arXiv subjects

Mojtaba Hosseini

Publications and source records attributed to Mojtaba Hosseini.

18 recordsLinked to original sources

arXiv2025

The vacuum stability and the hierarchy problem in a fermionic dark matter model

We consider an extension to the Standard Model (SM) with four new fields including scalar($S$), spinor($\psi^{1,2}$) and vector($V_\mu$) under new $U(1)$ gauge group in the hidden sector. The scalar particle interacts with the SM Higgs particle and is an intermediary between the dark and the SM parts . Our dark matter(DM) candidate is the spinor particle. We show that the model successfully explains the relic density of the DM in the universe and evades the strong bounds from direct detection experiments while respecting the theoretical constraints and the vacuum stability conditions. In addition, we study the hierarchy problem within the Veltman approach by solving the renormalization group equations at one-loop. We demonstrate that the addition of the new fields contributes to the Veltman parameters which in turn results in satisfying the Veltman conditions much lower than the Planck scale. For the our DM model we find one representative point in the viable parameter space which satisfy also the Veltman conditions at $\Lambda$ = 1 TeV. Therefore, the presence of the extra particle solves the fine-tuning problem of the Higgs mass.

hep-ph↗

arXiv2025

Limitations of Freeze-in WIMP Dark Matter from Supercooled Phase Transitions

We revisit the possibility of producing Weakly Interacting Massive Particle (WIMP) dark matter via a freeze-in mechanism triggered by a supercooled first-order phase transition (FOPT) in the early universe. Unlike traditional freeze-out and FIMP scenarios, this mechanism relies on a rapid entropy injection that dilutes the preexisting dark matter abundance and prevents re-equilibration due to a sudden mass increase. In this study, we systematically examine a variety of single-component dark matter models-including vector, fermionic, and scalar-mediated candidates-to assess whether they can satisfy the key cosmological condition T2 >> T1, required for successful WIMP freeze-in after FOPT. Contrary to earlier results, our revised analysis finds that none of the models fulfill this condition across viable parameter spaces. We confirm, however, that the scalar dark matter model analyzed in Ref. [1] is the only known viable single-component model that fulfills T2 >> T1 and enables WIMP freeze-in via this mechanism. These findings place important constraints on model-building efforts and suggest that successful freeze-in after FOPT may require multi-component or more complex dark sectors beyond the scope of minimal models.

hep-ph↗

arXiv2024

Gravitational wave signatures of first-order phase transition in two-component dark matter model

Here, we consider a classically scale-invariant extension of the Standard Model (SM) with two-component dark matter (DM) candidates, including a Dirac spinor and a scalar DM. We probe the parameter space of the model, constrained by relic density and direct detection, and investigate the generation of gravitational waves (GWs) produced by an electroweak first-order phase transition. The analysis demonstrates that there are points in the parameter space, leading to a detectable GW spectrum arising from the first-order phase transition, which is also consistent with the DM relic abundance and direct detection bounds. These GWs could be observed by forthcoming space-based interferometers such as the Big Bang Observer, Decihertz Interferometer Gravitational-wave Observatory, and Ultimate-Decihertz Interferometer Gravitational-wave Observatory.

hep-ph↗

arXiv2024

Gravitational wave in a filtered vector dark matter model

We consider a first order phase transition (FOPT) for a Vector Dark Matter (VDM) in the early universe in which its mass may partially arise from such mechanism in the hidden sector. We calculate the ratio of VDM that may enter the bubble for various bubble wall velocities as well as various nucleation temperatures that produce the measured dark matter relic abundance via bubble filtering. In the following, we focus on gravitational wave (GW) signals which produced by FOPT and show that it can be detectable at the DECIGO, TianQin, LISA and Ultimate-DECIGO (UDECIGO) experiments.

hep-ph↗

arXiv2024

Gravitational wave effects and phenomenology of a two-component dark matter model

We study an extension of the Standard Model (SM) which could have two candidates for dark matter (DM) including a Dirac fermion and a vector dark matter (VDM) under a new $U(1)$ gauge group in the hidden sector. The model is classically scale-invariant and the electroweak symmetry breaks because of loop effects. We investigate the parameter space allowed by current experimental constraints and phenomenological bounds. We probe the parameter space of the model in the mass range $1< M_V<5000$ GeV and $1<M_ψ<5000$ GeV. It has been shown that there are many points in this mass range that are in agreement with all phenomenological constraints. The electroweak phase transition has been discussed and it has been shown that there is region in the parameter space of the model consistent with DM relic density and direct detection constraints that, at the same time, can lead to first order electroweak phase transition. The gravitational waves produced during the phase transition could be probed by future space-based interferometers such as LISA and BBO.

