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Applying pseudo labeling techniques has been found to be advantageous in semi-supervised 3D object detection (SSOD) in Bird's-Eye-View (BEV) for autonomous driving, particularly where labeled data is limited. In the literature, Exponential Moving Average (EMA) has been used for adjustments of the weights of teacher network by the student network. However, the same induces catastrophic forgetting in the teacher network. In this work, we address this issue by introducing a novel concept of Reflective Teacher where the student is trained by both labeled and pseudo labeled data while its knowledge is progressively passed to the teacher through a regularizer to ensure retention of previous knowledge. Additionally, we propose Geometry Aware BEV Fusion (GA-BEVFusion) for efficient alignment of multi-modal BEV features, thus reducing the disparity between the modalities - camera and LiDAR. This helps to map the precise geometric information embedded among LiDAR points reliably with the spatial priors for extraction of semantic information from camera images. Our experiments on the nuScenes and Waymo datasets demonstrate: 1) improved performance over state-of-the-art methods in both fully supervised and semi-supervised settings; 2) Reflective Teacher achieves equivalent performance with only 25% and 22% of labeled data for nuScenes and Waymo datasets respectively, in contrast to other fully supervised methods that utilize the full labeled dataset.

We investigate TeV scale type-I and type-II seesaw scenarios at the tree level at the $\mu^+ e^-$ and $\mu^+ \mu^+$ colliders in the $\mu$TRISTAN experiment. In minimal type-I seesaw scenario we consider two generations of Standard Model (SM) singlet heavy Majorana type Right Handed Neutrinos (RHNs) which couples with SM gauge boson through light-heavy neutrino mixing. We consider a minimal scenario where one (other) generation of the RHN dominantly couples with electron (muon). Generating events for the signal and generic SM backgrounds for the final states $e^- \nu jj$ and $\mu^+ \nu jj$ from $\mu^+ e^-$ collision at $\sqrt{s}=346$ GeV and 1 ab$^{-1}$ luminosity, we estimate limits on the light-heavy neutrino mixing angles with respect to heavy neutrino mass for two generations of the heavy neutrinos. Comparing with the existing limits we find such limits could be two orders of magnitude stronger than electroweak precision data. Further, we study the effect of doubly charged scalar boson $(H^{++})$ from the type-II seesaw scenario in $\mu^+ \mu^+$ collision at $\sqrt{s}=2$ TeV. In this case we consider $\mu^+ \mu^+ \to \ell_i^+ \ell_j^+$ and $\mu^+ \mu^+ \to H^{++} Z/ \gamma$ processes followed by the same sign dilepton decay of $H^{++}$. We find that events involving $e^+ e^+$ among these final states could provide significant difference between the normal and inverted orderings of the neutrino mass which could be probed in $\mu$TRISTAN collider at 5$\sigma$ significance in future., Comment: 22 pages, 11 figures

Predicting ego vehicle trajectories remains a critical challenge, especially in urban and dense areas due to the unpredictable behaviours of other vehicles and pedestrians. Multimodal trajectory prediction enhances decision-making by considering multiple possible future trajectories based on diverse sources of environmental data. In this approach, we leverage ResNet-50 to extract image features from high-definition map data and use IMU sensor data to calculate speed, acceleration, and yaw rate. A temporal probabilistic network is employed to compute potential trajectories, selecting the most accurate and highly probable trajectory paths. This method integrates HD map data to improve the robustness and reliability of trajectory predictions for autonomous vehicles., Comment: This paper is a preprint of a paper submitted to the 26th Irish Machine Vision and Image Processing Conference (IMVIP 2024). If accepted, the copy of record will be available at IET Digital Library

