Miracle-dz/UniFAR-FTUs huggingface dataset:
On Why Affirmative Action May Never End and Why it Should

Successive welfare-maximizing governments must decide whether to implement an affirmative action policy. This affirmative action policy is purported to improve the performance distribution of a targeted group (minority or otherwise), although this improvement becomes trivial when the policy is implemented over a long period of time. Employers pay workers according to their expected productivity, but without (perfectly) observing whether they benefited from affirmative action or not. This has a depressing effect on the wages of non-beneficiaries (whether or not they belong to the targeted group), leading in turn to a feeling of injustice. We find that governments perpetually choose to implement an affirmative action policy, even though the overall feeling of injustice is worse than the purported beneficial effect on the performance of the targeted group (which becomes marginal over time). The explanation we propose is based on a novel moral hazard argument: Each government's actual policy decision is not (perfectly) observed by market participants and thus has no direct effect on wages. Governments therefore do not internalize the depressing effect of an affirmative action policy on wages when choosing to implement it. This outcome is in sharp contrast with a first-best, welfare-maximizing policy plan, in which governments choose to end affirmative action after a certain number of periods, when the purported improvement in the performance distribution of the targeted group becomes small enough.
Governance, Once Removed: Shareholder Rights Effectiveness vs. Expatriate Deployment

We investigate the impact of host-country shareholder rights protection on location and governance modes of Japanese foreign direct investment (JFDI). Agency theory predicts that strong shareholder rights protection in host countries should not increase location attractiveness nor impact entry and governance mode decisions, as foreign shareholders are outside the jurisdiction of host country laws. However, informal institutional mechanisms, including managerial bonding and isomorphic pressures, as predicted by institutional theory, may create indirect control over host country managers which reduces agency costs. Hence, we predict that higher shareholder rights protections will enhance a potential host country’s JFDI attractiveness, and that subsidiaries established in host countries with higher shareholder rights protection will employ fewer Japanese expatriates. We find support for our predictions based on a sample of JFDI established between 1986 and 2012.
Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion

BERT-based information retrieval models are expensive, in both time (query latency) and computational resources (energy, hardware cost), making many of these models impractical especially under resource constraints. The reliance on a query encoder that only performs tokenization and on the pre-processing of passage representations at indexing, has allowed the recently proposed TILDE method to overcome the high query latency issue typical of BERT-based models. This however is at the expense of a lower effectiveness compared to other BERT-based re-rankers and dense retrievers. In addition, the original TILDE method is characterised by indexes with a very high memory footprint, as it expands each passage into the size of the BERT vocabulary. In this paper, we propose TILDEv2, a new model that stems from the original TILDE but that addresses its limitations. TILDEv2 relies on contextualized exact term matching with expanded passages. This requires to only store in the index the score of tokens that appear in the expanded passages (rather than all the vocabulary), thus producing indexes that are 99% smaller than those of TILDE. This matching mechanism also improves ranking effectiveness by 24%, without adding to the query latency. This makes TILDEv2 the state-of-the-art passage re-ranking method for CPU-only environments, capable of maintaining query latency below 100ms on commodity hardware.
Using Multilevel Circulant Matrix Approximation to Speed Up Kernel Logistic Regression

Kernel logistic regression (KLR) is a conventional nonlinear classiﬁer in machine learning. With the explosive growth of data size, the storage and computation of large dense kernel matrices is a major challenge in scaling KLR to massive datasets. This paper proposes an approximate Newton method for e ﬃ ciently solving large-scale KLR problems by exploiting the storage and computing advantages of multilevel circulant matrix(MCM). We reduce the storage space of the Newton method to O ( n ) by approximating the kernel matrix K with the MCM K q , where n is the number of training instances and q is the order of the MCM K q . And by approximating the coe ﬃ cient matrix of the Newton equation as an MCM, we can reduce the computational complexity in the Newton direction to O ( n log n ). Our method can run in linearithmic time at each iteration by using the multidimensional fast Fourier transform (mFFT). Experimental results on some large-scale binary and multi-classiﬁcation problems show that our method enables KLR to scale to large scale problems with less memory consumption and less time without sacriﬁcing test accuracy.
IIB String Theory and Sequestered Inflation

