Advancing state of the art in face recognition and bridging the gap between laboratory and real-world scenarios require the availability of challenging databases. One of the challenging applications of face recognition is surveillance, where unconstrained video data is captured both in day and night time (visible and near infrared spectrum). These videos have multiple subjects in each frame, which are matched with good quality gallery images. Due to the lack of an existing database for such a cross spectral cross resolution video-to-still face recognition application, this is still an open research problem. This paper presents a video database that can be utilized to benchmark face recognition algorithms addressing cross spectral cross resolution matching. The proposed Cross-Spectral Cross-Resolution Video dataset (CSCRV) contains videos pertaining to 160 subjects with an open-set protocol. We present baseline results with two commercial matchers for two experimental scenarios, where we observe very low performance of both the matchers. It is our assertion that this dataset can help researchers develop robust face recognition algorithms to handle real world surveillance scenarios.
Garbage collection is one of the most hazardous jobs in our country, and workers should avoid direct contact with hazardous trash.This paper is devoted to the design of a garbage collection vehicle that can be operated automatically.The creation of cutting-edge deep learning-based data processing technologies in recent years has sped up this rise.Furthermore, major automakers produce cars capable of partially or completely autonomous driving on public roadways.Contrarily, self-driving vehicles are now only permitted on multi-lane highways, such as interstates, and are not yet ready for urban areas or residential complexes.Because the autonomous garbage collection vehicle is battery-powered, the quantity of pollution it emits is insignificant in nature.
High-level synthesis (HLS) tools dramatically reduce the nonrecurring engineering cost of creating specialized hardware accelerators. Existing HLS tools are successful in synthesizing efficient accelerators for program kernels with regular memory accesses and simple control flows. For other programs, however, these tools yield poor performance because they invoke computation units for instructions sequentially, without exploiting parallelism. To address this problem, this paper proposes Coarse-Grained Pipelined Accelerators (CGPA), an HLS framework that utilizes coarsegrained pipeline parallelism techniques to synthesize efficient specialized accelerator modules from irregular C/C++ programs without requiring any annotations. Compared to the sequential method, CGPA shows speedups of 3.0x--3.8x for 5 kernels from programs in different domains.
The effects of COVID-19 infection persist beyond the active phase. Comprehensive description and analysis of the post COVID sequelae in various population groups are critical to minimise the long-term morbidity and mortality associated with COVID-19. This analysis was conducted with an objective to estimate the frequency of post COVID sequelae and subsequently, design a framework for holistic management of post COVID morbidities.Follow-up data collected as part of a registry-based observational study in 31 hospitals across India since September 2020-October 2022 were used for analysis. All consenting hospitalised patients with COVID-19 are telephonically followed up for up to 1 year post-discharge, using a prestructured form focused on symptom reporting.Dyspnoea, fatigue and mental health issues were reported among 18.6%, 10.5% and 9.3% of the 8042 participants at first follow-up of 30-60 days post-discharge, respectively, which reduced to 11.9%, 6.6% and 9%, respectively, at 1-year follow-up in 2192 participants. Patients who died within 90 days post-discharge were significantly older (adjusted OR (aOR): 1.02, 95% CI: 1.01, 1.03), with at least one comorbidity (aOR: 1.76, 95% CI: 1.31, 2.35), and a higher proportion had required intensive care unit admission during the initial hospitalisation due to COVID-19 (aOR: 1.49, 95% CI: 1.08, 2.06) and were discharged at WHO ordinal scale 6-7 (aOR: 49.13 95% CI: 25.43, 94.92). Anti-SARS-CoV-2 vaccination (at least one dose) was protective against such post-discharge mortality (aOR: 0.19, 95% CI: 0.01, 0.03).Hospitalised patients with COVID-19 experience a variety of long-term sequelae after discharge from hospitals which persists although in reduced proportions until 12 months post-discharge. Developing a holistic management framework with engagement of care outreach workers as well as teleconsultation is a way forward in effective management of post COVID morbidities as well as reducing mortality.
Sequential programming models express a total program order, of which a partial order must be respected. This inhibits parallelizing tools from extracting scalable performance. Programmer written semantic commutativity assertions provide a natural way of relaxing this partial order, thereby exposing parallelism implicitly in a program. Existing implicit parallel programming models based on semantic commutativity either require additional programming extensions, or have limited expressiveness. This paper presents a generalized semantic commutativity based programming extension, called Commutative Set (COMMSET), and associated compiler technology that enables multiple forms of parallelism. COMMSET expressions are syntactically succinct and enable the programmer to specify commutativity relations between groups of arbitrary structured code blocks. Using only this construct, serializing constraints that inhibit parallelization can be relaxed, independent of any particular parallelization strategy or concurrency control mechanism. COMMSET enables well performing parallelizations in cases where they were inapplicable or non-performing before. By extending eight sequential programs with only 8 annotations per program on average, COMMSET and the associated compiler technology produced a geomean speedup of 5.7x on eight cores compared to 1.5x for the best non-COMMSET parallelization.
