Miracle-dz/UniFAR-FTUs huggingface dataset:
Second-order cone certification of external positivity

Internal positivity offers a computationally cheap certificate for external (input-output) positivity of a linear time-invariant system. However, the drawback with this certificate lies in the its realization dependency. Firstly, computing such a realization requires to find a polyhedral cone with a potentially high number of extremal generators that lifts the dimension of the state-space representation, significantly. Secondly, not all externally positive systems posses an internally positive realization. Thirdly, in many typical applications such as controller design, system identification and model order reduction, internal positivity is not preserved. To overcome these drawbacks, we present a tractable certificate of external positivity based on finding second-order cones. In particular, the certificate is guaranteed to succeed, whenever it is feasible for any minimal realization. Nonetheless, it is also revealed that neither second-order cones nor polyhedral cones can provide a necessary certificate. Finally, we exemplify the potential of our certificate based on three applications. We discuss how our certificate an be used to find externally positive approximations of nearly externally positive systems, which as demonstrated can help to reduce system identification errors. The same algorithm is used then to design state-feedback controllers that avoid over- and undershooting. Lastly, we present modifications to generalized balanced truncation such that external positivity is preserved.
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach

It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally efficiently incorporating syntactic structure into neural language models has been a challenging topic. In this paper, we make use of a multi-task objective, i.e., the models simultaneously predict words as well as ground truth parse trees in a form called “syntactic distances”, where information between these two separate objectives shares the same intermediate representation. Experimental results on the Penn Treebank and Chinese Treebank datasets show that when ground truth parse trees are provided as additional training signals, the model is able to achieve lower perplexity and induce trees with better quality.
O-Minimal Invariants for Discrete-Time Dynamical Systems

Termination analysis of linear loops plays a key rôle in several areas of computer science, including program verification and abstract interpretation. Already for the simplest variants of linear loops the question of termination relates to deep open problems in number theory, such as the decidability of the Skolem and Positivity Problems for linear recurrence sequences, or equivalently reachability questions for discrete-time linear dynamical systems. In this article, we introduce the class of o-minimal invariants, which is broader than any previously considered, and study the decidability of the existence and algorithmic synthesis of such invariants as certificates of non-termination for linear loops equipped with a large class of halting conditions. We establish two main decidability results, one of them conditional on Schanuel’s conjecture is transcendental number theory.
MOReL : Model-Based Offline Reinforcement Learning

In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based solely on a dataset of historical interactions with the environment. The ability to train RL policies offline can greatly expand the applicability of RL, its data efficiency, and its experimental velocity. Prior work in offline RL has been confined almost exclusively to model-free RL approaches. In this work, we present MOReL, an algorithmic framework for model-based offline RL. This framework consists of two steps: (a) learning a pessimistic MDP (P-MDP) using the offline dataset; and (b) learning a near-optimal policy in this P-MDP. The learned P-MDP has the property that for any policy, the performance in the real environment is approximately lower-bounded by the performance in the P-MDP. This enables it to serve as a good surrogate for purposes of policy evaluation and learning, and overcome common pitfalls of model-based RL like model exploitation. Theoretically, we show that MOReL is minimax optimal (up to log factors) for offline RL. Through experiments, we show that MOReL matches or exceeds state-of-the-art results in widely studied offline RL benchmarks. Moreover, the modular design of MOReL enables future advances in its components (e.g. generative modeling, uncertainty estimation, planning etc.) to directly translate into advances for offline RL.
Evidence of many thermodynamic states of the three-dimensional Ising spin glass

We present a large-scale simulation of the three-dimensional Ising spin glass with Gaussian disorder to low temperatures and large sizes using optimized population annealing Monte Carlo. Our primary focus is investigating the number of pure states regarding a controversial statistic, characterizing the fraction of centrally peaked disorder instances, of the overlap function order parameter. We observe that this statistic is subtly and sensitively influenced by the slight fluctuations of the integrated central weight of the disorder-averaged overlap function, making the asymptotic growth behaviour very difficult to identify. Modified statistics effectively reducing this correlation are studied and essentially monotonic growth trends are obtained. The effect of temperature is also studied, finding a larger growth rate at a higher temperature. Our detailed examination and state-of-the-art simulation provide a coherent picture of many pure states, explain the previous findings, and the controversy is solved. The pertinent status of the number of pure states beyond this statistic is also discussed, and we find the spin glass balance is overall tilting towards many pure states studied by simulations.
First-and second-order necessary optimality conditions for Bolza functionals with Caputo fractional derivatives and general mixed initial/final constraints

