Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing the Quality of the Stego Image Using Quads-based Differences in Spatial Domain Images

In contemporary times, ensuring the security of shared information is a paramount concern to prevent unauthorized interception during communication over public networks. One approach to address this challenge is implementing data hiding (DH) techniques. However, the concealment of confidential data within multimedia files, such as images, often results in a degradation of visual quality. This decline in quality can raise suspicions among unauthorized users regarding the potential presence of concealed data. Throughout the years, researchers have devised various DH algorithms to mitigate the issue of significant image distortion following the data embedding process. Nevertheless, achieving a high-quality stego image, which carries secret data, remains a formidable task. This paper introduces a novel DH method grounded in the difference expansion (DE) paradigm, aiming to enhance the quality of the stego image for a given payload size. Experimental results presented herein demonstrate an improvement in visual quality, albeit with the observation that augmenting the embedding capacity introduces additional noise.
Metal Strip Layer Approach to Enhance the DC Characteristics and Analog/RF Performance of a Dual Material Control Gate TFET

In this paper, a novel approach to enhance the electrical characteristics of the dual material control gate tunnel field effect transistor (DMCG-TFET) by introducing metal strips (referred to as MS) into the oxide layer at the source-channel (S-C) region are presented. This modification has led to the creation of a new device called the dual material control gate metal-strip tunnel FET (DMCG-MS-TFET). Furthermore, we have incorporated an optimization step in our work to select a suitable work function for the metal strip. The simulation results using Silvaco ATLAS software have demonstrated significant improvements in the ON-state current $\left(I_{O N}=\mathbf{1. 8 6} \times \mathbf{1 0}^{-6}\right.$ A), average subthreshold slope $\left(S S_{A v g}\right.$) of approximately 56.5 $\mathrm{mV} /$ decade, switching ratio $\left(I_{O N} / I_{O F F}\right)$ of $4.73 \times 10^{10}$, suppression of ambipolar current, transconductance value of $\left(g_{m}=\right.$ $1.04 \times 10^{-5} \mathrm{~S}$), and improvements in analog/RF figure of merit (FOMs). The simulation results show that the proposed device has a lot of potential for use in future high-frequency and analog circuits, especially when low gate voltages are needed for low-power and radio-frequency circuits.
Revealing Compiler Heuristics Through Automated Discovery and Optimization

Tuning compiler heuristics and parameters is well known to improve optimization outcomes dramatically. Prior works have tuned command line flags and a few expert identified heuristics. However, there are an unknown number of heuristics buried, unmarked and unexposed inside the compiler as a consequence of decades of development without auto-tuning being foremost in the minds of developers. Many may not even have been considered heuristics by the developers who wrote them. The result is that auto-tuning search and machine learning can optimize only a tiny fraction of what could be possible if all heuristics were available to tune. Manually discovering all of these heuristics hidden among millions of lines of code and exposing them to auto-tuning tools is a Herculean task that is simply not practical. What is needed is a method of automatically finding these heuristics to extract every last drop of potential optimization. In this work, we propose Heureka, a framework that automatically identifies potential heuristics in the compiler that are highly profitable optimization targets and then automatically finds available tuning parameters for those heuristics with minimal human involvement. Our work is based on the following key insight: When modifying the output of a heuristic within an acceptable value range, the calling code using that output will still function correctly and produce semantically correct results. Building on that, we automatically manipulate the output of potential heuristic code in the compiler and decide using a Differential Testing approach if we found a heuristic or not. During output manipulation, we also explore acceptable value ranges of the targeted code. Heuristics identified in this way can then be tuned to optimize an objective function. We used Heureka to search for heuristics among eight thousand functions from the LLVM optimization passes, which is about 2% of all available functions. We then use identified heuristics to tune the compilation of 38 applications from the NAS and Polybench benchmark suites. Compared to an -ozbaseline we reduce binary sizes by up to 11.6% considering single heuristics only and up to 19.5% when stacking the effects of multiple identified tuning targets and applying a random search with minimal search effort. Generalizing from existing analysis results, Heureka needs, on average, a little under an hour on a single machine to identify relevant heuristic targets for a previously unseen application.
SpotCrack: Leveraging a Lightweight Framework for Crack Segmentation in Infrastructure

