Miracle-dz/UniFAR-FTUs huggingface dataset:
Causal modifications of gravity and their observational bounds

Since general relativity is the unique theory of massless spin 2 particles at large distances, the most reasonable way to have significant modifications is to introduce one or more light scalars that mediate a new long-range force. Most existing studies of such scalars invoke models that exhibit some kind of"screening"at short distances to hide the force from solar system tests. However, as is well known, such modifications also exhibit superluminality, which can be interpreted as a form of acausality. In this work we explore explicitly subluminal and causal scalar field models. In particular, we study a conformally coupled scalar $\phi$, with a small coupling to matter to obey solar system bounds, and a non-canonical kinetic term $K(X)$ ($X=(\partial\phi)^2/2$) that obeys all subluminality constraints and is hyperbolic. We consider $K(X)$ that is canonical for small $X$, but beyond some nonlinear scale enters a new scaling regime of power $p$, with $1/2<p<1$ (the DBI kinetic term is the limit $p=1/2$ and a canonical scalar is $p=1$). As opposed to screening (and superluminality), this new force becomes more and more important in the regime of high densities (and subluminality). We then turn to the densest environments to put bounds on this new interaction. We compute constraints from precession in binary systems such as Hulse-Taylor, we compute corrections to neutron star hydrostatic equilibrium, and we compute power in radiation, both tensor mode corrections and the new scalar mode, which can be important during mergers.
Exploiting Reward Shifting in Value-Based Deep RL

In this work, we study the simple yet universally applicable case of reward shaping in value-based Deep Reinforcement Learning (DRL). We show that reward shifting in the form of the linear transformation is equivalent to changing the initialization of the Q -function in function approximation. Based on such an equivalence, we bring the key insight that a positive reward shifting leads to conservative exploitation, while a negative reward shifting leads to curiosity-driven exploration. Accordingly, conservative exploitation improves ofﬂine RL value estimation, and optimistic value estimation improves exploration for online RL. We validate our insight on a range of RL tasks and show its improvement over baselines: (1) In ofﬂine RL, the conservative exploitation leads to improved performance based on off-the-shelf algorithms; (2) In online continuous control, multiple value functions with different shifting constants can be used to tackle the exploration-exploitation dilemma for better sample efﬁciency; (3) In discrete control tasks, a negative reward shifting yields an improvement over the curiosity-based exploration method.
Blind and Channel-agnostic Equalization Using Adversarial Networks

Due to the rapid development of autonomous driving, the Internet of Things and streaming services, modern communication systems have to cope with varying channel conditions and a steadily rising number of users and devices. This, and the still rising bandwidth demands, can only be met by intelligent network automation, which requires highly flexible and blind transceiver algorithms. To tackle those challenges, we propose a novel adaptive equalization scheme, which exploits the prosperous advances in deep learning by training an equalizer with an adversarial network. The learning is only based on the statistics of the transmit signal, so it is blind regarding the actual transmit symbols and agnostic to the channel model. The proposed approach is independent of the equalizer topology and enables the application of powerful neural network based equalizers. In this work, we prove this concept in simulations of different―both linear and nonlinear―transmission channels and demonstrate the capability of the proposed blind learning scheme to approach the performance of non-blind equalizers. Furthermore, we provide a theoretical perspective and highlight the challenges of the approach.
Online Marker-free Extrinsic Camera Calibration using Person Keypoint Detections

Calibration of multi-camera systems, i.e. determining the relative poses between the cameras, is a prerequisite for many tasks in computer vision and robotics. Camera calibration is typically achieved using offline methods that use checkerboard calibration targets. These methods, however, often are cumbersome and lengthy, considering that a new calibration is required each time any camera pose changes. In this work, we propose a novel, marker-free online method for the extrinsic calibration of multiple smart edge sensors, relying solely on 2D human keypoint detections that are computed locally on the sensor boards from RGB camera images. Our method assumes the intrinsic camera parameters to be known and requires priming with a rough initial estimate of the camera poses. The person keypoint detections from multiple views are received at a central backend where they are synchronized, filtered, and assigned to person hypotheses. We use these person hypotheses to repeatedly solve optimization problems in the form of factor graphs. Given suitable observations of one or multiple persons traversing the scene, the estimated camera poses converge towards a coherent extrinsic calibration within a few minutes. We evaluate our approach in real-world settings and show that the calibration with our method achieves lower reprojection errors compared to a reference calibration generated by an offline method using a traditional calibration target.
Towards Healing the Blindness of Score Matching