We investigate a general $U(1)_X$ scenario where we introduce three generations of Standard Model (SM) singlet Right Handed Neutrinos (RHNs) to generate the light neutrino mass through the seesaw mechanism after the breaking of $U(1)_X$ and electroweak symmetries. In addition to that, a general $U(1)_X$ scenario involves an SM-singlet scalar field and due to the $U(1)_X$ symmetry breaking the mass of a neutral beyond the SM (BSM) gauge boson $Z^\prime$ is evolved. The RHNs, being charged under $U(1)_X$ scenario, can explain the origin of observed baryon asymmetry through the resonant leptogenesis process. Applying observed neutrino oscillation data we study $Z^\prime$ and BSM scalar induced processes to reproduce the observed baryon asymmetry. Hence we estimate bounds on the $U(1)_X$ gauge coupling $(g_X)$ and the mass of the $Z^\prime$ $(M_{Z^\prime})$ for different $U(1)_X$ charges and benchmark masses of RHN and SM-singlet scalar. Finally we compare our results with limits obtained from the existing limits from LEP-II and LHC. We find that depending on the $U(1)_X$ charges, the masses of RHNs and SM-singlet scalar resonant leptogenesis could provide stronger limit on $g_X$ for $M_{Z^\prime} > 5.8$ TeV which could be probed by high energy scattering experiment in future., Comment: 13 pages, two figures

We investigate dark matter (DM)-electron scattering in a minimal $U(1)_X$ extension of the Standard Model (SM), where the DM can appear as a Majorana fermion, a complex singlet scalar or a Dirac fermion. To study bounds on the $U(1)_X$ gauge coupling $(g_X)$ and new gauge boson mass $(M_{Z^\prime})$, from DM-electron scattering, we consider several direct search experiments like CDMS, DAMIC, SENSEI, PandaX-II, DarkSide-50 and XENON1T-S2 for different $U(1)_X$ charges. In this set-up we consider DM production via freeze-in both in radiation dominated and modified cosmological background to project sensitivities on $g_X-M_{Z^\prime}$ plane satisfying observed relic abundance. DM-electron scattering could provide comparable, or even stronger bounds than those obtained from the electron/ muon $(g-2)$, low energy scattering and intensity frontier experiments within 0.01 GeV $\lesssim M_{Z^\prime} \lesssim$ 0.1 GeV. Constrains from freeze-in could provide stronger sensitivities for $M_{Z^\prime}\gtrsim \mathcal{O}(1)$ GeV, however, these limits are comparable to those obtained from LHCb, LEP experiments for $\mathcal{O}(10)$ GeV $\lesssim M_{Z^\prime} \lesssim 150$ GeV. In future, electron-muon scattering (MUonE), proton (FASER, DUNE) and electron/positron (ILC) beam dump experiments could probe these parameters., Comment: 22 pages, 6 figures, version accepted for publication in PRD

The origin of tiny neutrino mass is a long standing unsolved puzzle of the Standard Model (SM), which allows us to consider scenarios beyond the Standard Model (BSM) in a variety of ways. One of them being a gauge extension of the SM may be realized as in the form of an anomaly free, general $U(1)_X$ extension of the SM, where an $SU(2)_L$ triplet scalar with a $U(1)_X$ charge is introduced to have Dirac Yukawa couplings with the SM lepton doublets. Once the triplet scalar developes a Vacuum Expectation Value (VEV), light neutrinos acquire their tiny Majorana masses. Hence, the decay modes of the triplet scalar has a direct connection to the neutrino oscillation data for different neutrino mass hierarchies. After the breaking of the $U(1)_X$ gauge symmetry, a neutral $U(1)_X$ gauge boson $(Z^\prime)$ acquires mass, which interacts differently with the left and right handed SM fermions. Satisfying the recent LHC bounds on the triplet scalar and $Z^\prime$ boson productions, we study the pair production of the triplet scalar at LHC, 100 TeV proton proton collider FCC, $e^-e^+$ and $\mu^-\mu^+$ colliders followed by its decay into dominant dilepton modes whose flavor structure depend on the neutrino mass hierarchy. Generating the SM backgrounds, we study the possible signal significance of four lepton final states from the triplet scalar pair production. We also compare our results with the purely SM gauge mediated triplet scalar pair production followed by four lepton final states, which could be significant only in $\mu^- \mu^+$ collider., Comment: 14 pages, 7 figures, extended analysis and muon collider corrected