In this work, we introduce a novel methodology, “SpotCrack” designed to precisely segment road surfaces and infrastructure cracks. We employ lightweight modules to effectively identify various types of potential cracks in the infrastructure. The effectiveness of this approach is systematically evaluated on a diverse set of crack images, thereby substantiating its capacity to enhance operational efficiency within road and infrastructure management tasks. Notably, SpotCrack achieves real-time processing at an impressive rate of 130 frames per second (FPS) with an approximate inference time of 8 milliseconds, enabling its versatile applications in various real-world scenarios. This accelerated processing capability positions SpotCrack for diverse deployments, including integration with Unmanned Aerial Vehicles (UAVs) and road maintenance fleets. Significantly, SpotCrack applicability extends to individual mobility, as its high FPS empowers its integration within private vehicles, marking a transformative step in road safety, benefiting both self-driving and conventional vehicles alike. These findings underscore SpotCrack pivotal role in delivering precise crack segmentation, driving advancements in road management practices, and reinforcing road safety endeavors.
Achievable Second-Order Asymptotics for Additive Non-Gaussian MAC and RAC

We derive a second-order achievability bound for the two-user multiple access channel with additive non-Gaussian noise. In our setting, both users use spherical codebooks and the decoder uses the nearest neighbor decoding. Our result generalizes the dispersion analysis for point-to-point mismatched channel coding by Scarlett, Tan and Durisi (TIT 2017) to the multiple user setting. When specialized to the Gaussian noise, our proof provides an alternative second-order achievability analysis of MolavianJazi and Laneman (TIT, 2015). Furthermore, we generalize our results to the random access channel with additive non-Gaussian noise, where the number of active users in each time slot are unknown, and derive a second-order achievability bound. When specialized to the Gaussian noise, our second-order asymptotic results are consistent with the Gaussian noise case recently derived by Yavas, Kostina and Effros (TIT, 2021).
Air pollution and vegetation: ICP Vegetation Annual Report 2015/2016

The International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops (ICP Vegetation) was established in 1987. It is led by the UK and has its Programme Coordination Centre at the Centre for Ecology & Hydrology (CEH) in Bangor. It is one of seven ICPs and Task Forces that report to the Working Group on Effects (WGE) of the Convention on Long-range Transboundary Air Pollution (LRTAP Convention) on the effects of atmospheric pollutants on different components of the environment (e.g. forests, fresh waters, materials) and health in Europe and North America. Today, the ICP Vegetation comprises an enthusiastic group of scientists from 50 countries, including scientists from outside the UNECE region. An overview of contributions to the WGE workplan and other research activities in the year 2015/16 is provided in this report.
Joint Power and Spectrum Allocation in Wireless Localization Networks

—Network localization is a key feature in many wireless services and applications. In typical range-based localization techniques, “agents” try to perform position estimation through ranging with respect to “anchors” with known positions. Based on the deﬁnition of squared positional error bound (SPEB), the localization accuracy can be determined by the transmit power, carrier frequency and signal bandwidth, etc. This paper analyzes the joint power and spectrum allocation (JPSA) optimization problems in resource restricted wireless localization systems. We ﬁrst formulate both optimal interference-free and interference-concerned JPSA problems. We then formulate the robust counter-parts of the problems in the presence of uncertainty of the agents’ positions. Since all JPSA problems are non-convex, we show that they can be modeled and solved as geometric programming (GP) by proper approximations. Numeric results validate our analysis and show that the developed algorithms are able to ﬁnd solutions close to the global optimum in the investigated cases. We can ﬁnd the optimal/robust resource deployment in wireless localization networks based on the proposed frameworks.
Opinion A Neural Model of Mind Wandering