Score-based divergences have been widely used in machine learning and statistics applications. Despite their empirical success, a blindness problem has been observed when using these for multi-modal distributions. In this work, we discuss the blindness problem and propose a new family of divergences that can mitigate the blindness problem. We illustrate our proposed divergence in the context of density estimation and report improved performance compared to traditional approaches.
Examining Large Pre-Trained Language Models for Machine Translation: What You Don’t Know about It

Pre-trained language models (PLMs) often take advantage of the monolingual and multilingual dataset that is freely available online to acquire general or mixed domain knowledge before deployment into specific tasks. Extra-large PLMs (xLPLMs) are proposed very recently to claim supreme performances over smaller-sized PLMs such as in machine translation (MT) tasks. These xLPLMs include Meta-AI’s wmt21-dense-24-wide-en-X (2021) and NLLB (2022). In this work, we examine if xLPLMs are absolutely superior to smaller-sized PLMs in fine-tuning toward domain-specific MTs. We use two different in-domain data of different sizes: commercial automotive in-house data and clinical shared task data from the ClinSpEn2022 challenge at WMT2022. We choose the popular Marian Helsinki as smaller sized PLM and two massive-sized Mega-Transformers from Meta-AI as xLPLMs.Our experimental investigation shows that 1) on smaller-sized in-domain commercial automotive data, xLPLM wmt21-dense-24-wide-en-X indeed shows much better evaluation scores using SacreBLEU and hLEPOR metrics than smaller-sized Marian, even though its score increase rate is lower than Marian after fine-tuning; 2) on relatively larger-size well prepared clinical data fine-tuning, the xLPLM NLLB tends to lose its advantage over smaller-sized Marian on two sub-tasks (clinical terms and ontology concepts) using ClinSpEn offered metrics METEOR, COMET, and ROUGE-L, and totally lost to Marian on Task-1 (clinical cases) on all official metrics including SacreBLEU and BLEU; 3) metrics do not always agree with each other on the same tasks using the same model outputs; 4) clinic-Marian ranked No.2 on Task- 1 (via SacreBLEU/BLEU) and Task-3 (via METEOR and ROUGE) among all submissions.
Visual Recognition with Deep Nearest Centroids

We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most classic and simple classifiers. Current deep models learn the classifier in a fully parametric manner, ignoring the latent data structure and lacking simplicity and explainability. DNC instead conducts nonparametric, case-based reasoning; it utilizes sub-centroids of training samples to describe class distributions and clearly explains the classification as the proximity of test data and the class sub-centroids in the feature space. Due to the distance-based nature, the network output dimensionality is flexible, and all the learnable parameters are only for data embedding. That means all the knowledge learnt for ImageNet classification can be completely transferred for pixel recognition learning, under the"pre-training and fine-tuning"paradigm. Apart from its nested simplicity and intuitive decision-making mechanism, DNC can even possess ad-hoc explainability when the sub-centroids are selected as actual training images that humans can view and inspect. Compared with parametric counterparts, DNC performs better on image classification (CIFAR-10, ImageNet) and greatly boots pixel recognition (ADE20K, Cityscapes), with improved transparency and fewer learnable parameters, using various network architectures (ResNet, Swin) and segmentation models (FCN, DeepLabV3, Swin). We feel this work brings fundamental insights into related fields.
Vaidya and Generalized Vaidya Solutions by Gravitational Decoupling

In this paper, we apply the gravitational decoupling method for dynamical systems in order to obtain a new type of solution that can describe a hairy dynamical black hole. We consider three cases of decoupling. The first one is the simplest and most well known when the mass function is the function only of space coordinate r. The second case is a Vaidya spacetime case when the mass function depends on time v . Finally, the third case represents the generalization of these two cases: the mass function is the function of both r and v. We also calculate the apparent horizon and singularity locations for all three cases.
Report of the Topical Group on Higgs Physics for Snowmass 2021: The Case for Precision Higgs Physics

A future Higgs Factory will provide improved precision on measurements of Higgs couplings beyond those obtained by the LHC, and will enable a broad range of investigations across the fields of fundamental physics, including the mechanism of electroweak symmetry breaking, the origin of the masses and mixing of fundamental particles, the predominance of matter over antimatter, and the nature of dark matter. Future colliders will measure Higgs couplings to a few per cent, giving a window to beyond the Standard Model (BSM) physics in the 1-10 TeV range. In addition, they will make precise measurements of the Higgs width, and characterize the Higgs self-coupling. This report details the work of the EF01 and EF02 working groups for the Snowmass 2021 study.
MVNet: Memory Assistance and Vocal Reinforcement Network for Speech Enhancement

