Abstract

Crowdfunding is an efficient method for raising funds for projects. When used for non-excludable public projects, the process is termed Civic Crowdfunding (CC) and is an active research area. Researchers have analyzed CC in game-theoretic settings assuming that agents are interested in (and contribute to) a single public project. Gen- eralizing the existing single project theory to determine agents’ equilibrium contributions for multiple projects is non-trivial – espe- cially with budget-constrained agents. This work hypothesizes that the agents can learn their equilibrium contributions with repeated participation in multi-project CC. We model CC as a game to vali- date the hypothesis and build an RL-based simulator: EqC-Learner. We first show that EqC-Learner learns a policy that mimics equi- librium contributions in a single project case for the existing CC mechanisms. To validate EqC-Learner for the multi-project case, we present certain theoretical results for the general multi-project case. Via extensive simulation-based experiments, we show that the learned contributions in EqC-Learner follow all the available theo- retical analysis. Thus, we believe that such an RL-based simulator can learn equilibrium contributions for the general multi-project CC mechanism

Abstract

Smart cities play a vital role in limiting the ill-effects of rapid urbanization on the environment without compromising on benefits such as improving infrastructure, standard of living, and productivity. However, the collection, storage, and sharing of data from the plethora of sensor networks in a typical smart city deployment warrants a well-defined data platform architecture. In this paper, we propose a multi layer architecture compliant with the oneM2M standards and the Indian Urban Data Exchange (IUDX) framework. The proposed architecture consists of Data Monitoring (DML), Data Storage (DSL), and Data Exchange (DEL) layers. The DML employs oneM2M as the middleware platform to achieve interoperability. The DSL uses a multi-tenant architecture with multiple logical databases, enabling efficient and reliable data management. The DEL utilizes standard data schemas and open APIs of IUDX to avoid data silos, and enables secure data sharing. Further, we present a proof-of-concept implementation of our architecture deployed in a university campus using OM2M, PostgreSQL, and Django. Finally, simulations mimicking real-time data insertion and retrieval showed that the DML can handle 600 concurrent users with an average latency under 100 milli seconds. The DSL improved the latency compared to a single database architecture and the DEL could handle 100 concurrent users with zero failed requests.

Abstract

Transformers, which were introduced for solving the task of machine translation, have expanded their utility in multiple domains. A recent application of transformers is in solving elementary mathematics problems. In this paper, we use a hybrid tokenization technique for encoding the mathematics and science problems and answers, which is used to train the transformer. We compare the performance of our tokenization with that of the char-to-char tokenzation in solving various types of mathematics and science problems. We discuss the accuracy, memory usage, and time to train the model with proposed tokenization. The proposed tokenization shows higher accuracy for some problems, and requires lesser memory compared to char-to-char tokenization. We propose an extended dataset of science and mathematics problems that consists of billions of samples in questionanswer format in raw text. Code and Dataset: https: //github.com/misterpawan/scimat2

Abstract

Locomotion in a virtual environment within a limited physical space is a complex activity. There exist established techniques to support limitless natural walking in virtual environments. These include Redirected walking, Dynamic path generation, and Walk-Inplace technique, etc. PragPal is one such limitless path generation technique that supports natural walking in virtual environments. It is a novel software-based non-haptic locomotion technique. In this paper, we detail the enhancements to the existing PragPal path generation technique that addresses underlying issues in the technique like (1) path collision at angular turns, (2) effective usage of the physical play area, and (3) the ability to set path-width during path turns.

Abstract

Eye disorders like myopia, hypermetropia, and color blindness affect millions of people around the world. Early detection can help the affected persons take preventive measures against further degradation. However, early detection requires regular testing and visits to trained healthcare professionals to judge the level of disorder. The customization of the test requirements can simplify the method of diagnosis. This paper presents an approach towards developing a virtual reality based low-cost head-mounted device (HMD). The standalone HMD can detect eye disorders like visual acuity, astigmatism, color vision deficiency, and age-related macular degeneration, limiting the involvement of healthcare professionals. We did a user study to test the accuracy of our prototype against various optical test scenarios. The study revealed that the prototype achieved an overall accuracy of 93%. We further aim to deploy the device in economically vulnerable communities with limited access to healthcare infrastructure.