The present work was primarily motivated by our findings in the literature of some flaws within the proof of the second-order Legendre necessary optimality condition for fractional calculus of variations problems. Therefore we were eager to elaborate a correct proof and it turns out that this goal is highly nontrivial, especially when considering final constraints. This paper is the result of our reflections on this subject. Precisely we consider here a constrained minimization problem of a general Bolza functional that depends on a Caputo fractional derivative of order 0 0, the constraint set describing general mixed initial/final constraints. The main contribution of our work is to derive corresponding first-and second-order necessary optimality conditions, namely the Euler-Lagrange equation, the transversality conditions and, of course, the Legendre condition. A detailed discussion is provided on the obstructions encountered with the classical strategy, while the new proof that we propose here is based on the Ekeland variational principle. Furthermore we underline that some subsidiary contributions are provided all along the paper. In particular we prove an independent and intrinsic result of fractional calculus stating that it does not exist a nontrivial function which is, together with its Caputo fractional derivative of order 0 < α < 1, compactly supported. Moreover we also discuss some evidences claiming that Riemann-Liouville fractional integrals should be considered in the formulation of fractional calculus of variations problems in order to preserve the existence of solutions.
Dynamic Memory Induction Networks for Few-Shot Text Classification

This paper proposes Dynamic Memory Induction Networks (DMIN) for few-short text classification. The model develops a dynamic routing mechanism over static memory, enabling it to better adapt to unseen classes, a critical capability for few-short classification. The model also expands the induction process with supervised learning weights and query information to enhance the generalization ability of meta-learning. The proposed model brings forward the state-of-the-art performance significantly by 2~4% improvement on the miniRCV1 and ODIC datasets. Detailed analysis is further performed to show how the proposed network achieves the new performance.
Diffeomorphic souls and disconnected moduli spaces of nonnegatively curved metrics

We give examples of open manifolds that carry infinitely many complete metrics of nonnegative sectional curvature such that they all have the same soul, and their isometry classes lie in different connected components of the moduli space. All previously known examples of this kind have souls of codimension one. In our examples the souls have codimensions three and two.
Reobserving the NLS1 galaxy RE J1034+396 – I. The long-term, recurrent X-ray QPO with a high significance

RE J1034+396 is a narrow-line Seyfert 1 galaxy (NLS1) in which the first significant X-ray quasi-periodic oscillation (QPO) in an active galactic nucleus (AGN) was observed in 2007. We report the detection of this QPO in a recent XMM–Newton observation in 2018 with an even higher significance. The quality factor of this QPO is 20, and its period is 3550 ± 80 s, which is 250 ± 100 s shorter than in 2007. While the QPO’s period has no significant energy dependence, its fractional root mean square variability increases from 4 per cent in 0.3–1 keV to 12 per cent in 1–4 keV bands. An interesting phenomenon is that the QPO in 0.3–1 keV leads that in the 1–4 keV bands by 430 ± 50 s with a high coherence, opposite to the soft X-ray lag reported for the observation in 2007. We speculate that the QPO has an intrinsic hard lag, while the previous reported soft lag is caused by the interference of stochastic variability. This soft X-ray lead in the new data supports the idea that the QPO of RE J1034+396 is a possible AGN counterpart of the 67 Hz high-frequency QPO seen in the black hole binary GRS 1915+105. We also search for QPO harmonics, but do not find any significant signals. Our new data reinforce previous results that the QPO is seen in a specific spectral state, as the only two observations showing no significant QPO signal exhibit an even stronger soft X-ray excess than the other six observations that display the QPO. Therefore, our results imply that the QPO in RE J1034+396 is physically linked to a soft X-ray component.
Effects of Brackish Water Utilization by Broilers and Growing Pullets on Performance

Abstract Brackish water from two wells in the Al-Shaggaya area of Kuwait and soft water from Kuwait City were utilized in two experiments to investigate the effects of brackish water on the performance of broilers and growing replacement pullets. Results obtained indicated that brackish water containing up to 3000 ppm of dissolved solids did not adversely affect the overall performance of broilers at either 4 or 6 weeks of age nor the performance of growing pullets from 12 to 20 weeks of age. Differences in body weight gains, feed consumption, water intake and ratio of water consumption to feed intake were not statistically significant. Examination of the internal organs of slaughtered birds at the end of the experiments did not reveal any abnormalities. Also, examination of the livers for fat accumulations and blood sera for concentrations of major cations and anions did not reveal any significant differences between treatments.
Understanding and Correcting Low-quality Retinal Fundus Images for Clinical Analysis