We develop sequestered inflation models, where inflation occurs along flat directions in supergravity models derived from type IIB string theory. It is compactified on a T6Z2×Z2 orientifold with generalized fluxes and O3/O7‐planes. At Step I, we use flux potentials which 1) satisfy tadpole cancellation conditions and 2) have supersymmetric Minkowski vacua with flat direction(s). The 7 moduli are split into heavy and massless Goldstone multiplets. At Step II we add a nilpotent multiplet and uplift the flat direction(s) of the type IIB string theory to phenomenological inflationary plateau potentials: α‐attractors with 7 discrete values 3α=1,2,3,…,7 . Their cosmological predictions are determined by the hyperbolic geometry inherited from string theory. The masses of the heavy fields and the volume of the extra dimensions change during inflation, but this does not affect the inflationary dynamics.
Property-Based Testing in Simulation for Verifying Robot Action Execution in Tabletop Manipulation

An important prerequisite for the reliability and robustness of a service robot is ensuring the robot’s correct behavior when it performs various tasks of interest. Extensive testing is one established approach for ensuring behavioural correctness; this becomes even more important with the integration of learning-based methods into robot software architectures, as there are often no theoretical guarantees about the performance of such methods in varying scenarios. In this paper, we aim towards evaluating the correctness of robot behaviors in tabletop manipulation through automatic generation of simulated test scenarios in which a robot assesses its performance using property-based testing. In particular, key properties of interest for various robot actions are encoded in an action ontology and are then verified and validated within a simulated environment. We evaluate our framework with a Toyota Human Support Robot (HSR) which is tested in a Gazebo simulation. We show that our framework can correctly and consistently identify various failed actions in a variety of randomised tabletop manipulation scenarios, in addition to providing deeper insights into the type and location of failures for each designed property.
Holographic Chiral Algebra: Supersymmetry, Infinite Ward Identities, and Shadows

Celestial holography promisingly reformulates the scattering amplitude holographically in terms of celestial conformal field theory living at null infinity. Recently, an infinite-dimensional symmetry algebra was discovered in Einstein-Yang-Mills theory. The starting point in the derivation is the celestial OPE of two soft currents, and the key ingredient is the summation of SL(2,R) descendants in OPE. In this paper, we consider the supersymmetric Einstein-Yang-Mills theory and obtain the supersymmetric extension of the holographic symmetry algebra. Furthermore, we derive infinitely many Ward identities associated with the infinite soft currents which generate the holographic symmetry algebra. This is realized by considering the OPE between a soft symmetry current and a hard operator, and then summing over its SL(2,R) descendants. These Ward identities reproduce the known Ward identities corresponding to leading, sub-leading, and sub-sub-leading soft graviton theorem as well as the leading and sub-leading soft gluon theorem. By performing shadow transformations, we also obtain infinitely many shadow Ward identities, including the stress tensor Ward identities for sub-leading soft graviton. Finally, we use our procedure to discuss the corrections to Ward identities in effective field theory, and reproduce the corrections to soft theorems at subsub-leading order for graviton and sub-leading order for photon. For this aim, we derive a general formula for leading celestial OPE arising from cubic interactions of three spinning massless particles. Our formalism thus provides a unified framework for understanding the Ward identities in celestial conformal field theory, or equivalently the soft theorems in scattering amplitude. ar X iv :2 10 8. 08 79 9v 1 [ he pth ] 1 9 A ug 2 02 1
SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation

Directly regressing all 6 degrees-of-freedom (6DoF) for the object pose (i.e. the 3D rotation and translation) in a cluttered environment from a single RGB image is a challenging problem. While end-to-end methods have recently demonstrated promising results at high efficiency, they are still inferior when compared with elaborate PnP/RANSAC-based approaches in terms of pose accuracy. In this work, we address this shortcoming by means of a novel reasoning about self-occlusion, in order to establish a two-layer representation for 3D objects which considerably enhances the accuracy of end-to-end 6D pose estimation. Our framework, named SO-Pose, takes a single RGB image as input and respectively generates 2D-3D correspondences as well as self-occlusion information harnessing a shared encoder and two separate decoders. Both outputs are then fused to directly regress the 6DoF pose parameters. Incorporating cross-layer consistencies that align correspondences, self-occlusion and 6D pose, we can further improve accuracy and robustness, surpassing or rivaling all other state-of-the-art approaches on various challenging datasets.
Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, finding the optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-and-error process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows per application. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms for each component. To optimize the workflow per application, we employ automated machine learning using a random search and ensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1) liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77); 5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis (0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer's disease (0.87); and 12) head and neck cancer (0.84). We show that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performs similar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automatically optimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications. To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework, and the code to reproduce this study.
Development of a Clinical Chemical Exchange Saturation Transfer MR fingerprinting (CEST-MRF) Pulse Sequence and Reconstruction for Brain Tumor Quantification