Speech enhancement improves speech quality and promotes the performance of various downstream tasks. However, most current speech enhancement work was mainly devoted to improving the performance of downstream automatic speech recognition (ASR), only a relatively small amount of work focused on the automatic speaker verification (ASV) task. In this work, we propose a MVNet consisted of a memory assistance module which improves the performance of downstream ASR and a vocal reinforcement module which boosts the performance of ASV. In addition, we design a new loss function to improve speaker vocal similarity. Experimental results on the Libri2mix dataset show that our method outperforms baseline methods in several metrics, including speech quality, intelligibility, and speaker vocal similarity et al.
Direct observation of the Yb(4f13 6s2)F states and accurate determination of the YbF ionization energy

YbF has been identified as a molecule that can be used to investigate charge-parity symmetry violations that are beyond the Standard Model of particle physics. Cooling to sub-milli-Kelvin is advantageous for experiments that probe manifestations of these symmetry violations. One approach involves laser cooling of YbF via the A 2 P 1/2 -X 2 S + transition. However, it appears that cooling by means of this transition may be limited by the radiative loss of population from the cooling cycle. YbF has low-energy states that arise from the Yb + (4f 13 6s 2 )F - configuration. Recent theoretical calculations predict (Zhang et al J. Mol. Spectrsc. 386 11625 (2022)) that radiative decay from A 2 Π 1/2 to the 4f 13 6s 2 states occurs with a branching fraction of approximately 10 -3 . In the present study we have used dispersed laser induced fluorescence spectroscopy to the observe the lowest energy 4f 13 6s 2 states. These measurements were carried out using excitation of previously unobserved YbF transitions in the near UV spectral range. An accurate ionization energy (IE) for YbF is also reported. A two-color photoionization technique was used to determine the IE and observe the v + =0-3 vibrational levels of YbF + X 1 S + .
Multi-Modal Beam Prediction Challenge 2022: Towards Generalization

Beam management is a challenging task for millimeter wave (mmWave) and sub-terahertz communication systems, especially in scenarios with highly-mobile users. Leveraging external sensing modalities such as vision, LiDAR, radar, position, or a combination of them, to address this beam management challenge has recently attracted increasing interest from both academia and industry. This is mainly motivated by the dependency of the beam direction decision on the user location and the geometry of the surrounding environment -- information that can be acquired from the sensory data. To realize the promised beam management gains, such as the significant reduction in beam alignment overhead, in practice, however, these solutions need to account for important aspects. For example, these multi-modal sensing aided beam selection approaches should be able to generalize their learning to unseen scenarios and should be able to operate in realistic dense deployments. The"Multi-Modal Beam Prediction Challenge 2022: Towards Generalization"competition is offered to provide a platform for investigating these critical questions. In order to facilitate the generalizability study, the competition offers a large-scale multi-modal dataset with co-existing communication and sensing data collected across multiple real-world locations and different times of the day. In this paper, along with the detailed descriptions of the problem statement and the development dataset, we provide a baseline solution that utilizes the user position data to predict the optimal beam indices. The objective of this challenge is to go beyond a simple feasibility study and enable necessary research in this direction, paving the way towards generalizable multi-modal sensing-aided beam management for real-world future communication systems.
A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems

The traditional Machine Learning (ML) methodology requires to fragment the development and experimental process into disconnected iterations whose feedback is used to guide design or tuning choices. This methodology has multiple efﬁciency and scalability disadvantages, such as leading to spend signiﬁcant resources into the creation of multiple trial models that do not contribute to the ﬁnal solution. The presented work is based on the intuition that deﬁning ML models as modular and extensible artefacts allows to introduce a novel ML development methodology enabling the integration of multiple design and evaluation iterations into the continuous enrichment of a single unbounded intelligent system. We deﬁne a novel method for the generation of dynamic multitask ML models as a sequence of extensions and generalizations. We ﬁrst analyze the capabilities of the proposed method by using the standard ML empirical evaluation methodology. Finally, we propose a novel continuous development methodology that allows to dynamically extend a pre-existing multitask large-scale ML system while analyzing the properties of the proposed method extensions. This results in the generation of an ML model capable of jointly solving 124 image classiﬁcation tasks achieving state of the art quality with improved size and compute cost. the ﬁnal ML solution. Furthermore, it relies on signiﬁcant human intervention rather than aiming to automate any of the design or tuning exploration. We propose and demonstrate a methodology that integrates the ML design and the experimental evaluation iterations into the continual expansion of an arbitrary pre-existing ML model. The proposed ML methodology allows any design variation and model knowledge enrichment to be evaluated as an extension of the latest state of the dynamic ML system. Successful extensions can be integrated as base for future experimentation and further extensions. This methodology enables the creation of dynamic multitask systems at a scale that would not be feasible to achieve with the constraint of running parallel repetitions from a neutral start state.
Self-Supervised Attention Networks and Uncertainty Loss Weighting for Multi-Task Emotion Recognition on Vocal Bursts

