Miracle-dz/UniFAR-FTUs huggingface dataset:
NoRA: A Tensor Network Ansatz for Volume-Law Entangled Equilibrium States of Highly Connected Hamiltonians

Motivated by the ground state structure of quantum models with all-to-all interactions such as mean-field quantum spin glass models and the Sachdev-Ye-Kitaev (SYK) model, we propose a tensor network architecture which can accomodate volume law entanglement and a large ground state degeneracy. We call this architecture the non-local renormalization ansatz (NoRA) because it can be viewed as a generalization of MERA, DMERA, and branching MERA networks with the constraints of spatial locality removed. We argue that the architecture is potentially expressive enough to capture the entanglement and complexity of the ground space of the SYK model, thus making it a suitable variational ansatz, but we leave a detailed study of SYK to future work. We further explore the architecture in the special case in which the tensors are random Clifford gates. Here the architecture can be viewed as the encoding map of a random stabilizer code. We introduce a family of codes inspired by the SYK model which can be chosen to have constant rate and linear distance at the cost of some high weight stabilizers. We also comment on potential similarities between this code family and the approximate code formed from the SYK ground space.
Probabilistic Shaping for High-Speed Unamplified IM/DD Systems With an O-Band EML

Probabilistic constellation shaping has been used in long-haul optically amplified coherent systems for its capability to approach the Shannon limit and realize fine rate granularity. The availability of high-bandwidth optical-electronic components and the previously mentioned advantages have invigorated researchers to explore probabilistic shaping (PS) in intensity-modulation and direct-detection (IM/DD) systems. This article presents an extensive comparison of uniform 8-ary pulse amplitude modulation (PAM) with PS PAM-8 using cap and cup Maxwell-Boltzmann (MB) distributions as well as MB distributions of different Gaussian orders. We report that in the presence of linear equalization, PS-PAM-8 outperforms uniform PAM-8 in terms of bit error ratio, achievable information rate and operational net bit rate indicating that cap-shaped PS-PAM-8 shows high tolerance against nonlinearities. In this paper, we have focused our investigations on O-band electro-absorption modulated laser unamplified IM/DD systems, which are operated close to the zero dispersion wavelength.
Language Models can Solve Computer Tasks

Agents capable of carrying out general tasks on a computer can improve efficiency and productivity by automating repetitive tasks and assisting in complex problem-solving. Ideally, such agents should be able to solve new computer tasks presented to them through natural language commands. However, previous approaches to this problem require large amounts of expert demonstrations and task-specific reward functions, both of which are impractical for new tasks. In this work, we show that a pre-trained large language model (LLM) agent can execute computer tasks guided by natural language using a simple prompting scheme where the agent Recursively Criticizes and Improves its output (RCI). The RCI approach significantly outperforms existing LLM methods for automating computer tasks and surpasses supervised learning (SL) and reinforcement learning (RL) approaches on the MiniWoB++ benchmark. We compare multiple LLMs and find that RCI with the InstructGPT-3+RLHF LLM is state-of-the-art on MiniWoB++, using only a handful of demonstrations per task rather than tens of thousands, and without a task-specific reward function. Furthermore, we demonstrate RCI prompting's effectiveness in enhancing LLMs' reasoning abilities on a suite of natural language reasoning tasks, outperforming chain of thought (CoT) prompting with external feedback. We find that RCI combined with CoT performs better than either separately. Our code can be found here: https://github.com/posgnu/rci-agent.
Electrooptical effects in silicon

A numerical Kramers-Kronig analysis is used to predict the refractive-index perturbations produced in crystalline silicon by applied electric fields or by charge carriers. Results are obtained over the 1.0-2.0 \mu m optical wavelength range. The analysis makes use of experimental electroabsorption spectra and impurity-doping spectra taken from the literature. For electrorefraction at the indirect gap, we find \Delta n = 1.3 \times 10^{5} at \lambda = 1.07 \mu m when E = 10^{5} V/cm, while the Kerr effect gives \Delta n = 10^{-6} at that field strength. The charge-carrier effects are larger, and a depletion or injection of 1018carriers/cm3produces an index change of \pm1.5 \times 10^{-3} at \lambda = 1.3 \mu m.
On a nonlinear Robin problem with an absorption term on the boundary and $L^1$ data