The role of the default-mode network (DMN) in the emergence of mind wandering and task-unrelated thought has been studied extensively. In parallel work, mind wandering has been associated with neuromodulation via the locus coeruleus (LC) norepinephrine (LC-NE) system. Here we propose a neural model that links the two systems in an integrative framework. The model attempts to explain how dynamic changes in brain systems give rise to the subjective experience of mind wandering. The model implies a neural and conceptual distinction between an off-focus state and an active mind-wandering state and provides a potential neural grounding for well-known cognitive theories of mind wandering. Finally, the proposed neural model of mind wandering generates precise, testable predictions at neural and behavioral levels
Source Characterization of Picoseismic Events During Hydraulic Stimulation at Hectometer Scale

From 2021 to 2023, we performed 17 hydraulic stimulations at hectometer scale in a fully controlled environment in the Bedretto tunnel. The Bedretto tunnel is a new research facility below the Swiss Alps with an overburden of 1 km. For the experiments, a multi-packer system was installed in a 400 m long stimulation borehole, dividing it into 14 intervals. Additionally, a densely spaced multi-component geophysical monitoring system was installed in six surrounding monitoring boreholes. Information on in-situ stress conditions were collected prior to the stimulations from all boreholes. A first analysis of the data showed that the dense network allowed us to resolve the seismic event locations well beyond point clouds. The seismic event locations reveal clear alignments which reflect the activation of the faults in the rock volume in space and time. In addition to the highly resolved event locations, we are able to estimate well constrained moment tensors for the picoseismic events. The moment tensors align with the known fractures and reveal differences in the mechanism between faults within short distances (<10 m).
Constraints on the abundance of lensed supergiant stars in the Spock galaxy at redshift 1

In this work, we present a constraint on the abundance of supergiant (SG) stars at redshift z ≈ 1, based on recent observations of a strongly lensed arc at this redshift. First we derived a free-form model of MACS J0416.1-2403 using data from the Beyond Ultra-deep Frontier Fields and Legacy Observations (BUFFALO) program. The new lens model is based on 72 multiply lensed galaxies that produce 214 multiple images, making it the largest sample of spectroscopically conﬁrmed lensed galaxies on this cluster. The larger coverage in BUFFALO allowed us to measure the shear up to the outskirts of the cluster, and extend the range of lensing constraints up to ∼ 1Mpc from the central region, providing a mass estimate up to this radius. As an application, we make predictions for the number of high-redshift multiply lensed galaxies detected in future observations with the James Webb Space Telescope (JWST). Then we focus on a previously known lensed galaxy at z = 1 . 0054, nicknamed Spock, which contains four previously reported transients. We interpret these transients as microcaustic crossings of SG stars and explain how we computed the probability of such events. Based on simpliﬁcations regarding the stellar evolution, we ﬁnd that microlensing (by stars in the intracluster medium) of SG stars at z = 1 . 0054 can fully explain these events. The inferred abundance of SG stars is consistent with either (1) a number density of stars with bolometric luminosities beyond the Humphreys-Davidson (HD) limit ( L max ≈ 6 × 10 5 L (cid:12) for red stars), which is below ∼ 400starskpc − 2 , or (2) the absence of stars beyond the HD limit but with a SG number density of ∼ 9000kpc − 2 for stars with luminosities between 10 5 L (cid:12) and 6 × 10 5 L (cid:12) . This is equivalent to one SG star per 10 × 10pc 2 . Finally, we make predictions for future observations with JWST’s NIRcam. We ﬁnd that in observations made with the F 200 W ﬁlter that reach 29mag AB, if cool red SG stars exist at z ≈ 1 beyond the HD limit, they should be easily detected in this arc.
Ocular vestibular evoked myogenic potentials in patients with Ocular vestibular evoked myogenic potentials in patients with single sided deafness single sided deafness