hep-ph↗

arXiv2024

Underground Freight Transportation for Package Delivery in Urban Environments

The use of underground freight transportation (UFT) is gaining attention because of its ability to quickly move freight to locations in urban areas while reducing road traffic and the need for delivery drivers. Since packages are transported through the tunnels by electric motors, the use of tunnels is also environmentally friendly. Unlike other UFT projects, we examine the use of tunnels to transport individual orders, motivated by the last mile delivery of goods from e-commerce providers. The use of UFT for last mile delivery requires more complex network planning than for direct lines that have previously been considered for networks connecting large cities. We introduce a new network design problem based on this delivery model and transform the problem into a fixed charge multicommodity flow problem with additional constraints. We show that this problem, the nd-UFT, is NP-hard, and provide an exact solution method for solving large-scale instances. Our solution approach exploits the combinatorial sub-structures of the problem in a cutting planes fashion, significantly reducing the time to find optimal solutions on most instances compared to a MIP. We provide computational results for real urban environments to build a set of insights into the structure of such networks and evaluate the benefits of such systems. We see that a budget of only 45 miles of tunnel can remove 42% of packages off the roads in Chicago and 32% in New York City. We estimate the fixed and operational costs for implementing UFT systems and break them down into a per package cost. Our estimates indicate over a 40% savings from using a UFT over traditional delivery models. This indicates that UFT systems for last mile delivery are a promising area for future research.

math.OC↗

arXiv2024

Vector dark matter and LHC constraints, including a 95 GeV light Higgs boson

We study LHC searches for an extension of the Standard Model (SM) by exploiting an additional Abelian $U_D(1)$ gauge symmetry and a complex scalar Higgs portal. As the scalar is charged under this gauge symmetry, a vector dark matter (VDM) candidate can satisfy the observed relic abundance and limits from direct dark matter (DM) searches. The ATLAS and CMS experiments have developed a broad search program for the DM candidates, including associate production of Higgs boson, $Z$ boson, and top quark that couple to DM. In this paper, we perform an extensive analysis to constrain the model by using these experiments at LHC. It can be seen that the LHC results can exclude some parts of the parameter space that are still allowed by relic density and the direct detection searches. Using the LHC results, all scalar Higgs portal masses are excluded for the light VDM. Furthermore, exclusion limits on the parameter space of the model by using the new results of the CMS and ATLAS Collaborations for a new light Higgs boson with mass $\sim95~\rm GeV$ are provided.

hep-ph↗

arXiv2024

Beyond Suspension: A Two-phase Methodology for Concluding Sports Leagues

Problem definition: Professional sports leagues may be suspended due to various reasons such as the recent COVID-19 pandemic. A critical question the league must address when re-opening is how to appropriately select a subset of the remaining games to conclude the season in a shortened time frame. Academic/practical relevance: Despite the rich literature on scheduling an entire season starting from a blank slate, concluding an existing season is quite different. Our approach attempts to achieve team rankings similar to that which would have resulted had the season been played out in full. Methodology: We propose a data-driven model which exploits predictive and prescriptive analytics to produce a schedule for the remainder of the season comprised of a subset of originally-scheduled games. Our model introduces novel rankings-based objectives within a stochastic optimization model, whose parameters are first estimated using a predictive model. We introduce a deterministic equivalent reformulation along with a tailored Frank-Wolfe algorithm to efficiently solve our problem, as well as a robust counterpart based on min-max regret. Results: We present simulation-based numerical experiments from previous National Basketball Association (NBA) seasons 2004--2019, and show that our models are computationally efficient, outperform a greedy benchmark that approximates a non-rankings-based scheduling policy, and produce interpretable results. Managerial implications: Our data-driven decision-making framework may be used to produce a shortened season with 25-50\% fewer games while still producing an end-of-season ranking similar to that of the full season, had it been played.

math.OC↗

arXiv2023

W-boson mass anomaly and vacuum structure in vector dark matter model with a singlet scalar mediator

Motivated by the deviation of the W boson mass reported by the CDF collaboration, we study an extension of the Standard Model (SM) including a vector dark matter (VDM) candidate and a scalar mediator. In the model, the one-loop corrections induced by the new scalar, shift the W boson mass. We identify the parameter space of the model consistent with dark matter (DM) relic abundance, W mass boson anomaly, invisible Higgs decay at LHC, and direct detection of DM. It is shown that the W-mass anomaly can be explained for the large part of parameter space of VDM mass and scalar mediator mass between $100-124~\rm GeV$ by the model. We also investigate the renormalization group equations (RGE) at one-loop order for the model. We show that the contribution of new scalar mediator to RGE, guarantees positivity and vacuum stability of SM Higgs up to Planck scale.