We deal with existence and uniqueness of nonnegative solutions to \begin{equation*} \left\{ \begin{array}{l} -\Delta u = f(x) \text{ in }\Omega, \frac{\partial u}{\partial \nu} + \lambda(x) u = \frac{g(x)}{u^\eta} \text{ on } \partial\Omega, \end{array} \right. \end{equation*} where $\eta\ge 0$ and $f,\lambda$ and $g$ are nonnegative integrable functions. The set $\Omega\subset\mathbb{R}^N (N>2)$ is open and bounded with smooth boundary and $\nu$ denotes its unit outward normal vector. More generally, we handle equations driven by monotone operators of $p$-Laplacian type jointly with nonlinear boundary conditions. We prove existence of an entropy solution and check that this solution is unique under natural assumptions. Among other features, we study the regularizing effect given to the solution by both the absorption and the nonlinear boundary term.
Electromagnetic fields in compact binaries: A post-Newtonian approach

Galactic binaries, and notably double white dwarfs systems, will be a prominent source for the future LISA and Einstein Telescope detectors. Contrarily to the black holes observed by the current LIGO-Virgo-KAGRA network, such objects bear intense magnetic fields, that are naturally expected to leave some imprints on the gravitational wave emission. The purpose of this work is thus to study those imprints within the post-Newtonian (PN) framework, particularly adapted to double white dwarfs systems. To this end, we construct an effective action that takes into account the whole electromagnetic structure of a star, and then specify it to dipolar order. With this action at hand, we compute the acceleration and Noetherian quantities for generic electric and magnetic dipoles, at a relative 2PN order. Finally, focusing on physically relevant systems, we show that the magnetic effects on the orbital frequency, energy and angular momentum is significant, confirming previous works conclusions.
A DG-VEM method for the dissipative wave equation

A novel space-time discretization for the (linear) scalar-valued dissipative wave equation is presented. It is a structured approach, namely, the discretization space is obtained tensorizing the Virtual Element (VE) discretization in space with the Discontinuous Galerkin (DG) method in time. As such, it combines the advantages of both the VE and the DG methods. The proposed scheme is implicit and it is proved to be unconditionally stable and accurate in space and time.
A Quantization Procedure for Nonlinear Pricing with an Application to Electricity Markets

We consider a revenue maximization model, in which a company aims at designing a menu of contracts, given a population of customers. A standard approach consists in constructing an incentive-compatible continuum of contracts, i.e., a menu composed of an infinite number of contracts, where each contract is especially adapted to an infinitesimal customer, taking his type into account. Nonetheless, in many applications, the company is constrained to offering a limited number of contracts. We show that this question reduces to an optimal quantization problem, similar to McEneaney's pruning problem that appeared in the max-plus based numerical methods in optimal control. We develop a new quantization algorithm, which, given an initial menu of contracts, iteratively prunes the less important contracts, to construct an implementable menu of the desired cardinality, while minimizing the revenue loss. We apply this algorithm to solve a pricing problem with price-elastic demand, originating from the electricity retail market. Numerical results show an improved performance by comparison with earlier pruning algorithms.
Cylindrical radio waves

The formulas required for simple accurate numerical evaluation of the radiation patterns from slots on large circular cylinders are derived. From the usual harmonic series, integral representations are obtained. These lead to either the residue series via the Watson transformations or to the geometrical-optics representations via a saddle-point evaluation. Considerable attention is given to the calculation of the residue series constants so as to obtain sufficiently accurate numerical values for a wide range of circumference to wavelength ratios. Very good numerical agreement is shown between the representations derived here and results previously obtained from the harmonic series. The physical significance of the various representations is discussed and compared.
The secret of immersion: actor driven camera movement generation for auto-cinematography

