Miracle-dz/UniFAR-FTUs huggingface dataset:
Utility of Genetic Algorithms for Solving Large-Scale Construction Time-Cost Trade-Off Problems

The Time/Cost Trade-off (TCT) problem has long been a popular optimization question for construction engineering and management researchers. The problem manifests itself as the opti- mization of total costs of construction projects that consist of indirect project costs and individual activity costs. The trade-off occurs as project duration and, as a result, indirect project costs de- crease with reduced individual activity duration. This reduction in individual activity duration is achieved by increasing resource allocation to individual activities, which increases their costs to completion. Historically, metaheuristic solutions have been applied to small scale problems due to computational complexities and requirements of larger networks. In this paper, we demonstrate that the metaheuristic approach is highly effective for solving large scale construction TCT problems. A custom Genetic Algorithm (GA) is developed and used to solve large benchmark networks of up to 630 variables with high levels of accuracy (<3% deviation) consistently using computational power of a personal computer in under ten minutes. The same method can also be used to solve larger net- works of up to 6,300 variables with reasonable accuracy (∼7% deviation) at the expense of longerprocessing times. A number of simple, yet effective, techniques that improve GA performance for TCT problems are demonstrated; the most effective of which is a novel problem encoding, based on weighted graphs, that enables the critical path problem to be partially solved for all candidate solutions a priori, thus significantly increasing fitness evaluation. Other improvements include parallel fitness evaluations, optimal algorithm parameters, and the addition of a stagnation criteria. We also present some guidelines of optimal algorithm parameter selection through a comprehensive parameter sweep and a computational demand profile analysis. Moreover, the methods proposed in this article are based on open source development projects that enable scalable solutions without significant development efforts. This information will be beneficial for other researchers in improving computational efficiency of their solution in addressing TCT problems.
Mobile Apps and Transportation: A Review of Smartphone Apps and A Study of User Response to Multimodal Traveler Information

Author(s): Shaheen, Susan; Martin, Elliot; Cohen, Adam; Musunuri, Apoorva; Bhattacharyya, Abhinav | Abstract: In recent years, technological and social forces have pushed smartphone applications (apps) from the fringe to the mainstream. Understanding the role of transportation apps in urban mobility is important for policy development and transportation planners. This study evaluates the role and impact of multimodal aggregators from a variety of perspectives, including a literature review; a review of the most innovative, disruptive, and highest-rated transportation apps; interviews with experts in the industry, and a user survey of former multimodal aggregator RideScout users. Between February and April 2016, researchers conducted interviews with experts to gain a stronger understanding about challenges and benefits of data sharing between private companies and public agencies. Key findings from the expert interviews include the critical need to protect user privacy; the potential to use data sharing to address integrated corridor and congestion management as well as various pricing strategies during peak hours; along with the potential benefits for improving coordination between the public and private sectors. In March 2016, researchers surveyed 130 people who had downloaded the RideScout app to evaluate attitudes and perceptions toward mobile apps, travel behavior, and modal shift. The goal was to enhance understanding of how the multimodal apps were impacting the transportation behavior. The survey did found that respondents used multimodal apps in ways that yielded travel that was less energy intensive and more supportive of public transit. Looking to the future, smartphone applications and more specifically multimodal aggregators, may offer the potential for transportation planners and policymakers to enhance their understanding of multimodal travel behavior, share data, enhance collaboration, and identify opportunities for public-private partnerships.
Local loss coefficients inside air cavity of ventilated pitched roofs

Pitched roofs with a ventilated air cavity to avoid snow melt and ensure dry conditions beneath the roofing are a widely used construction in northern parts of Europe and America. The purpose of this study has been to determine pressure losses at the inlet (eaves) and inside the air cavity consisting of friction losses and passing of tile battens. These results are necessary to increase the accuracy of ventilation calculations of pitched roofs. Laboratory measurements, numerical analysis as well as calculations by use of empirical expressions have been used in the study. A large difference in the local loss coefficients depending on the edge design and height of the tile batten was found. The local loss coefficients of the round-edged tile battens were approximately 40% lower than the local loss coefficients of the sharp-edged tile battens. Furthermore, the local loss factor increased by increasing height of the tile batten. The numerical analysis was found to reliably reproduce the results from the measurements.
Computational Characterization of Drag Reduction for Platooning Heavy Vehicles