hep-ph↗

arXiv2023

Nash-Bargaining-Based Models for Matching Markets: One-Sided and Two-Sided; Fisher and Arrow-Debreu

This paper addresses two deficiencies of models in the area of matching-based market design. The first arises from the recent realization that the most prominent solution that uses cardinal utilities, namely the Hylland-Zeckhauser (HZ) mechanism, is intractable; computation of even an approximate equilibrium is PPAD-complete. The second is the extreme paucity of models that use cardinal utilities. Our paper addresses both these issues by proposing Nash-bargaining-based matching market models. Since the Nash bargaining solution is captured by a convex program, efficiency follows. In addition, it possesses several desirable game-theoretic properties. Our approach yields a rich collection of models: for one-sided as well as two-sided markets, for Fisher as well as Arrow-Debreu settings, and for a wide range of utility functions, all the way from linear to Leontief. We give very fast implementations for these models using Frank-Wolfe and Cutting Plane algorithms. These help solve large instances with several thousand agents and goods in a matter of minutes on a PC, even for a one-sided matching market under piecewise-linear concave utility functions and a two-sided matching market under linear utility functions. In contrast, using HZ, going beyond even $n = 10$ is prohibitive. Several new ideas were needed, beyond the standard methods, to obtain these implementations. In particular, we present several lower bounding schemes, which not only help improve the convergence of our solution methods but also shed light on fairness properties of the Nash-bargaining-based models.

cs.GT↗

arXiv2023

RoboCup 2022 AdultSize Winner NimbRo: Upgraded Perception, Capture Steps Gait and Phase-based In-walk Kicks

Beating the human world champions by 2050 is an ambitious goal of the Humanoid League that provides a strong incentive for RoboCup teams to further improve and develop their systems. In this paper, we present upgrades of our system which enabled our team NimbRo to win the Soccer Tournament, the Drop-in Games, and the Technical Challenges in the Humanoid AdultSize League of RoboCup 2022. Strong performance in these competitions resulted in the Best Humanoid award in the Humanoid League. The mentioned upgrades include: hardware upgrade of the vision module, balanced walking with Capture Steps, and the introduction of phase-based in-walk kicks.

cs.RO↗

arXiv2022

State Estimation for Hybrid Locomotion of Driving-Stepping Quadrupeds

Fast and versatile locomotion can be achieved with wheeled quadruped robots that drive quickly on flat terrain, but are also able to overcome challenging terrain by adapting their body pose and by making steps. In this paper, we present a state estimation approach for four-legged robots with non-steerable wheels that enables hybrid driving-stepping locomotion capabilities. We formulate a Kalman Filter (KF) for state estimation that integrates driven wheels into the filter equations and estimates the robot state (position and velocity) as well as the contribution of driving with wheels to the above state. Our estimation approach allows us to use the control framework of the Mini Cheetah quadruped robot with minor modifications. We tested our approach on this robot that we augmented with actively driven wheels in simulation and in the real world. The experimental results are available at https://www.ais.uni-bonn.de/%7Ehosseini/se-dsq .

cs.RO↗

arXiv2021

Deepest Cuts for Benders Decomposition

Since its inception, Benders Decomposition (BD) has been successfully applied to a wide range of large-scale mixed-integer (linear) problems. The key element of BD is the derivation of Benders cuts, which are often not unique. In this paper, we introduce a novel unifying Benders cut selection technique based on a geometric interpretation of cut ``depth'', produce deepest Benders cuts based on $\ell_p$-norms, and study their properties. Specifically, we show that deepest cuts resolve infeasibility through minimal deviation (in a distance sense) from the incumbent point, are relatively sparse, and may produce optimality cuts even when classical Benders would require a feasibility cut. Leveraging the duality between separation and projection, we develop a Guided Projections Algorithm for producing deepest cuts while exploiting the combinatorial structure and decomposability of problem instances. We then propose a generalization of our Benders separation problem, which not only brings several well-known cut selection strategies under one umbrella, but also, when endowed with a homogeneous function, enjoys several properties of geometric separation problems. We show that, when the homogeneous function is linear, the separation problem takes the form of the Minimal Infeasible Subsystems (MIS) problem. As such, we provide systematic ways of selecting the normalization coefficients of the MIS method, and introduce a Directed Depth-Maximizing Algorithm for deriving these cuts. Inspired by the geometric interpretation of distance-based cuts and the repetitive nature of two-stage stochastic programs, we introduce a tailored algorithm to further facilitate deriving these cuts. Our computational experiments on various benchmark problems illustrate effectiveness of deepest cuts in reducing both computation time and number of Benders iterations, and producing high quality bounds at early iterations.