Abstract

FPGAs are increasingly significant for deploying convolutional neural network (CNN) inference models because of performance demands and power constraints in embedded and data centre applications. Object detection and classification are essential tasks in computer vision. You Only Look Once (YOLO) is a very efficient algorithm for object detection and classification with its variant Yolov3-tiny specially designed for embedded applications. This paper presents the FPGA accelerator for multiple precisions (FIXED-8, FIXED-16, FLOAT32) of YoloV3-tiny. We use a homogenous systolic array architecture with a synchronized pipeline adder tree for convolution, allowing it to be scalable for multiple variants of Yolo with a change in host driver. We evaluated the design on Terasic DE5a-Net-DDR4. The Fixed point (FP-8, FP-16) implementations attain a throughput of 57 GOPs/s (> 23%) and 46.16 GOPs/s (> 340 %). We synthesized the first FLOAT32 imnlementation attaining 11.22 GFLOPs/s.

Abstract

This paper presents a low cost, easy to fabricate, thin-film sensor. We used three different methods to make the thin film using two different base (film) materials: carbon and graphite. We briefly summarize the method and outcome of multi-layer thin film fabrication using spray coating, spin-coating, and drop-casting. The quality of the thin films is compared, and electrical resistance is characterized. The fabricated thin film is used to measure humidity and is characterized against a commercial humidity sensor (DHT22). The experimental details and results of humidity measurement are discussed. We were able to achieve resistance range of 5k - 1.5k ohms for 40 - 80 % RH, respectively. Plans to further optimize the sensitivity of the sensor, in future is discussed. Index Terms—thin film sensors, spray coating, spin-coating, drop-casting, low-cost sensor fabrication, humidity sensor

Abstract

The possibility of a vectorlike top partner decaying to a new colorless weak-singlet scalar or pseudoscalar has attracted some attention in the literature. We investigate the production of a weaksinglet charge-2=3 T quark that can decay to a spinless boson (Φ) and a top quark at the LHC. Earlier [A. Bhardwaj et al., Roadmap to explore vectorlike quarks decaying to a new scalar or pseudoscalar, arXiv:2203.13753.], we showed that in a large part of the parameter space, the T → tΦ and the loopinduced Φ → gg decays become the dominant decay modes for these particles. Here, we investigate the discovery prospects of the T quark in this region through the above decays. In particular, we focus on the pp → TT → ðtΦÞðtΦÞ → ðtðggÞÞðtðggÞÞ channel. Separating this signal from the huge Standard Model background is a challenging task, forcing us to employ a multivariate machine-learning technique. We find that the above channel can be a discovery channel of the top partner in the large part of the parameter space where the above decay chain dominates. Our analysis is largely model independent, and hence our results would be useful in a broad class of new physics models.

Abstract

In the left-right symmetric models, a heavy charged gauge boson W0 can decay to a lepton and a righthanded neutrino (RHN). If the neutrino masses are generated through the standard type-I seesaw mechanism, the Yukawa couplings controlling two-body decays of the RHN become very small. As a result, the RHN decays to another lepton and a pair of jets via an off-shell W0 . This is the basis of the Keung-Senjanović (KS) process, which was originally proposed as a probe of lepton number violation at the LHC. However, if a different mechanism like the inverse seesaw generates the neutrino masses, a TeV-scale RHN can have large Yukawa couplings and hence dominantly decay to a lepton and a W boson, leading to a kinematically different process from the KS one. We investigate the prospect of this unexplored process as a probe of the inverse seesaw mechanism in the left-right symmetric models at the High Luminosity LHC (HL-LHC). Our signal arises from the Drell-Yan production of a W0 and leads to two high-pT same-flavour-opposite-sign leptons and a boosted W-like fatjet in the final state. We find that a sequential W0 with mass up to ∼6 TeV along with a TeV-scale RHN can be discovered at the HL-LHC.

Abstract

At the LHC, the gluon-initiated processes are considered to be the primary source of di-Higgs production. However, in the presence of a new resonance, the light-quark initiated processes can also contribute significantly. In this letter, we look at the di-Higgs production mediated by a new singlet scalar. The singlet is produced in both quark-antiquark and gluon fusion processes through loops involving a scalar leptoquark and right-handed neutrinos. With benchmark parameters inspired from the recent resonant di-Higgs searches by the ATLAS collaboration, we examine the prospects of such a resonance in the TeVrange at the High-Luminosity LHC (HL-LHC) in the b¯ bτ +τ − mode with a multivariate analysis. We obtain the 5σ and 2σ contours and find that a significant part of the parameter space is within the reach of the HL-LHC.