitors i.e. baricitinib and ruxolitinib. Accordingly, we suggest our wide list of candidate drugs including indomethacin, etodolac, buprenorphine, rofecoxib, duloxetine, valdecoxib, naproxen, methadone, and amitriptilin for clinical assessments for their usefulness in COVID-19 treatment, especially taking into account that up to now, there is
Generally, in any human field, a Smarandache Structure on a set A means a weak structure W on A such that there exists a proper subset B which is embedded with a stronger structure S. By proper subset one understands a set included in A, different from the empty set, from the unit element if any, and from A. These types of structures occur in our everyday's life thats why we study them in this book. Thus, as two particular cases: A Smarandache Ring of level I (S-ring I) is a ring R that contains a proper subset that is a field with respect to the operations induced. A Smarandache Ring of level II (S-ring II) is a ring R that contains a proper subset A that verifies: A is an additive abelian group; A is a semigroup under multiplication, for a, b belonging to A, a . b = 0 if and only if a = 0 or b = 0.
In the field of software engineering there are many new archetypes are introducing day to day Improve the efficiency and effectiveness of software development. Due to dynamic environment organizations are frequently exchanging their software constraint to meet their objectives. The propose research is a new approach by integrating the traditional V model and agile methodology to combining the strength of these models while minimizing their individual weakness.The fluctuating requirements of emerging a carried software system and accumulative cost of operational software are imposing researchers and experts to determine innovative and superior means for emerging software application at slight business or at enterprise level are viewing for. Agile methodology has its own benefits but there are deficiency several of the features of traditional software development methodologies that are essential for success. Thats why an embedded approach will be the right answer for software industry rather than a pure agile approach. This research shows how agile embedded traditional can play a vital role in development of software. A survey conducted to find the impact of this approach in industry
IPTEK and IMTAQ should be followed by knowledge of the ability in reading the hijaiyah letters as Al Qur-an base. Current people are so busy with their activities thats way authors develop this mobile application using pocket pc. The development of this research using waterfall model. Authors use the programming language of Microsoft Visual BASIC.Net. Authors also use Photoshop to prepare the image of every letter. In Indonesia, there six level in reading Al Qur-an, but for the purpose of thi research authors only use Iqro-1 until Iqro-4. This mobile application also enriched with the voice for every letter image.
Scientists are grappling with a cosmic mystery: why does the Universe behave differently on massive scales compared to our own solar system。 While distant galaxies reveal clear signs of something bending the rules of gravity—often attributed to dark energy or a hidden “fifth force”—everything nearby seems to follow Einstein’s playbook perfectly
"You're a man who needs to control his fate。 But you cannot control this
Researchers have, for the first time, directly visualized how electronic patterns known as charge density waves evolve across a phase transition。 Using cutting-edge microscopy, they found these patterns form unevenly, breaking into patches influenced by tiny structural distortions。 Unexpectedly, small pockets of order persist even above the transit
A mysterious magnetic material once thought to host an exotic “quantum spin liquid” has turned out to be something entirely different—and possibly just as intriguing。 Scientists studying cerium magnesium hexalluminate found it showed the hallmark signs of this elusive quantum state, like a lack of magnetic order and a spread of energy states。 But a
It is shown that in many-electron systems quantum transfer amplitudes and thus transfer probabilities may be strongly influenced by fast fluctuating fields, in particular, caused by simultaneous electron transfers. Corresponding mutual interplay of many electron jumps, arising at the fundamental level of quantum phases, results in long-correlated (1/f type) conductance fluctuations. However, thats could not be theoretically catched if neglect the real discreteness of quantum energy spectra and use the continuous spectrum approximation when building kinetic theory. Basing on first principles, the estimates of low-frequency fluctuations of tunneling conductance are presented.
Onshore wind development in the United States is being brought to a standstill