math.OC↗

arXiv2019

RoboCup 2019 AdultSize Winner NimbRo: Deep Learning Perception, In-Walk Kick, Push Recovery, and Team Play Capabilities

Individual and team capabilities are challenged every year by rule changes and the increasing performance of the soccer teams at RoboCup Humanoid League. For RoboCup 2019 in the AdultSize class, the number of players (2 vs. 2 games) and the field dimensions were increased, which demanded for team coordination and robust visual perception and localization modules. In this paper, we present the latest developments that lead team NimbRo to win the soccer tournament, drop-in games, technical challenges and the Best Humanoid Award of the RoboCup Humanoid League 2019 in Sydney. These developments include a deep learning vision system, in-walk kicks, step-based push-recovery, and team play strategies.

cs.RO↗

arXiv2019

NimbRo Robots Winning RoboCup 2018 Humanoid AdultSize Soccer Competitions

Over the past few years, the Humanoid League rules have changed towards more realistic and challenging game environments, which encourage teams to advance their robot soccer performances. In this paper, we present the software and hardware designs that led our team NimbRo to win the competitions in the AdultSize league -- including the soccer tournament, the drop-in games, and the technical challenges at RoboCup 2018 in Montreal. Altogether, this resulted in NimbRo winning the Best Humanoid Award. In particular, we describe our deep-learning approaches for visual perception and our new fully 3D printed robot NimbRo-OP2X.

cs.RO↗

arXiv2018

NimbRo-OP2X: Adult-sized Open-source 3D Printed Humanoid Robot

Humanoid robotics research depends on capable robot platforms, but recently developed advanced platforms are often not available to other research groups, expensive, dangerous to operate, or closed-source. The lack of available platforms forces researchers to work with smaller robots, which have less strict dynamic constraints or with simulations, which lack many real-world effects. We developed NimbRo-OP2X to address this need. At a height of 135 cm our robot is large enough to interact in a human environment. Its low weight of only 19 kg makes the operation of the robot safe and easy, as no special operational equipment is necessary. Our robot is equipped with a fast onboard computer and a GPU to accelerate parallel computations. We extend our already open-source software by a deep-learning based vision system and gait parameter optimisation. The NimbRo-OP2X was evaluated during RoboCup 2018 in Montréal, Canada, where it won all possible awards in the Humanoid AdultSize class.

cs.RO↗

arXiv2016

Real-Time Anomalous Behavior Detection and Localization in Crowded Scenes

In this paper, we propose an accurate and real-time anomaly detection and localization in crowded scenes, and two descriptors for representing anomalous behavior in video are proposed. We consider a video as being a set of cubic patches. Based on the low likelihood of an anomaly occurrence, and the redundancy of structures in normal patches in videos, two (global and local) views are considered for modeling the video. Our algorithm has two components, for (1) representing the patches using local and global descriptors, and for (2) modeling the training patches using a new representation. We have two Gaussian models for all training patches respect to global and local descriptors. The local and global features are based on structure similarity between adjacent patches and the features that are learned in an unsupervised way. We propose a fusion strategy to combine the two descriptors as the output of our system. Experimental results show that our algorithm performs like a state-of-the-art method on several standard datasets, but even is more time-efficient.

cs.CV↗

arXiv2015

Real-Time Anomaly Detection and Localization in Crowded Scenes

In this paper, we propose a method for real-time anomaly detection and localization in crowded scenes. Each video is defined as a set of non-overlapping cubic patches, and is described using two local and global descriptors. These descriptors capture the video properties from different aspects. By incorporating simple and cost-effective Gaussian classifiers, we can distinguish normal activities and anomalies in videos. The local and global features are based on structure similarity between adjacent patches and the features learned in an unsupervised way, using a sparse auto- encoder. Experimental results show that our algorithm is comparable to a state-of-the-art procedure on UCSD ped2 and UMN benchmarks, but even more time-efficient. The experiments confirm that our system can reliably detect and localize anomalies as soon as they happen in a video.

cs.CV↗