Introduction : Clinically unilateral severe-to-profound sensorineural hearing loss is known as single-sided deafness (SSD). It is de ﬁ ned by severe-to-profound hearing thresholds with a poor word recognition ability. SSD is not un-commonly encountered in otology, its symptoms are hearing loss and often associated with the vestibular system. In SSD, otolithic dysfunction may be frequently misdiagnosed and the majority of patients were affected by cochlear and superior vestibular nerve and then inferior vestibular nerve. Otolith damage may be present in patients with SSD because it shares a membranous structure, in addition, it has similar receptor cells. The vestibule and cochlea may be impacted by the same detrimental elements from an anatomical perspective. Objective : Here, we assess the prevalence of vestibular dysfunction by measuring ocular vestibular-evoked myogenic potential (oVEMP), cervical vestibular-evoked myogenic potential (cVEMP), and caloric ﬁ ndings in patients with SSD. Patients and methods : This study had two groups of participants: group A, which consisted of 20 patients with normal hearing sensitivity on both sides, and group B, which consisted of 20 participants with SSD. Two groups, each with an age range of 20 e 60 years, were free of vestibular symptoms. Results : The results showed a statistically signi ﬁ cant difference in oVEMP between SSD and control group as increase in latencies of P1 (P10) and N1 (N15) and decrease in amplitude. Conclusion : oVEMPs are able to identify vestibular signs in patients with SSD who are asymptomatic and have never complained of vestibular impairment, oVEMPs are able to identify vestibular signs in these patients, and oVEMP test is the most sensitive test and caloric test is most speci ﬁ c for identifying important vestibular signs in these patients.
Vascular access for hemodialysis: current perspectives

A well-functioning vascular access (VA) is a mainstay to perform an efficient hemodialysis (HD) procedure. There are three main types of access: native arteriovenous fistula (AVF), arteriovenous graft, and central venous catheter (CVC). AVF, described by Brescia and Cimino, remains the first choice for chronic HD. It is the best access for longevity and has the lowest association with morbidity and mortality, and for this reason AVF use is strongly recommended by guidelines from different countries. Once autogenous options have been exhausted, prosthetic fistulae become the second option of maintenance HD access alternatives. CVCs have become an important adjunct in maintaining patients on HD. The preferable locations for insertion are the internal jugular and femoral veins. The subclavian vein is considered the third choice because of the high risk of thrombosis. Complications associated with CVC insertion range from 5% to 19%. Since an increasing number of patients have implanted pacemakers and defibrillators, usually inserted via the subclavian vein and superior vena cava into the right heart, a careful assessment of risk and benefits should be taken. Infection is responsible for the removal of about 30%–60% of HD CVCs, and hospitalization rates are higher among patients with CVCs than among AVF ones. Proper VA maintenance requires integration of different professionals to create a VA team. This team should include a nephrologist, radiologist, vascular surgeon, infectious disease consultant, and members of the dialysis staff. They should provide their experience in order to give the best options to uremic patients and the best care for their VA.
Integrating Planned Behavior and Technology Acceptance Models: Study of GoFood after COVID-19

- One of the most apparent changes due to COVID-19 pandemic was the increase in the usage of online food delivery services. An in-depth analysis was needed to find the fundamental aspects behind this change that make online food delivery services an integral part of people’s lifestyles even after the pandemic. The research aimed to explore the determinants of customers’ intention to use GoFood (IU) by integrating the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) to construct the research model. The research novelty was reflected through convenience motivation, which was crucial in enhancing the intention to use GoFood. This was achieved by exploring the interaction between the TAM and TPB. The underlying factors behind customers’ intention to use GoFood were defined by price-saving orientation (PSO) and time-saving orientation (TSO) with a mediating variable called convenience motivation (CM). Afterward, 252 valid questionnaires from respondents who live in Surabaya and Sidoarjo were collected using the snowball sampling method and analyzed using the SEM-PLS method. The result reveals that PSO is the most significant factor behind the intention to use (IU). Meanwhile, TSO only indirectly affects IU, which means it will only be impactful when mediated by CM. Moreover, CM itself has a significant impact on IU.
Nephroprotective and Anti-Diabetic Potential of Beta vulgaris L. Root (Beetroot) Methanolic Extract in a Rat Model of Type 2 Diabetes Mellitus