Purpose: To develop a clinical chemical exchange saturation transfer magnetic resonance fingerprinting (CEST-MRF) pulse sequence and reconstruction method. Methods: The CEST-MRF pulse sequence was modified to conform to hardware limits on clinical scanners while keeping scan time ≤ 2 minutes. The measured data was reconstructed using a deep reconstruction network (DRONE) to yield the water relaxation and chemical exchange parameters. The feasibility of the 6 parameter DRONE reconstruction was tested in simulations in a digital brain phantom. A healthy subject was scanned with the CEST-MRF sequence and a conventional MRF sequence for comparison. The reproducibility was assessed via test-retest experiments and the concordance correlation coefficient (CCC) calculated for white matter (WM) and grey matter (GM). The clinical utility of CEST-MRF was demonstrated in a brain metastasis patient in comparison to standard clinical imaging sequences. The tumor was segmented into edema, solid core and necrotic core regions and the CEST-MRF values compared to the contra-lateral side. Results: The 6 parameter DRONE reconstruction of the digital phantom yielded a mean absolute error of ≤ 6% for all parameters. The CEST-MRF parameters were in good agreement with those from a conventional MRF sequence and previous studies in the literature. The mean CCC for all 6 parameters was 0.79±0.02 in WM and 0.63±0.03 in GM. The CEST-MRF values in nearly all tumor regions were significantly different (p=0.001) from each other and the contra-lateral side. Conclusion: The clinical CEST-MRF sequence provides a method for fast simultaneous quantification of multiple tissue parameters in pathologies.
Computing Steiner Trees using Graph Neural Networks

Graph neural networks have been successful in many learning problems and real-world applications. A recent line of research explores the power of graph neural networks to solve combinatorial and graph algorithmic problems such as subgraph isomorphism, detecting cliques, and the traveling salesman problem. However, many NP-complete problems are as of yet unexplored using this method. In this paper, we tackle the Steiner Tree Problem. We employ four learning frameworks to compute low cost Steiner trees: feed-forward neural networks, graph neural networks, graph convolutional networks, and a graph attention model. We use these frameworks in two fundamentally different ways: 1) to train the models to learn the actual Steiner tree nodes, 2) to train the model to learn good Steiner point candidates to be connected to the constructed tree using a shortest path in a greedy fashion. We illustrate the robustness of our heuristics on several random graph generation models as well as the SteinLib data library. Our finding suggests that the out-of-the-box application of GNN methods does worse than the classic 2-approximation method. However, when combined with a greedy shortest path construction, it even does slightly better than the 2-approximation algorithm. This result sheds light on the fundamental capabilities and limitations of graph learning techniques on classical NP-complete problems.
Tropical cyclone size is strongly limited by the Rhines scale: experiments with a barotropic model

Recent work found evidence using aquaplanet experiments that tropical cyclone (TC) size on Earth is limited by the Rhines scale, which depends on the planetary vorticity gradient, β. This study aims to examine how the Rhines scale limits the size of an individual TC. The traditional Rhines scale is first re-expressed as a Rhines speed to characterize how the effect of β varies with radius in a vortex whose wind profile is known. The framework is used to define the vortex Rhines scale, which is the transition radius that divides the vortex into a vortex-dominant region at smaller radii, where the axisymmetric circulation is steady, and a wave-dominant region at larger radii, where the circulation stimulates planetary Rossby waves and dissipates. Experiments are performed using a simple barotropic model on a β-plane initialized with a TC-like axisymmetric vortex defined using a recently-developed theoretical TC wind profile model. β and initial vortex size are each systematically varied to investigate the detailed responses of the TC-like vortex to β. Results show that the vortex shrinks towards an equilibrium size that closely follows the vortex Rhines scale. A larger initial vortex relative to its vortex Rhines scale will shrink faster. The shrinking timescale is well-described by the vortex Rhines timescale, which is defined as the overturning timescale of the circulation at the vortex Rhines scale and is shown to be directly related to the Rossby wave group velocity. The relationship between our idealized results and the real Earth is discussed. Our results may generalize to other eddy circulations, such as the extratropical cyclone.
GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network