Severe weather events such as floods, hurricanes, earthquakes, and large wind or ice storms can cause extensive damage to electrical distribution networks, requiring a multi-day restoration effort. Complicating the recovery process is the lack of complete and accurate information regarding the extent and locations of damages, at least during the initial part of the recovery process. These factors make workforce planning challenging. In this paper, we adopt a rolling horizon restoration framework whereby repairs are planned for adjustable finite length restoration windows. Considering both repair times as well as travel times, we show that the optimal scheduling problem with multiple crews, each with their own time budget, can be recast in terms of a cost constrained reward maximizing mTSP (traveling salesman problem) on doubly weighted graphs, where the objective is to maximize the aggregate reward earned during the upcoming restoration window, provided no crew violates its time budget and certain electrical continuity constraints are met. We propose a mixed integer linear programming (MILP) model for solving the above problem which is validated on standard IEEE PES test feeder networks., Comment: 26 pages, 17 figures

Droplet impact is a fascinating phenomenon that occurs when a liquid droplet collides with a surface. It is not only a fundamental area of scientific inquiry but also has practical implications across many industries and natural systems. The dynamics during droplet impact on liquid-impregnated surfaces are of special interest because the properties of the surface and impregnated liquid may significantly change the impact outcome. We present a detailed study of the impact and subsequent retraction of liquid droplets on a liquid-impregnated surface using high-speed imagery. Square-shaped textures with varying post-spacings of 5, 20, and 30 um on a silicon wafer were fabricated and functionalised using octadecyl trichlorosilane. Two different lubricants, silicone oil and hexadecane, were infused to investigate how their properties affect impact dynamics. Droplet impacts were investigated on these surfaces across a broad range of Weber numbers (28-495). Additionally, we measured the stability of the LIS surface by calculating spreading coefficients and contact angles. The experiments revealed that the properties of the infused oil play an insignificant role in droplet dynamics, including spreading, rebound, and unique phenomena related to oil interaction with surface textures. This study provides insights into the intricate dynamics of droplet interactions with LIS, offering valuable contributions to understanding surface-wetting phenomena., Comment: 25 Page number and onwards, supporting information is shown

We discuss the Higgs phenomenology in models with a new $U(1)_X$ gauge symmetry including the $U(1)_{B-L}$ scenario, where three right-handed neutrinos are inevitably introduced due to the gauge anomaly cancellations. We find that the decay branching ratio of the discovered Higgs boson into a pair of new massive gauge bosons ($Z'$) can significantly be enhanced in the Dirac neutrino case as compared with the Majorana case for a fixed value of the new gauge coupling and the mass of $Z'$ under constraints from current experimental data. Because of such an enhancement, the Dirac case can indirectly be discriminated from the Majorana case via the Higgs decay., Comment: 6 pages, 4 figures. Version accepted for publication in Phys. Lett. B

Accurate obstacle identification represents a fundamental challenge within the scope of near-field perception for autonomous driving. Conventionally, fisheye cameras are frequently employed for comprehensive surround-view perception, including rear-view obstacle localization. However, the performance of such cameras can significantly deteriorate in low-light conditions, during nighttime, or when subjected to intense sun glare. Conversely, cost-effective sensors like ultrasonic sensors remain largely unaffected under these conditions. Therefore, we present, to our knowledge, the first end-to-end multimodal fusion model tailored for efficient obstacle perception in a bird's-eye-view (BEV) perspective, utilizing fisheye cameras and ultrasonic sensors. Initially, ResNeXt-50 is employed as a set of unimodal encoders to extract features specific to each modality. Subsequently, the feature space associated with the visible spectrum undergoes transformation into BEV. The fusion of these two modalities is facilitated via concatenation. At the same time, the ultrasonic spectrum-based unimodal feature maps pass through content-aware dilated convolution, applied to mitigate the sensor misalignment between two sensors in the fused feature space. Finally, the fused features are utilized by a two-stage semantic occupancy decoder to generate grid-wise predictions for precise obstacle perception. We conduct a systematic investigation to determine the optimal strategy for multimodal fusion of both sensors. We provide insights into our dataset creation procedures, annotation guidelines, and perform a thorough data analysis to ensure adequate coverage of all scenarios. When applied to our dataset, the experimental results underscore the robustness and effectiveness of our proposed multimodal fusion approach., Comment: 16 pages, 12 Figures, 6 tables