Background and Objectives: Diabetes mellitus is a chronic metabolic disease associated with several complications, including that of kidney disease. Plant-based dietary products have shown promise in mitigating these effects to improve kidney function and prevent tissue damage. This study assessed the possible favorable effects of beetroot extract (BE) in improving kidney function and preventing tissue damage in diabetic rats. Materials and Methods: Type 2 diabetes mellitus (T2DM) was induced using a low dose of streptozotocin (STZ). Both control and rats with pre-established T2DM were divided into six groups (each consisting of eight rats). All treatments were given by gavage and continued for 12 weeks. Fasting blood glucose levels, serum fasting insulin levels, Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), serum triglycerides, cholesterol, low-density lipoprotein-cholesterol, serum and urinary albumin, and creatinine and urea levels were measured. Apart from this, glutathione, malondialdehyde, superoxide dismutase, tumor necrosis factor-α, and interleukine-6 in the kidney homogenates of all groups of rats were measured, and the histopathological evaluation of the kidney was also performed. Results: It was observed that treatment with BE increased body weight significantly (p ≤ 0.05) to be similar to that of control groups. Fasting glucose, insulin, HOMA-IR levels, and lipid profile in the plasma of the pre-established T2DM rats groups decreased to p ≤ 0.05 in the BE-treated rats as the BE concentration increased. Treatment with BE also improved the renal levels of oxidative stress and inflammatory markers, urinary albumin, and serum creatinine and urea levels. Unlike all other groups, only the kidney tissues of the T2DM + BE (500 mg/kg) rats group showed normal kidney tissue structure, which appears to be similar to those found in the kidney tissues of the control rats groups. Conclusion: we found that streptozotocin administration disturbed markers of kidney dysfunction. However, Beta vulgaris L. root extract reversed these changes through antioxidant, anti-inflammatory, and antiapoptotic mechanisms.
ADEPNET: A Dynamic-Precision Efficient Posit Multiplier for Neural Networks

The posit number system aims to be a drop-in replacement of the existing IEEE floating-point standard. Its properties- tapered precision and high dynamic range, allow a smaller size posit to almost match the performance of a much larger size floating-point in representing decimals. This becomes especially useful for performing error-tolerant tasks like deep learning inference computation where low latency and area are a priority. Recent research has found that the performance of deep neural network models saturates beyond a certain level of accuracy of multipliers used for convolutions. Therefore, the extra hardware cost of developing precise arithmetic circuits for such applications becomes an unnecessary overhead. This paper explores approximate posit multipliers in the convolutional layers of deep neural networks and attempts to find an ideal balance between hardware utilization and inference accuracy. Posit multiplication involves several steps, with the mantissa multiplication step utilizing maximum hardware resources. To mitigate this, a posit multiplier circuit using an approximate hybrid-radix Booth encoding for mantissa multiplication and techniques such as truncation and bit masking based on input regime size are proposed. In addition, a novel Booth encoding control scheme to prevent unnecessary bits from switching has been devised to reduce dynamic power dissipation. Compared to existing literature, these optimizations have contributed to a 23% decrease in power dissipation in the mantissa multiplication stage. Further, a novel area and energy-efficient decoder architecture have also been developed with an 11% reduction in dynamic power dissipation and area compared to existing decoders. Overall, the proposed < 16, 2 > posit multiplier offers a 14% reduction in the PDP over the existing approximate posit multiplier designs. The proposed < 16, 2 > multiplier also achieves over 90% accuracy in inferencing deep learning models such as ResNet20, VGG-19 and DenseNet.