Panoptic segmentation as an integrated task of both static environmental understanding and dynamic object identification, has recently begun to receive broad research interest. In this paper, we propose a new computationally efficient LiDAR based panoptic segmentation framework, called GP-S3Net. GP-S3Net is a proposal-free approach in which no object proposals are needed to identify the objects in contrast to conventional two-stage panoptic systems, where a detection network is incorporated for capturing instance information. Our new design consists of a novel instance-level network to process the semantic results by constructing a graph convolutional network to identify objects (foreground), which later on are fused with the back-ground classes. Through the fine-grained clusters of the foreground objects from the semantic segmentation back-bone, over-segmentation priors are generated and subsequently processed by 3D sparse convolution to embed each cluster. Each cluster is treated as a node in the graph and its corresponding embedding is used as its node feature. Then a GCNN predicts whether edges exist between each cluster pair. We utilize the instance label to generate ground truth edge labels for each constructed graph in order to supervise the learning. Extensive experiments demonstrate that GP-S3Net outperforms the current state-of-the-art approaches, by a significant margin across available datasets such as, nuScenes and SemanticPOSS, ranking 1st on the competitive public SemanticKITTI leaderboard upon publication.
Neural Operator: Learning Maps Between Function Spaces

The classical development of neural networks has primarily focused on learning mappings between finite dimensional Euclidean spaces or finite sets. We propose a generalization of neural networks to learn operators, termed neural operators, that map between infinite dimensional function spaces. We formulate the neural operator as a composition of linear integral operators and nonlinear activation functions. We prove a universal approximation theorem for our proposed neural operator, showing that it can approximate any given nonlinear continuous operator. The proposed neural operators are also discretization-invariant, i.e., they share the same model parameters among different discretization of the underlying function spaces. Furthermore, we introduce four classes of efficient parameterization, viz., graph neural operators, multi-pole graph neural operators, low-rank neural operators, and Fourier neural operators. An important application for neural operators is learning surrogate maps for the solution operators of partial differential equations (PDEs). We consider standard PDEs such as the Burgers, Darcy subsurface flow, and the Navier-Stokes equations, and show that the proposed neural operators have superior performance compared to existing machine learning based methodologies, while being several orders of magnitude faster than conventional PDE solvers.
Cost-Efficient RIS-Aided Channel Estimation via Rank-One Matrix Factorization

A reconfigurable intelligent surface (RIS) consists of massive meta elements, which can improve the performance of future wireless communication systems. Existing RIS-aided channel estimation methods try to estimate the cascaded channel directly, incurring high computational and training overhead especially when the number of elements of RIS is extremely large. In this letter, we propose a cost-efficient channel estimation method via rank-one matrix factorization (MF). Specifically, if the RIS is employed near base station (BS), it is found that the RIS-aided channel can be factorized into a product of low-dimensional matrices. To estimate these factorized matrices, we propose alternating minimization and gradient descent approaches to obtain the near optimal solutions. Compared to directly estimating the cascaded channel, the proposed MF method reduces training overhead substantially. Finally, the numerical simulations show the effectiveness of the proposed MF method.
NB QC-LDPC Coded QAM Signals with Optimized Mapping: Bounds and Simulation Results

This paper studies speciﬁc properties of nonbinary low-density parity-check (NB LDPC) codes when used in coded modulation systems. The paper is focused on the practically important NB LDPC codes over extensions of the Galois ﬁeld GF (2 m ) with m ≤ 6 used with QAM signaling. Performance of NB QC LDPC coded transmission strongly depends on mapping of nonbinary symbols to signal constellation points. We obtain a random coding bound on the maximum-likelihood decoding error probability for an ensemble of random irregular NB LDPC codes used with QAM signaling for speciﬁc symbol-to-signal point mappings. This bound is based on the ensemble average squared Euclidean distance spectra derived for these mappings. The simulation results for the belief-propagation decoding in the coded modulation schemes with the NB quasi-cyclic (QC)-LDPC codes under different mappings are given. Comparisons with the optimized binary QC-LDPC codes in the WiFi and 5G standards, as well as with the new bound, are performed.