Immersion plays a vital role when designing cinematic creations, yet the difficulty in immersive shooting prevents designers to create satisfactory outputs. In this work, we analyze the specific components that contribute to cinematographic immersion considering spatial, emotional, and aesthetic level, while these components are then combined into a high-level evaluation mechanism. Guided by such a immersion mechanism, we propose a GAN-based camera control system that is able to generate actor-driven camera movements in the 3D virtual environment to obtain immersive film sequences. The proposed encoder-decoder architecture in the generation flow transfers character motion into camera trajectory conditioned on an emotion factor. This ensures spatial and emotional immersion by performing actor-camera synchronization physically and psychologically. The emotional immersion is further strengthened by incorporating regularization that controls camera shakiness for expressing different mental statuses. To achieve aesthetic immersion, we make effort to improve aesthetic frame compositions by modifying the synthesized camera trajectory. Based on a self-supervised adjustor, the adjusted camera placements can project the character to the appropriate on-frame locations following aesthetic rules. The experimental results indicate that our proposed camera control system can efficiently offer immersive cinematic videos, both quantitatively and qualitatively, based on a fine-grained immersive shooting. Live examples are shown in the supplementary video.
Small-scale clustering of BOSS galaxies: dependence on luminosity, colour, age, stellar mass, specific star formation rate, and other properties

We measure and analyse galaxy clustering and the dependence on luminosity, colour, age, stellar mass, and specific star formation rate using Baryon Oscillation Spectroscopic Survey (BOSS) galaxies at 0.48 < z < 0.62. We fit the monopole and quadrupole moments of the two-point correlation function and its projection on scales of 0.1–60.2 h−1 Mpc, after having split the catalogue in a variety of ways. We find that the clustering dependence is consistent with previous well-established results showing the broad trends expected: For example, that brighter, redder, older, more massive and quenched galaxies are more strongly clustered. We also investigate the dependence on additional parameters previously derived from stellar population synthesis model fits to the spectra. We find that galaxy clustering depends on look-back formation time at a low level, while it has little dependence on metallicity. To understand the physics behind these trends, we fit the clustering with a simulation-based emulator to simultaneously model cosmology and galaxy bias using a halo occupation distribution framework. After marginalizing parameters determining the background cosmology, galaxy bias, and a scaling parameter to decouple halo velocity field, we find that the growth rate of large-scale structure as determined by the redshift space distortions is consistent with previous analysis using the full sample, and we do not find evidence that cosmological constraints depend systematically on galaxy selection. This demonstrates that cosmological inference using small-scale clustering measurements is robust to changes in the catalogue selection.
Have it your way: Individualized Privacy Assignment for DP-SGD

When training a machine learning model with differential privacy, one sets a privacy budget. This budget represents a maximal privacy violation that any user is willing to face by contributing their data to the training set. We argue that this approach is limited because different users may have different privacy expectations. Thus, setting a uniform privacy budget across all points may be overly conservative for some users or, conversely, not sufficiently protective for others. In this paper, we capture these preferences through individualized privacy budgets. To demonstrate their practicality, we introduce a variant of Differentially Private Stochastic Gradient Descent (DP-SGD) which supports such individualized budgets. DP-SGD is the canonical approach to training models with differential privacy. We modify its data sampling and gradient noising mechanisms to arrive at our approach, which we call Individualized DP-SGD (IDP-SGD). Because IDP-SGD provides privacy guarantees tailored to the preferences of individual users and their data points, we find it empirically improves privacy-utility trade-offs.
Interaction Replica: Tracking Human-Object Interaction and Scene Changes From Human Motion