This study uses Computational Fluid Dynamics to analyze the aerodynamic properties of multiple heavy vehicle groups in leader-follower configurations, referred to as platoons. The primary metric of interest is the resistive aerodynamic force experienced in the freestream direction, commonly known as drag. The computational model first was validated using a simplified car body for both single and multiple body simulations. After validation, three platooning topics were examined: two vehicle platoons, three vehicle platoons, and multiple geometry two vehicle platoons. It was discovered that at close distances the follower vehicle interferes with the formation of the leader vehicle wake, offering significant drag reduction for both vehicles. At larger distances, it was found that the follower vehicle experiences near constant benefit until the end of the lead vehicle slipstream, at which point the drag rapidly transitions to the single vehicle value. Upon analysis of three vehicle platoons, it was observed that the interior vehicle drag could be reduced below that of the outer vehicles at small spacings and thus larger platoons can offer more benefit per vehicle than their smaller counterparts. Upon investigation of platoons with multiple geometries, it was discovered that placing the least aerodynamic vehicle follow position offers the most potential for overall platoon drag reduction.
Engines for predictive work extraction from memoryful quantum stochastic processes

Quantum information-processing techniques enable work extraction from a system's inherently quantum features, in addition to the classical free energy it contains. Meanwhile, the science of computational mechanics affords tools for the predictive modeling of non-Markovian classical and quantum stochastic processes. We combine tools from these two sciences to develop a technique for predictive work extraction from non-Markovian stochastic processes with quantum outputs. We demonstrate that this technique can extract more work than non-predictive quantum work extraction protocols, on the one hand, and predictive work extraction without quantum information processing, on the other. We discover a phase transition in the efficacy of memory for work extraction from quantum processes, which is without classical precedent. Our work opens up the prospect of machines that harness environmental free energy in an essentially quantum, essentially time-varying form.
Optical absorption measurement of spin Berry curvature and spin Chern marker

In two-dimensional time-reversal symmetric topological insulators described by Dirac models, the Z2 topological invariant can be described by the spin Chern number. We present a linear response theory for the spin Berry curvature that integrates to the spin Chern number, and introduce its spectral function that can be measured at finite temperature by momentum- and spin-resolved circular dichroism, which may be achieved by pump-probe type of experiments using spin- and time-resolved ARPES. As a result, the sign of the Pfaffian of the Z2 invariant can be directly measured. A spin Chern number spectral function is further introduced from the optical spin current response, and is shown to be measurable from the spin-resolved opacity of two-dimensional materials under circularly polarized light, pointing to an optical measurement of the Z2 invariant and a frequency sum rule. The spin Chern number expressed in real space is known to yield a spin Chern marker, and we propose that it may be measurable from spin-resolved local heating rate caused by circularly polarized light. A nonlocal spin Chern marker is further proposed to characterize the quantum criticality near topological phase transitions, and is shown to be equivalent to an overlap between spin-selected Wannier states.
Differentially Private Stochastic Linear Bandits: (Almost) for Free

In this paper, we propose diﬀerentially private algorithms for the problem of stochastic linear bandits in the central, local and shuﬄed models. In the central model, we achieve almost the same regret as the optimal non-private algorithms, which means we get privacy for free. In particular, we achieve a regret of ˜ O ( √ T + 1 (cid:15) ) matching the known lower bound for private linear bandits, while the best previously known algorithm achieves ˜ O ( 1 (cid:15) √ T ) . In the local case, we achieve a regret of ˜ O ( 1 (cid:15) √ T ) which matches the non-private regret for constant (cid:15) , but suﬀers a regret penalty when (cid:15) is small. In the shuﬄed model, we also achieve regret of ˜ O ( √ T + 1 (cid:15) ) while the best previously known algorithm suﬀers a regret of ˜ O ( 1 (cid:15) T 3 / 5 ) . Our numerical evaluation validates our theoretical results.
Multi-View Vision-to-Geometry Knowledge Transfer for 3D Point Cloud Shape Analysis

—As two fundamental representation modalities of 3D objects, 2D multi-view images and 3D point clouds reﬂect shape information from different aspects of visual appearances and geometric structures. Unlike deep learning-based 2D multi-view image modeling, which demonstrates leading performances in various 3D shape analysis tasks, 3D point cloud-based geometric modeling still suffers from insufﬁcient learning capacity. In this paper, we innovatively construct a uniﬁed cross-modal knowledge transfer framework, which distills discriminative visual descriptors of 2D images into geometric descriptors of 3D point clouds. Technically, under a classic teacher-student learning paradigm, we propose multi-view vision-to-geometry distillation, consisting of a deep 2D image encoder as teacher and a deep 3D point cloud encoder as student. To achieve heterogeneous feature alignment, we further propose visibility-aware feature projection, through which per-point embeddings can be aggregated into multi-view geometric descriptors. Extensive experiments on 3D shape classiﬁcation, part segmentation, and unsupervised learning validate the superiority of our method. We will make the code and data publicly available.
Signed Link Representation in Continuous-Time Dynamic Signed Networks