General $U(1)$ extension of the Standard Model (SM) is a well motivated beyond the Standard Model(BSM) scenario where three generations of right handed neutrinos (RHNs) are introduced to cancel gauge and mixed gauge-gravity anomalies. After the $U(1)_X$ is broken, RHNs participate in the seesaw mechanism to generate light neutrino masses satisfying neutrino oscillation data. In addition to that, a neutral gauge boson $Z^\prime$ is evolved which interacts with the left and right handed fermions differently manifesting chiral nature of the model which could be probed in future collider experiments. As a result, if we consider $\mu^+ e^-$ and $\mu^+ \mu^+$ collisions in $\mu$TRISTAN experiment $Z^\prime$ mediated $2\to2$ scattering will appear in $t-$ and $u-$channels depending on the initial and final states being accompanied by the photon and $Z$ mediated interactions. This will result well motivated resulting forward dominant scenarios giving rise to sizable left-right asymmetry. Estimating constrains on general $U(1)$ coupling from LEP-II and LHC for different $U(1)_X$ charges, we calculate differential and integrated scattering cross section and left-right asymmetry for $\mu^+ e^- \to \mu^+ e^-$ and $\mu^+ \mu^+ \to \mu^+ \mu^+$ processes which could be probed at $\mu$TRISTAN experiment further enlightening the interaction between $Z^\prime$ and charged leptons and the $U(1)_X$ breaking scale., Comment: 18 pages

Trajectory prediction is, naturally, a key task for vehicle autonomy. While the number of traffic rules is limited, the combinations and uncertainties associated with each agent's behaviour in real-world scenarios are nearly impossible to encode. Consequently, there is a growing interest in learning-based trajectory prediction. The proposed method in this paper predicts trajectories by considering perception and trajectory prediction as a unified system. In considering them as unified tasks, we show that there is the potential to improve the performance of perception. To achieve these goals, we present BEVSeg2TP - a surround-view camera bird's-eye-view-based joint vehicle segmentation and ego vehicle trajectory prediction system for autonomous vehicles. The proposed system uses a network trained on multiple camera views. The images are transformed using several deep learning techniques to perform semantic segmentation of objects, including other vehicles, in the scene. The segmentation outputs are fused across the camera views to obtain a comprehensive representation of the surrounding vehicles from the bird's-eye-view perspective. The system further predicts the future trajectory of the ego vehicle using a spatiotemporal probabilistic network (STPN) to optimize trajectory prediction. This network leverages information from encoder-decoder transformers and joint vehicle segmentation., Comment: Accepted for publication in the International Conference on Computer Vision Theory and Applications (VISAPP) 2024

The design and fabrication of extremely hydrate phobic Liquid Impregnated Surfaces (sometimes abbreviated as LIS) based on industrial material Aluminium Al6061 and industrially scalable low-cost method were carried out. A simple hydrochloric acid-based etching method and boiling water treatment were used to generate micro and nanoscale nanopetal roughness features respectably. A theoretical analysis was performed to find out the relationship between the interfacial interactions and surface roughness features to predict the stability of lubricant oil of LIS under water and oil environment. LIS with appropriate surface chemistry and textures were fabricated to experimentally validate the theoretical analysis on lubricant stability. Subsequent experimental measurements of hydrate adhesion were performed on LIS with stable lubricant layers, using a custom-made experimental setup and cantilever-based method. Cyclopentane hydrate, mimics gas hydrate forming mechanism at atmospheric pressure, and is used for the hydrate adhesion measurements. Extreme reduction of hydrate adhesion with more than four orders of magnitude was observed on LIS compared to control smooth aluminium surfaces