Our world is not static and humans naturally cause changes in their environments through interactions, e.g., opening doors or moving furniture. Modeling changes caused by humans is essential for building digital twins, e.g., in the context of shared physical-virtual spaces (metaverses) and robotics. In order for widespread adoption of such emerging applications, the sensor setup used to capture the interactions needs to be inexpensive and easy-to-use for non-expert users. I.e., interactions should be captured and modeled by simple ego-centric sensors such as a combination of cameras and IMU sensors, not relying on any external cameras or object trackers. Yet, to the best of our knowledge, no work tackling the challenging problem of modeling human-scene interactions via such an ego-centric sensor setup exists. This paper closes this gap in the literature by developing a novel approach that combines visual localization of humans in the scene with contact-based reasoning about human-scene interactions from IMU data. Interestingly, we can show that even without visual observations of the interactions, human-scene contacts and interactions can be realistically predicted from human pose sequences. Our method, iReplica (Interaction Replica), is an essential first step towards the egocentric capture of human interactions and modeling of dynamic scenes, which is required for future AR/VR applications in immersive virtual universes and for training machines to behave like humans. Our code, data and model are available on our project page at http://virtualhumans.mpi-inf.mpg. de/ireplica/
Semi-Parametric Inducing Point Networks and Neural Processes

We introduce semi-parametric inducing point networks (SPIN), a general-purpose architecture that can query the training set at inference time in a compute-efficient manner. Semi-parametric architectures are typically more compact than parametric models, but their computational complexity is often quadratic. In contrast, SPIN attains linear complexity via a cross-attention mechanism between datapoints inspired by inducing point methods. Querying large training sets can be particularly useful in meta-learning, as it unlocks additional training signal, but often exceeds the scaling limits of existing models. We use SPIN as the basis of the Inducing Point Neural Process, a probabilistic model which supports large contexts in meta-learning and achieves high accuracy where existing models fail. In our experiments, SPIN reduces memory requirements, improves accuracy across a range of meta-learning tasks, and improves state-of-the-art performance on an important practical problem, genotype imputation.
Leveraging electronic health records to identify risk factors for recurrent pregnancy loss across two medical centers: a case-control study

Recurrent pregnancy loss (RPL), defined as 2 or more pregnancy losses, affects 5–6% of ever-pregnant individuals. Approximately half of these cases have no identifiable explanation. To generate hypotheses about RPL etiologies, we implemented a case-control study comparing the history of over 1,600 diagnoses between RPL and live-birth patients, leveraging the University of California San Francisco (UCSF) and Stanford University electronic health record databases. In total, our study included 8,496 RPL (UCSF: 3,840, Stanford: 4,656) and 53,278 Control (UCSF: 17,259, Stanford: 36,019) patients. Menstrual abnormalities and infertility-associated diagnoses were significantly positively associated with RPL in both medical centers. Age-stratified analysis revealed that the majority of RPL-associated diagnoses had higher odds ratios for patients <35 compared with 35+ patients. While Stanford results were sensitive to control for healthcare utilization, UCSF results were stable across analyses with and without utilization. Intersecting significant results between medical centers was an effective filter to identify associations that are robust across center-specific utilization patterns.
Ultralow-dose binary oncolytic/helper-dependent adenovirus promotes antitumor activity in preclinical and clinical studies

We show that a binary oncolytic/helper-dependent adenovirus (CAdVEC) that both lyses tumor cells and locally expresses the proinflammatory cytokine IL-12 and PD-L1 blocking antibody has potent antitumor activity in humanized mouse models. On the basis of these preclinical studies, we treated four patients with a single intratumoral injection of an ultralow dose of CAdVEC (NCT03740256), representing a dose of oncolytic adenovirus more than 100-fold lower than used in previous trials. While CAdVEC caused no significant toxicities, it repolarized the tumor microenvironment with increased infiltration of CD8 T cells. A single administration of CAdVEC was associated with both locoregional and abscopal effects on metastases and, in combination with systemic administration of immune checkpoint antibodies, induced sustained antitumor responses, including one complete and two partial responses. Hence, in both preclinical and clinical studies, CAdVEC is safe and even at extremely low doses is sufficiently potent to induce significant tumor control through oncolysis and immune repolarization.
Typical Diagnostic Reference Levels of Common Indications for Computed Tomography Scans Among Adult Patients in Uganda: a Cross-sectional Study