Retinal fundus images are widely used for clinical screening and diagnosis of eye diseases. However, fundus images captured by operators with various levels of experiences have a large variation in quality. Low-quality fundus images increase the uncertainty in clinical observation and lead to a risk of misdiagnosis. Due to the special optical beam of fundus imaging and retinal structure, the natural image enhancement methods cannot be utilized directly. In this paper, we first analyze the ophthalmoscope imaging system and model the reliable degradation of major inferior-quality factors, including uneven illumination, blur, and artifacts. Then, based on the degradation model, a clinical-oriented fundus enhancement network~(cofe-Net)~is proposed to suppress the global degradation factors, and simultaneously preserve anatomical retinal structures and pathological characteristics for clinical observation and analysis. Experiments on both synthetic and real fundus images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details. Moreover, we also show that the fundus correction method can benefit medical image analysis applications, e.g, retinal vessel segmentation and optic disc/cup detection.
Site-Specific Online Compressive Beam Codebook Learning in mmWave Vehicular Communication

Millimeter wave (mmWave) communication is one viable solution to support Gbps sensor data sharing in vehicular networks. The use of large antenna arrays at mmWave and high mobility in vehicular communication make it challenging to design fast beam alignment solutions. In this paper, we propose a novel framework that learns the channel angle-of-departure (AoD) statistics at a base station (BS) and uses this information to efficiently acquire channel measurements. Our framework integrates online learning for compressive sensing (CS) codebook learning and the optimized codebook is used for CS-based beam alignment. We formulate a CS matrix optimization problem based on the AoD statistics available at the BS. Furthermore, based on the CS channel measurements, we develop techniques to update and learn such channel AoD statistics at the BS. We use the upper confidence bound (UCB) algorithm to learn the AoD statistics and the CS matrix. Numerical results show that the CS matrix in the proposed framework provides faster beam alignment than standard CS matrix designs. Simulation results indicate that the proposed beam training technique can reduce overhead by 80% compared to exhaustive beam search, and 70% compared to standard CS solutions that do not exploit any AoD statistics.
Neural Architecture Transfer

Neural architecture search (NAS) has emerged as a promising avenue for automatically designing task-specific neural networks. Existing NAS approaches require one complete search for each deployment specification of hardware or objective. This is a computationally impractical endeavor given the potentially large number of application scenarios. In this paper, we propose <italic>Neural Architecture Transfer</italic> (NAT) to overcome this limitation. NAT is designed to efficiently generate task-specific custom models that are competitive under multiple conflicting objectives. To realize this goal we learn task-specific supernets from which specialized subnets can be sampled without any additional training. The key to our approach is an integrated online transfer learning and many-objective evolutionary search procedure. A pre-trained supernet is iteratively adapted while simultaneously searching for task-specific subnets. We demonstrate the efficacy of NAT on 11 benchmark image classification tasks ranging from large-scale multi-class to small-scale fine-grained datasets. In all cases, including ImageNet, NATNets improve upon the state-of-the-art under mobile settings (<inline-formula><tex-math notation="LaTeX">$\leq$</tex-math><alternatives><mml:math><mml:mo>≤</mml:mo></mml:math><inline-graphic xlink:href="boddeti-ieq1-3052758.gif"/></alternatives></inline-formula> 600M Multiply-Adds). Surprisingly, small-scale fine-grained datasets benefit the most from NAT. At the same time, the architecture search and transfer is orders of magnitude more efficient than existing NAS methods. Overall, experimental evaluation indicates that, across diverse image classification tasks and computational objectives, NAT is an appreciably more effective alternative to conventional transfer learning of fine-tuning weights of an existing network architecture learned on standard datasets. Code is available at <uri>https://github.com/human-analysis/neural-architecture-transfer</uri>.
Stillleben: Realistic Scene Synthesis for Deep Learning in Robotics

Training data is the key ingredient for deep learning approaches, but difficult to obtain for the specialized domains often encountered in robotics. We describe a synthesis pipeline capable of producing training data for cluttered scene perception tasks such as semantic segmentation, object detection, and correspondence or pose estimation. Our approach arranges object meshes in physically realistic, dense scenes using physics simulation. The arranged scenes are rendered using high-quality rasterization with randomized appearance and material parameters. Noise and other transformations introduced by the camera sensors are simulated. Our pipeline can be run online during training of a deep neural network, yielding applications in life-long learning and in iterative render-and-compare approaches. We demonstrate the usability by learning semantic segmentation on the challenging YCB-Video dataset without actually using any training frames, where our method achieves performance comparable to a conventionally trained model. Additionally, we show successful application in a real-world regrasping system.
Benchmarking Coherent Errors in Controlled-Phase Gates due to Spectator Qubits

A major challenge in operating multi-qubit quantum processors is to mitigate multi-qubit coherent errors. For superconducting circuits, besides crosstalk originating from imperfect isolation of control lines, dispersive coupling between qubits is a major source of multi-qubit coherent errors. We benchmark phase errors in a controlled-phase gate due to dispersive coupling of either of the qubits involved in the gate to one or more spectator qubits. We measure the associated gate infidelity using quantum process tomography. In addition, we point out that, due to coupling of the gate qubits to a non-computational state during the gate, two-qubit conditional phase errors are enhanced. Our work is important for understanding limits to the fidelity of two-qubit gates with finite on/off ratio in multi-qubit settings.
Viscosity Solutions to Second Order Path-Dependent Hamilton-Jacobi-Bellman Equations and Applications