The origin of tiny neutrino mass is an unsolved puzzle leading to a variety of phenomenological aspects beyond the Standard Model (BSM). We consider $U(1)$ gauge extension of the Standard Model (SM) where so-called seesaw mechanism is incarnated with the help of thee generations of Majorana type right-handed neutrinos followed by the breaking of $U(1)$ and electroweak gauge symmetries providing anomaly free structure. In this framework, a neutral BSM gauge boson $Z^\prime$ is evolved. To explore the properties of its interactions we consider chiral (flavored) frameworks where $Z^\prime$ interactions depend on the handedness (generations) of the fermions. In this paper we focus on $Z^\prime-$neutrino interactions which could be probed from cosmic explosions. We consider $\nu \overline{\nu} \to e^+ e^-$ process which can energize gamma-ray burst (GRB221009A, so far the highest energy) through energy deposition. Hence estimating these rates we constrain $U(1)$ gauge coupling $(g_X)$ and $Z^\prime$ mass $(M_{Z^\prime})$ under Schwarzchild (Sc) and Hartle-Thorne (HT) scenarios. We also study $\nu-$DM scattering through $Z^\prime$ to constrain $g_X-M_{Z^\prime}$ plane using IceCube data considering high energy neutrinos from cosmic blazar (TXS0506+056), active galaxy (NGC1068), the Cosmic Microwave Background (CMB) and the Lyman-$\alpha$ data, respectively. Finally highlighting complementarity we compare our results with current and prospective bounds on $g_X-M_{Z^\prime}$ plane from scattering, beam-dump and $g-2$ experiments., Comment: 47 pages, 16 figures

In this article, superhydrophobic leaves of Cassia tora, Adiantum capillus-veneris (ACV), and Bauhinia Variegate are reported for the first time, and the wettability of these leaf's surfaces was correlated with their surface morphology at micro and nanoscale. Field Emission Scanning Electron Microscopy (FESEM) images of the surfaces were used to get surface morphological information at the micro-nanoscale structures. A special drying method was implemented to ensure the minimal structural collapse of these surfaces under the high vacuum of FESEM. FESEM images of Cassia tora leaves showed widely spaced, low aspect ratio nano petals distributed on bumpy blunt micro features, responsible for high contact angle hysteresis and high roll angle measured on the Cassia tora leaves. ACV leaves showed the presence of micron-scale spherical morphology made of nanoscale hair-like features. These hierarchical re-entrant surface features generated a very high contact angle and low roll-off angle. Leaves of Bauhinia variegate showed similar superhydrophobic and self-cleaning properties. However, surface features were different, which consisted of a higher aspect ratio and closely spaced nano petals uniformly distributed over flat surfaces consisting of micro-scale ridges. Droplet impact studies on these surfaces at different Weber numbers showed different behaviour due to these different micro-nano features.

Motivated by the observation of tiny neutrino mass can not be explained within the framework of Standard Model (SM), we consider extra gauge extended scenarios in which tiny neutrino masses are generated through seesaw mechanism. These scenarios are equipped with beyond the standard model (BSM) neutral gauge boson called $Z^\prime$ in the general $U(1)_X$ symmetry which is a linear combination of $U(1)_Y$ and $U(1)_{B-L}$. In this case, left and right handed fermions interact differently with the $Z^\prime$. The $Z^\prime$ gives rise to different processes involving neutrino-nucleon, neutrino-electron, electron-nucleus and electron-muon scattering processes. By comparing with proton, electron beam-dump experiments data, recast data from searches for the long-lived and dark photon at BaBaR, LHCb and CMS experiments, the electron and muon $g-2$ data, and the data of the dilepton and dijet searches at the LEP experiment, we derive bounds on the gauge coupling and the corresponding gauge boson mass for different $U(1)_X$ charges and evaluate the prospective limits from the future beam-dump scenarios at DUNE, FASER(2) and ILC. We conclude that large parameter regions could be probed by scattering, beam-dump and collider experiments in future., Comment: 30 pages