Abstract Background Medical exposure to ionizing radiation has increased due to an increase in the number of computerized tomography (CT) scan examinations performed. The International Commission on Radiological Protection (ICRP) recommends indication-based diagnostic reference levels (IB-DRLs) as an effective tool that aids in optimizing CT scan radiation doses. In many low-income settings, there is a lack of IB-DRLs to support optimization of radiation doses. Objective To establish typical DRLs for common CT scan indications among adult patients in Kampala, Uganda. Methodology: A cross sectional study design was employed involving 337 participants enrolled from three hospitals using systematic sampling. The participants were adults who had been referred for a CT scan. The typical DRL of each indication was determined as the median value of the pooled distribution of CTDIvol (mGy) data and the median value of the pooled distribution of total DLP (tDLP)(mGy.cm) data from three hospitals. Comparison was made to anatomical, and indication based DRLs from other studies. Results 54.3% of the participants were male. The following were typical DRLs for: acute stroke (30.17mGy and 653mGy.cm); head trauma (32.04mGy and 878mGy.cm); interstitial lung diseases/ high resolution chest CT scan (4.66mGy and 161mGy.cm); pulmonary embolism (5.03mGy and 273mGy.cm); abdominopelvic lesion (6.93mGy and 838mGy.cm) and urinary calculi (7.61mGy and 975mGy.cm). Indication based total Dose Length Product (tDLP) DRLs was lower than tDLP DRLs of a whole anatomical region by 36.4% on average. Most of the developed typical IB-DLP DRLs were lower or comparable to values from studies in Ghana and Egypt in all indications besides urinary calculi while they were higher than values in a French study in all indications besides acute stroke and head trauma. Conclusion Typical IB-DRLs is a good clinical practice tool for optimization of CT doses and therefore recommended for use to manage CT radiation dose. The developed IB-DRLs varied from international values due to differences in selection of CT scan parameters and standardization of CT imaging protocols may narrow the variation. This study can serve as baseline for establishment of national indication-based CT DRLs in Uganda.
The Relationship between Topography and Gravity on Earth and Venus

Abstract The measurement of the admittance between the topography and the gravity of a planet as a function of wavelength provides important constraints on its interior processes. A detailed discussion of the influence of noise on such measurements shows that at long wavelengths the admittance is best determined in the frequency domain, using gravity as input and topography as output. This procedure is used to estimate the admittance in the Pacific and Indian Oceans. The observed values are reduced by volcanism above plumes, which produces a contribution to the long-wavelength-compensated topography. When this effect is taken into account there is little evidence that the admittance is reduced by the presence of a low viscosity zone in the upper mantle. A similar study of Venus shows an excellent correlation between gravity g and topography h for wavelengths greater than 700 km, with an admittance of about 50 mgal km -1 . This value agrees well with that expected from numerical axisymmetric models of plumes and can be used to construct maps of residual topography by calculating h - g /50, where h is in kilometers and g in milligals. The power spectrum of the gravity field and its skewness, with positive anomalies covering a smaller area, but being larger, than the negative ones, are also in agreement with those of the numerical models. Of the major topographic features, only Aphrodite and Ishtar Terrae behave differently, with a value of admittance of about one-third of other large structures. This difference requires their topography to be partly supported by variations in crustal thickness. By analogy with similar structures on Earth, both are likely to be supported by volcanically thickened crust on top of rising plumes, though the observed relationships do not rule out other models. The behavior of the admittance at long wavelengths is important, because it can constrain the thickness of the convecting layer. At wavelengths of more than 3000 km the gravity data provides some evidence that the layer depth is about 1000 km. Over the center of Beta and Phoebe Regiones there are short-wavelength anomalies of residual topography, produced by crust that is thinner than that elsewhere, flanked by positive anomalies of thicker crust. The regions of thin crust occur beneath rift zones. This behavior can be explained if crust flows down the flanks of elevated regions above plumes, and can generate coronae when the plume support disappears.