In this article, a notion of viscosity solutions is introduced for second order path-dependent Hamilton-Jacobi-Bellman (PHJB) equations associated with optimal control problems for path-dependent stochastic differential equations. We identify the value functional of optimal control problems as unique viscosity solution to the associated PHJB equations. We also show that our notion of viscosity solutions is consistent with the corresponding notion of classical solutions, and satisfies a stability property. Applications to backward stochastic Hamilton-Jacobi-Bellman equations are also given.
GalICS 2.1: a new semianalytic model for cold accretion, cooling, feedback, and their roles in galaxy formation

Dekel & Birnboim06 proposed that the mass-scale that separates late-type and early-type galaxies is linked to the critical halo mass for the propagation of a stable shock and showed that they could reproduce the observed bimodality scale for plausible values of the metallicity of the accreted gas and the shock radius. Here, we take their analysis one step further and present a new semianalytic model that computes the shock radius from first principles. This advancement allows us to compute the critical mass individually for each halo. Separating cold-mode and hot-mode accretion has little effect on the final galaxy masses if feedback does not preferentially couple to the hot gas. We also present an improved model for stellar feedback where 70% of the wind mass is in a cold galactic fountain with a shorter reaccretion timescale at high masses. The latter is the key mechanism that allows us to reproduce the low-mass end of the mass function of galaxies over the entire redshift range 0<z<2.5. Cooling must be mitigated to avoid overpredicting the number density of galaxies with stellar mass greater than a hundred billion Solar masses but is important to form intermediate-mass galaxies. At virial masses greater than three hundred billion Solar masses, cold accretion is more important at high z, where gas is accreted from smaller solid angles, but this is not true at lower masses because high-z filaments have lower metallicities. Our predictions are consistent with the observed metallicity evolution of the intergalactic medium at 0<z<5.
Evaluation of established breeds of sheep in Canada for daily gain and body weights.

Breed differences were assessed in the daily gain and body weights of 6356 lambs from the Federal-Provincial Canadian Record of Performance program. The breeds represented were Suffolk, Hampshire, Oxford, Lincoln, Columbia, Rambouillet, Dorset, Corriedale, Leicester, North Country Cheviot, Southdown and Finnish Landrace. Sources of variation due to the influence of year, sex, type of birth and rearing, age of dam, time of year of birth, breeder (flock) and interactions were determined together with linear regressions of daily gain and body weights at 50 and 100 d on age of lamb within breed. Significant interactions between breed and year showed that the ranking of breeds varied from year to year, probably due to sampling associated with the breeds that had fewer lambs assessed. Of the meat-type sire breeds, the Suffolk was superior in growth performance of lambs, followed by the Oxford, and Hampshire breeds. Lamb growth performance was intermediate in the Finnish Landrace Dorset, North Country Cheviot an.
Bulk locality in the 
AdS/CFT
 correspondence

In this paper, we study the bulk local states in AdS/CFT correspondence in the large $N$ limit using the formula explicitly relating the bulk local operators and the CFT local operators. We identify the bulk local state in terms of CFT local states and find that the bulk local state corresponds to a CFT state supported in the whole space, which means the subregion duality is not valid. On the other hand, CFT states supported in a space region are expressed in terms of the bulk states supported in a certain region. We find that the quantum error correction proposal is not realized although the puzzles of the radial locality which motivated the proposal are resolved in our analysis. For the understating of the bulk local states, an explicit realization of an analogue of the Reeh-Schlieder theorem plays an important role.
Localized convolutional neural networks for geospatial wind forecasting

Convolutional Neural Networks (CNN) possess many positive qualities when it comes to spatial raster data. Translation invariance enables CNNs to detect features regardless of their position in the scene. However, in some domains, like geospatial, not all locations are exactly equal. In this work, we propose localized convolutional neural networks that enable convolutional architectures to learn local features in addition to the global ones. We investigate their instantiations in the form of learnable inputs, local weights, and a more general form. They can be added to any convolutional layers, easily end-to-end trained, introduce minimal additional complexity, and let CNNs retain most of their benefits to the extent that they are needed. In this work we address spatio-temporal prediction: test the effectiveness of our methods on a synthetic benchmark dataset and tackle three real-world wind prediction datasets. For one of them, we propose a method to spatially order the unordered data. We compare the recent state-of-the-art spatio-temporal prediction models on the same data. Models that use convolutional layers can be and are extended with our localizations. In all these cases our extensions improve the results, and thus often the state-of-the-art. We share all the code at a public repository.