Signed networks allow us to model bi-faceted relationships and interactions, such as friend/enemy, support/oppose, etc. These interactions are often temporal in real datasets, where nodes and edges appear over time. Learning the dynamics of signed networks is thus crucial to effectively predict the sign and strength of future links. Existing works model either signed networks or dynamic networks but not both together. In this work, we study dynamic signed networks where links are both signed and evolving with time. Our model learns a S igned link’s E volution using M emory modules and B alanced A ggregation (hence, the name SEMBA). Each node maintains two separate memory encodings for positive and negative interactions. On the arrival of a new edge, each interacting node aggregates this signed information with its memories while exploiting balance theory. Node embeddings are generated using updated memories, which are then used to train for multiple downstream tasks, including link sign prediction and link weight prediction. Our results show that SEMBA outperforms all the baselines on the task of sign prediction by achieving up to 8% increase in the AUC and upto 50% reduction in FPR. Results on the task of predicting signed weights show that SEMBA reduces the mean squared error by 9%, while achieving up to 69% reduction in the KL-divergence on the distribution of predicted signed weights.
Small-scale clumping of dark matter and the mean free path of ionizing photons at z = 6

Recently, the mean free path of ionizing photons in the z = 6 intergalactic medium (IGM) was measured to be very short, presenting a challenge to existing reionization models. At face value, the measurement can be interpreted as evidence that the IGM clumps on scales M ≲ 108 M ⊙, a key but largely untested prediction of the cold dark matter (CDM) paradigm. Motivated by this possibility, we study the role that the underlying dark matter cosmology plays in setting the z > 5 mean free path. We use two classes of models to contrast against the standard CDM prediction: (1) thermal relic warm dark matter (WDM), representing models with suppressed small-scale power; (2) an ultralight axion exhibiting a white noise-like power enhancement. Differences in the mean free path between the WDM and CDM models are subdued by pressure smoothing and the possible contribution of neutral islands to the IGM opacity. For example, comparing late reionization scenarios with a fixed volume-weighted mean neutral fraction of 20% at z = 6, the mean free path is 19 (45)% longer in a WDM model with mx = 3 (1) keV. The enhanced power in the axion-like model produces better agreement with the short mean free path measured at z = 6. However, drawing robust conclusions about cosmology is hampered by large uncertainties in the reionization process, extragalactic ionizing background, and thermal history of the Universe. This work highlights some key open questions about the IGM opacity during reionization.
Conic Frameworks Infinitesimal Rigidity

This paper introduces new structures called conic frameworks and their rigidity. They are composed by agents and a set of directed constraints between pairs of agents. When the structure cannot be flexed while preserving the constraints, it is said to be rigid. If only smooth deformations are considered a sufficient condition for rigidity is called infinitesimal rigidity. In conic frameworks, each agent $u$ has a spatial position $x_u$ and a clock offset represented by a bias $\beta_u$. If the constraint from Agent $u$ to Agent $w$ is in the framework, the pseudo-range from $u$ to $w$, defined as ${\left\lVert{x_u - x_w}\right\rVert} + \beta_w - \beta_u$, is set. Pseudo-ranges appear when measuring inter-agent distances using a Time-of-Arrival method. This paper completely characterizes infinitesimal rigidity of conic frameworks whose agents are in general position. Two characterizations are introduced: one for unidimensional frameworks, the other for multidimensional frameworks. They both rely on the graph of constraints and use a decoupling between space and bias variables. In multidimensional cases, this new conic paradigm sharply reduces the minimal number of constraints required to maintain a formation with respect to classical Two-Way Ranging methods.
Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration

Recently, deep-learning-based approaches have been widely studied for deformable image registration task. However, most efforts directly map the composite image representation to spatial transformation through the convolutional neural network, ignoring its limited ability to capture spatial correspondence. On the other hand, Transformer can better characterize the spatial relationship with attention mechanism, its long-range dependency may be harmful to the registration task, where voxels with too large distances are unlikely to be corresponding pairs. In this study, we propose a novel Deformer module along with a multi-scale framework for the deformable image registration task. The Deformer module is designed to facilitate the mapping from image representation to spatial transformation by formulating the displacement vector prediction as the weighted summation of several bases. With the multi-scale framework to predict the displacement fields in a coarse-to-fine manner, superior performance can be achieved compared with traditional and learning-based approaches. Comprehensive experiments on two public datasets are conducted to demonstrate the effectiveness of the proposed Deformer module as well as the multi-scale framework.
Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines

In this work we use Equal Opportunity (EO) doctrines from political philosophy to make explicit the normative judgements embedded in different conceptions of algorithmic fairness. We contrast formal EO approaches that narrowly focus on fair contests at discrete decision points, with substantive EO doctrines that look at people’s fair life chances more holistically over the course of a lifetime. We use this taxonomy to provide a moral interpretation of the impossibility results as the incompatibility between different conceptions of a fair contest — foward-facing versus backward-facing — when people do not have fair life chances. We use this result to motivate substantive conceptions of algorithmic fairness and outline two plausible fair decision procedures based on the luck egalitarian doctrine of EO, and Rawls’s principle of fair equality of opportunity.
Vision Transformers: State of the Art and Research Challenges