Autonomous vehicles require accurate and reliable short-term trajectory predictions for safe and efficient driving. While most commercial automated vehicles currently use state machine-based algorithms for trajectory forecasting, recent efforts have focused on end-to-end data-driven systems. Often, the design of these models is limited by the availability of datasets, which are typically restricted to generic scenarios. To address this limitation, we have developed a synthetic dataset for short-term trajectory prediction tasks using the CARLA simulator. This dataset is extensive and incorporates what is considered complex scenarios - pedestrians crossing the road, vehicles overtaking - and comprises 6000 perspective view images with corresponding IMU and odometry information for each frame. Furthermore, an end-to-end short-term trajectory prediction model using convolutional neural networks (CNN) and long short-term memory (LSTM) networks has also been developed. This model can handle corner cases, such as slowing down near zebra crossings and stopping when pedestrians cross the road, without the need for explicit encoding of the surrounding environment. In an effort to accelerate this research and assist others, we are releasing our dataset and model to the research community. Our datasets are publicly available on https://github.com/sharmasushil/Navigating-Uncertainty-Trajectory-Prediction .

We investigate the possibilities of probing the electroweak scale seesaw scenarios such as type-I, type-II and type-III seesaw at $e^-\gamma$ and $\gamma\gamma$ colliders. For the case of type-I seesaw, the heavy neutrinos can be produced at $e^{-}\gamma$ colliders in association with a $W$ boson. We study a variety of final states in this case including single and multilepton modes in association with jets to estimate bounds on the light-heavy neutrino mixing angle. In case of type-II seesaw, doubly charged multiplets of the SU$(2)_L$ triplet scalar can be produced in pair at $\gamma \gamma$ collider. We study the multi-leptonic decay modes coming from this pair production of doubly charged Higgs and show how one can probe neutrino mass hierarchy. We also study same sign $W$ boson production from the doubly charged Higgs to study multilepton modes in association with missing energy. From the type-III seesaw, we study same sign dilepton+jets and trilepton+jets modes at $e^-\gamma$ collider which are coming from the neutral and charged component of the triplet fermion in association with a $W$ boson and $Z$ boson, respectively. Due to the existing limits on the triplet fermions from the LHC we choose heavier mass so that the gauge boson originated from the decay of a neutral multiplet can be sufficiently boosted producing a fat-jet signature in association with same sign dilepton and trilepton. Finally we estimate bounds on the light neutrino-heavy triplet fermion mixing angle and compare with the existing bounds., Comment: 53 pages, 30 figures

Deoxyribonucleic acid (DNA) has emerged as a promising building block for next-generation ultra-high density storage devices. Although DNA has high durability and extremely high density in nature, its potential as the basis of storage devices is currently hindered by limitations such as expensive and complex fabrication processes and time-consuming read-write operations. In this article, we propose the use of a DNA crossbar array architecture for an electrically readable Read-Only Memory (DNA-ROM). While information can be written error-free to a DNA-ROM array using appropriate sequence encoding, its read accuracy can be affected by several factors such as array size, interconnect resistance, and Fermi energy deviations from HOMO levels of DNA strands employed in the crossbar. We study the impact of array size and interconnect resistance on the bit error rate of a DNA-ROM array through extensive Monte Carlo simulations. We have also analyzed the performance of our proposed DNA crossbar array for an image storage application, as a function of array size and interconnect resistance. While we expect that future advances in bioengineering and materials science will address some of the fabrication challenges associated with DNA crossbar arrays, we believe that the comprehensive body of results we present in this paper establishes the technical viability of DNA crossbar arrays as low-power, high-density storage devices. Finally, our analysis of array performance vis-a-vis interconnect resistance should provide valuable insights into aspects of the fabrication process such as the proper choice of interconnects necessary for ensuring high read accuracies.