Summary Reservoir crudes often contain acidic components (primarily naphthenic acids), which undergo neutralization to form soaps in the presence of alkali. The generated soaps perform synergistically with injected synthetic surfactants to mobilize waterflood residual oil in what is termed alkali/surfactant/polymer (ASP) flooding. The two main advantages of using alkali in enhanced oil recovery (EOR) are to lower cost by injecting a lesser amount of expensive synthetic surfactant and to reduce adsorption of the surfactant on the mineral surfaces. The addition of alkali, however, complicates the measurement and prediction of the microemulsion phase behavior that forms with acidic crudes. For a robust chemical-flood design, a comprehensive understanding of the microemulsion phase behavior in such processes is critical. Chemical-flooding simulators currently use Hand's method to fit a limited amount of measured data, but that approach likely does not adequately predict the phase behavior outside the range of the measured data. In this paper, we present a novel and practical alternative. In this paper, we extend a dimensionless equation of state (EOS) (Ghosh and Johns 2016b) to model ASP phase behavior for potential use in reservoir simulators. We use an empirical equation to calculate the acid-distribution coefficient from the molecular structure of the soap. Key phase-behavior parameters such as optimum salinities and optimum solubilization ratios are calculated from soap-mole-fraction-weighted equations. The model is tuned to data from phase-behavior experiments with real crudes to demonstrate the procedure. We also examine the ability of the new model to predict fish plots and activity charts that show the evolution of the three-phase region. The predictions of the model are in good agreement with measured data.
Transient faults are emerging as a critical reliability concern in modern microprocessors. Redundant hardware solutions are commonly deployed to detect transient faults, but they are less flexible and cost-effective than software solutions. However, software solutions are rendered impractical because of high performance overheads. To address this problem, this paper presents Runtime Asynchronous Fault Tolerance via Speculation (RAFT), the fastest transient fault detection technique known to date. Serving as a layer between the application and the underlying platform, RAFT automatically generates two symmetric program instances from a program binary. It detects transient faults in a non-invasive way and exploits high-confidence value speculation to achieve low runtime overhead. Evaluation on a commodity multicore system demonstrates that RAFT delivers a geomean performance overhead of 2.83% on a set of 30 SPEC CPU benchmarks and STAMP benchmarks. Compared with existing transient fault detection techniques, RAFT exhibits the best performance and fault coverage, without requiring any change to the hardware or the software applications.
Alternating sequencing of styrene-maleimide/maleic anhydride (S-MI/MA) in the copolymer chain is known for a long time. But since early 2000, this class of copolymers has been extensively studied using various living/controlled polymerization techniques to design S-MI/MA alternating copolymers with tunable molecular weight, narrow dispersity (Ð), and precise chain-end functionality. The widespread diverse applications of this polymeric backbone are due to its ease of synthesis, cheap starting materials, high precision in alternating sequencing, and facile post-polymerization functionalization with simple organic reactions. Recently, S-MI/MA alternating copolymers have been rediscovered as novel polymers with unprecedented emissive behavior. It outperforms the traditional fluorophores with no aggregation caused quenching (ACQ), aqueous solubility, and greater cell viability. Herein, the origin of alternating sequence, synthesis, and recent (2010-Present) developments in applications of these polymers in different fields are elaborately discussed, including the advantages of the unconventional luminogenic property. This review article also highlights the future research directions of the versatile S-MI/MA copolymers.
Latent fingerprints are lifted from multiple types of surfaces, which vary in material type, texture, color, and shape. These differences in the surfaces introduce significant intra-class variations in the lifted prints such as availability of partial print, background noise, and poor ridge structure quality. Due to these observed variations, the overall quality and the matching performance of latent fingerprints vary with respect to surface properties. Thus, characterizing the performance of latent fingerprints according to the surfaces they are lifted from is an important research problem that needs attention. In this research, we create a novel multi-surface latent fingerprint database and make it publicly available for the research community. The database consists of 551 latent fingerprints from 51 subjects lifted from eight different surfaces. Using existing algorithms, we characterize the quality of latent fingerprints and compute the matching performance to analyze the effect of different surfaces.
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