Transformers have achieved great success in natural language processing. Due to the powerful capability of self-attention mechanism in transformers, researchers develop the vision transformers for a variety of computer vision tasks, such as image recognition, object detection, image segmentation, pose estimation, and 3D reconstruction. This paper presents a comprehensive overview of the literature on different architecture designs and training tricks (including self-supervised learning) for vision transformers. Our goal is to provide a systematic review with the open research opportunities.
Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding

Conformer has proven to be effective in many speech processing tasks. It combines the benefits of extracting local dependencies using convolutions and global dependencies using self-attention. Inspired by this, we propose a more flexible, interpretable and customizable encoder alternative, Branchformer, with parallel branches for modeling various ranged dependencies in end-to-end speech processing. In each encoder layer, one branch employs self-attention or its variant to capture long-range dependencies, while the other branch utilizes an MLP module with convolutional gating (cgMLP) to extract local relationships. We conduct experiments on several speech recognition and spoken language understanding benchmarks. Results show that our model outperforms both Transformer and cgMLP. It also matches with or outperforms state-of-the-art results achieved by Conformer. Furthermore, we show various strategies to reduce computation thanks to the two-branch architecture, including the ability to have variable inference complexity in a single trained model. The weights learned for merging branches indicate how local and global dependencies are utilized in different layers, which benefits model designing.
Masked Surfel Prediction for Self-Supervised Point Cloud Learning

Masked auto-encoding is a popular and effective self-supervised learning approach to point cloud learning. However, most of the existing methods reconstruct only the masked points and overlook the local geometry information, which is also important to understand the point cloud data. In this work, we make the first attempt, to the best of our knowledge, to consider the local geometry information explicitly into the masked auto-encoding, and propose a novel Masked Surfel Prediction (MaskSurf) method. Specifically, given the input point cloud masked at a high ratio, we learn a transformer-based encoder-decoder network to estimate the underlying masked surfels by simultaneously predicting the surfel positions (i.e., points) and per-surfel orientations (i.e., normals). The predictions of points and normals are supervised by the Chamfer Distance and a newly introduced Position-Indexed Normal Distance in a set-to-set manner. Our MaskSurf is validated on six downstream tasks under three fine-tuning strategies. In particular, MaskSurf outperforms its closest competitor, Point-MAE, by 1.2\% on the real-world dataset of ScanObjectNN under the OBJ-BG setting, justifying the advantages of masked surfel prediction over masked point cloud reconstruction. Codes will be available at https://github.com/YBZh/MaskSurf.
Subtracting glitches from gravitational-wave detector data during the third LIGO-Virgo observing run

Data from ground-based gravitational-wave detectors contains numerous short-duration instrumental artifacts, called ‘glitches’. The high rate of these artifacts in turn results in a significant fraction of gravitational-wave signals from compact binary coalescences overlapping glitches. In LIGO-Virgo’s third observing run, ≈20% of gravitational-wave source candidates required some form of mitigation due to glitches. This was the first observing run where glitch subtraction was included as a part of LIGO-Virgo-KAGRA data analysis methods for a large fraction of detected gravitational-wave events. This work describes the methods to identify glitches, the decision process for deciding if mitigation was necessary, and the two algorithms, BayesWave and gwsubtract, that were used to model and subtract glitches. Through case studies of two events, GW190424_180648 and GW200129_065458, we evaluate the effectiveness of the glitch subtraction, compare the statistical uncertainties in the relevant glitch models, and identify potential limitations in these glitch subtraction methods. We finally outline the lessons learned from this first-of-its-kind effort for future observing runs.
Non-vanishing of geometric Whittaker coefficients for reductive groups

We prove that cuspidal automorphic D-modules have non-vanishing Whittaker coefficients, generalizing known results in the geometric Langlands program from GL_n to general reductive groups. The key tool is a microlocal interpretation of Whittaker coefficients. We establish various exactness properties in the geometric Langlands context that may be of independent interest. Specifically, we show Hecke functors are t-exact on the category of tempered D-modules, strengthening a classical result of Gaitsgory (with different hypotheses) for GL_n. We also show that Whittaker coefficient functors are t-exact for sheaves with nilpotent singular support. An additional consequence of our results is that the tempered, restricted geometric Langlands conjecture must be t-exact. We apply our results to show that for suitably irreducible local systems, Whittaker-normailzed Hecke eigensheaves are perverse sheaves that are irreducible on each connected component of Bun_G.