Multimodal learning, particularly for pedestrian detection, has recently received emphasis due to its capability to function equally well in several critical autonomous driving scenarios such as low-light, night-time, and adverse weather conditions. However, in most cases, the training distribution largely emphasizes the contribution of one specific input that makes the network biased towards one modality. Hence, the generalization of such models becomes a significant problem where the non-dominant input modality during training could be contributing more to the course of inference. Here, we introduce a novel training setup with regularizer in the multimodal architecture to resolve the problem of this disparity between the modalities. Specifically, our regularizer term helps to make the feature fusion method more robust by considering both the feature extractors equivalently important during the training to extract the multimodal distribution which is referred to as removing the imbalance problem. Furthermore, our decoupling concept of output stream helps the detection task by sharing the spatial sensitive information mutually. Extensive experiments of the proposed method on KAIST and UTokyo datasets shows improvement of the respective state-of-the-art performance., Comment: 5 pages, 3 figure, 4 tables

We discuss the discovery potential of a promising signals, $3 \times \mu^+ \mu^-$ at a $\mu^+ \mu^-$ collider and $\mu^+ \mu^+ \mu^+ \mu^+ \mu^- \mu^- $ at a $\mu^+ \mu^+$ collider, that are obtained via production of $Z'$ and a new scalar boson $\phi$ in a spontaneously broken local $U(1)_{L_\mu - L_\tau}$ model. We consider the $Z'$ associated production from the process $\mu^+ \mu^- \to \phi Z'$ in addition to a muonphilic $Z'$ fusion process $\mu^+ \mu^\pm \to \phi \mu^+ \mu^\pm$. The scalar boson is associated with $U(1)_{L_\mu - L_\tau}$ symmetry breaking and dominantly decays into $Z' Z'$ mode. We carry out numerical simulation analysis for signal and background processes to estimate a discovery significance for different benchmark points. It is shown that our signal can be observed with integrated luminosity less than $\mathcal{O}(100)$ fb$^{-1}$ for both $\mu^+ \mu^-$ and $\mu^+ \mu^+$ colliders with more than 5-$\sigma$ significance., Comment: 16 pages, 6 figures, minor corrections are made

Clathrate hydrate is a naturally occurring ice-like solid which forms in water phase under suitable temperature and pressure conditions, in the presence of one or more hydrophobic molecules. It also forms inside the oil and gas pipes leading to higher pumping cost, flow blockage and even catastrophic accidents. Engineered surfaces with low hydrate adhesion can provide an effective solution to this problem. Liquid impregnated surfaces is one such example of engineered surfaces which has already shown tremendous potential in reducing the nucleation and adhesion of solids. Here we report the design and synthesis of liquid impregnated surfaces with extremely low hydrate adhesion under the mixed environment of oil and water. The most challenging aspect of designing these surfaces was to stabilize a lubricant layer simultaneously under the water and oil. A detailed methodology to make such lubricant stable surfaces from theoretical perspective was described and experimentally validated for lubricant stability. Experimental measurements on such surfaces showed extremely low hydrate accumulation and one order of magnitude or more reduction in hydrate adhesion force., Comment: 32 pages, 9 figures

This is the Snowmass2021 Energy Frontier (EF) Beyond the Standard Model (BSM) report. It combines the EF topical group reports of EF08 (Model-specific explorations), EF09 (More general explorations), and EF10 (Dark Matter at Colliders). The report includes a general introduction to BSM motivations and the comparative prospects for proposed future experiments for a broad range of potential BSM models and signatures, including compositeness, SUSY, leptoquarks, more general new bosons and fermions, long-lived particles, dark matter, charged-lepton flavor violation, and anomaly detection., Comment: 108 pages + 38 pages references and appendix, 37 figures, Report of the Topical Group on Beyond the Standard Model Physics at Energy Frontier for Snowmass 2021. The first nine authors are the Conveners, with Contributions from the other authors