—Vocal bursts play an important role in communicat- ing affect, making them valuable for improving speech emotion recognition. Here, we present our approach for classifying vocal bursts and predicting their emotional signiﬁcance in the ACII Affective Vocal Burst Workshop & Challenge 2022 (A-VB). We use a large self-supervised audio model as shared feature extractor and compare multiple architectures built on classiﬁer chains and attention networks, combined with uncertainty loss weighting strategies. Our approach surpasses the challenge baseline by a wide margin on all four tasks.
Modeling and Pricing Cyber Insurance – A Survey

The paper provides a comprehensive overview of modeling and pricing cyber insurance and includes clear and easily understandable explanations of the underlying mathematical concepts. We distinguish three main types of cyber risks: idiosyncratic, systematic, and systemic cyber risks. While for idiosyncratic and systematic cyber risks, classical actuarial and ﬁnancial mathematics appear to be well-suited, systemic cyber risks require more sophisticated approaches that capture both network and strategic interactions. In the context of pricing cyber insurance contracts, interdependence issues arise for both systematic and systemic cyber risks; classical actuarial valuation must be expanded to include more complex methodologies, e
Quantum Mechanics in Non-Inertial Frames with a Multi-Temporal Quantization Scheme: II) Non-Relativistic Particles

The non-relativistic version of the multi-temporal quantization scheme of relativistic particles in a family of non-inertial frames (see hep-th/0502194) is deﬁned. At the classical level the description of a family of non-rigid non-inertial frames, containing the standard rigidly linear accelerated and rotating ones, is given in the framework of parametrized Galilei theories. Then the multi-temporal quantization, in which the gauge variables, describing the non-inertial eﬀects, are not quantized but considered as c -number generalized times, is applied to non relativistic particles. It is shown that with a suitable ordering there is unitary evolution in all times and that, after the separation of the center of mass, it is still possible to identify the inertial bound states. The few existing results of quantization in rigid non-inertial frames are recovered as special cases.
Directions in General Relativity: What is the True Description of Charged Black Holes?

If string theory describes nature, then charged black holes are not described by the Reissner-Nordstrom solution. This solution must be modified to include a massive dilaton. In the limit of vanishing dilaton mass, the new solution can be found by a generalization of the Harrison transformation for the Einstein-Maxwell equations. These two solution generating transformations and the resulting black holes are compared. It is shown that the extremal black hole with massless dilaton can be viewed as the ``square root" of the extremal Reissner-Nordstrom solution. When the dilaton mass is included, extremal black holes are repulsive, and it is energetically favorable for them to bifurcate into smaller holes. (To appear in the Festschrift for Dieter Brill)
Three-Dimensional Imaging using a Novel Rotating Point Spread Function Imager

The capability to achieve three-dimensional (3D) imaging in a single snapshot is a highly coveted goal for the imaging community. To be able to extend the depth-offield while simultaneously encoding depth in the point spread function (PSF) itself allows an imaging solution which is less time consuming and less data intensive than the inefficient multiscan based conventional 3D imaging. Light-field cameras also achieve 3D imaging in a snapshot, but at the cost of greatly reduced resolution. Phase mask based depth encoding solutions have been proposed by other groups, but they all suffer from a relatively smaller depth-of-field. Recently there has been much progress in the field of live-cell imaging where intracellular molecules have been imaged with nano-meter (nm) resolution. Our rotating point spread function (RPSF) imager will allow for nm resolution in all three dimensions in a single snapshot over a much larger axial field depth. This will help indirectly in achieving a much better temporal resolution by means of 3D
A pr 2 00 6 Amplified Doppler shift observed in diffraction images as function of the COBE “ ether drift ” direction

We report results on an “one-way light path” laser diffraction experiment as a function of the laser beam alignment relative to the Earth’s velocity vector obtained by COBE measurements of the Doppler shift in the cosmic microwave background radiation (CMBR). An amplified Doppler shift is observed in the diffraction images, and the effect is compatible with a “dipole” speed of light anisotropy due to Earth’s motion relative to the “CMBR rest frame”, with an amplitude of δc/c̄ = 0.00123. This amplitude coincides with the value of the dipole temperature anisotropy δT/T̄ = 0.00123 of the CMBR obtained by COBE. Our results point out that it is not possible to neglect the preferred frame imposed by the cosmology and they are well described by the Ether Gauge Theory (an extension of the Lorentz’s ether theory) and it satisfies the cosmological time boundary condition.