We propose an anomaly free gauged U$(1)$ extension of the SM where three right handed heavy neutrinos, being charged under the general U$(1)$ gauge group, are introduced to explain the origin of the tiny neutrino mass through the seesaw mechanism after the general U$(1)$ symmetry is broken. Due to the breaking of the general U$(1)$ symmetry a neutral beyond the standard model gauge boson $Z^\prime$ acquires mass. There are two Higgs doublets in this model where one interacts with the SM fermions and the other one interacts with the right handed heavy neutrinos and charged leptons. The charged multiplet of the second Higgs can completely decay into the heavy neutrinos and charged lepton in the neutrinophilic limit of the model parameters. The charged Higgs pair production can be influenced due to presence of the $Z^\prime$ boson at the High Luminosity LHC (HL-LHC) in addition to the neutral SM gauge bosons. The pair produced charged Higgs bosons decay into SM charged leptons and heavy neutrinos. Following the leading decay modes of the heavy neutrinos into charged leptons and $W$ boson we study the 5 and 6 lepton final states after the leptonic and hadronic decay of the $W$ bosons considering solely muons and electrons in the final state. Combining the electron and muon final states we estimate the significance of the 5 and 6 charged lepton processes in the $m_{H^\pm}-m_N$ plane for different benchmark points of $m_{Z^\prime}$. It is found that the 5 (6) charged lepton processes could be probed at the High Luminosity LHC (HL-LHC) with at least 5 (3) $\sigma$ significance and there are parameter regions where the significance could be larger., Comment: Published version in European Journal of Physics C

The origin of neutrino mass is a big unsolved problem of the Standard Model (SM) that motivate us to consider beyond the SM (BSM) scenarios where SM-singlet right-handed neutrinos (RHNs) are introduced to explain the origin of the light neutrino masses through the seesaw mechanism. There is a variety of ways which could lead us to this goal and one of them is a general U$(1)$ extension of the SM. In this scenario, three SM-singlet RHNs are introduced to cancel the gauge and mixed gauge gravity anomalies. After anomaly cancellation, we notice that the left- and right-handed charged fermions are differently charged under the general U$(1)$ gauge group evolving a chiral scenario. After the breaking of the general U$(1)$ symmetry, a neutral BSM gauge boson $(Z^\prime)$ acquires mass and it is a free parameter. Such $Z^\prime$, being lighter than $5$ GeV, could be probed at the intensity and lifetime frontiers like FASER, FASER2, DUNE, and ILC beam dump experiments. The estimated bounds are needed to be compared with the existing bounds. We find that existing constraints from Orsay, Nomad, PS191, KEK, LSND, CHARM experiments, and cosmological scenario like SN1987A can be compared in our case once estimated for chiral scenarios. Finally, we compare the parameter spaces showing viable ones that could be probed by FASER, FASER2, DUNE, and ILC beam dump experiments and already excluded regions from Orsay, Nomad, PS191, KEK, LSND, CHARM, and SN1987A for a chiral scenario., Comment: 29 page, 7 figures; v2: typos corrected, references updated, figures modified, matches with the version published in Phys. Rev. D

Most of the existing works on pedestrian pose estimation do not consider estimating the pose of an occluded pedestrian, as the annotations of the occluded parts are not available in relevant automotive datasets. For example, CityPersons, a well-known dataset for pedestrian detection in automotive scenes does not provide pose annotations, whereas MS-COCO, a non-automotive dataset, contains human pose estimation. In this work, we propose a multi-task framework to extract pedestrian features through detection and instance segmentation tasks performed separately on these two distributions. Thereafter, an encoder learns pose specific features using an unsupervised instance-level domain adaptation method for the pedestrian instances from both distributions. The proposed framework has improved state-of-the-art performances of pose estimation, pedestrian detection, and instance segmentation., Comment: 4 pages, 5 tables, 2 figures

Shadows are frequently encountered natural phenomena that significantly hinder the performance of computer vision perception systems in practical settings, e.g., autonomous driving. A solution to this would be to eliminate shadow regions from the images before the processing of the perception system. Yet, training such a solution requires pairs of aligned shadowed and non-shadowed images which are difficult to obtain. We introduce a novel weakly supervised shadow removal framework UnShadowNet trained using contrastive learning. It is composed of a DeShadower network responsible for the removal of the extracted shadow under the guidance of an Illumination network which is trained adversarially by the illumination critic and a Refinement network to further remove artefacts. We show that UnShadowNet can be easily extended to a fully-supervised set-up to exploit the ground-truth when available. UnShadowNet outperforms existing state-of-the-art approaches on three publicly available shadow datasets (ISTD, adjusted ISTD, SRD) in both the weakly and fully supervised setups., Comment: Accepted for publication at IEEE Access, vol. 11, pp. 87760-87774, 2023