Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-cause causal inference with unmeasured confounding and binary outcome.

Unobserved confounding presents a major threat to causal inference in observational studies. Recently, several authors suggest that this problem may be overcome in a shared confounding setting where multiple treatments are independent given a common latent confounder. It has been shown that if additional data such as negative controls are available, then the causal effects are indeed identifiable. In this paper, we show that these additional data are not necessary for causal identification, provided that the treatments and outcome follow Gaussian and logistic structural equation models, respectively. Our novel identification strategy is based on the symmetry and tail properties of the observed data distribution. We further develop two-step likelihood-based estimation procedures. We illustrate our method through simulations and a real data application studying the causal relationship between the volume of various brain regions and cognitive scores.
PENGEMBANGAN MEDIA PEMBELAJARAN MULTIMEDIA INTERAKTIF BERBANTUAN SOFTWARE LECTORA INSPIRE UNTUK MENINGKATKAN HASIL BELAJAR PADA MATA PELAJARAN TEKNIK LISTRIK DI SMK NEGERI 2 SURABAYA

Abstrak Software Lectora Inspire adalah program yang digunakan untuk membuat E-learning multimedia pembelajaran dan presentasi dengan computerize. Tujuan penelitian ini adalah mengembangkan media pembelajaran interaktif yang sesuai dengan kurikulum 2013 serta media pembelajaran ini akan membantu peran guru mengajar agar mempermudah tercapainya tujuan belajar. Penelitian ini akan mengungkap kelayakan produk MERAPI (media pembelajaran berbantuan software lectora inspire), respon siswa, serta ketuntasan hasil belajar siswa pada mata pelajaran Teknik Listrik. Metode yang digunakan adalah metode Research and Development (R&D). Penelitian ini dilakukan dalam empat tahap, yaitu: (1) studi Pendahuluan, (2) merancang desain media pembelajaran, (3) validasi dan revisi, (4) uji coba media pembelajaran pada siswa kelas X jurusan Teknik Audio Video di SMK Negeri 2 Surabaya dengan menggunakan desain uji coba One-Shot Case Study dan revisi. Hasil penelitian menunjukan bahwa: (1) hasil kelayakan produk memperoleh persentase 87,37% dan masuk dalam kriteria sangat layak, (2) hasil respon siswa memperoleh persentase 91,72% dan masuk dalam kriteria sangat baik, (3) ketuntasan hasil belajar siswa memperoleh persentase 94,87% serta nilai rata-rata kelas sebesar 3,35 yang dilambangkan dengan huruf B+. Kata kunci : Media Pembelajaran, Software Lectora Inspire,Hasil Belajar. Abstract Lectora Inspire Software is a program used to creates E-learning multimedia and presentation by computerize. The aims of this research are to develop interactive learning media according to Curriculum 2013, also, this learning media will help teachers role when teaching in order to ease the achievement of learning goals. This research will reveals the properness of MERAPI (learning media assisted with Lectora Inspire Software), student response, and the completeness of learning achievement on subject of Electrical Engineering. Method used is Research and Development (R&D). This research conducted in four stages, they are: (1) preliminary study, (2) planning learning media design, (3) validation and revision, (4) learning media testing on grader X Audio Video department in SMK Negeri 2 Surabaya by using testing design One-Shot Case Study and revision. Research yield shows that: (1) result of product properness obtained 87.37% and included in very proper criteria, (2) result of student response obtained 91.72% and included in very good criteria, (3) the completeness of student learning achievement obtained 94.87% and the average of classroom score was 3.35 which denoted with B+. Keywords: Learning media, Lectora Inpire Software, Learning achievement.
Hot phase generation by supernovae in ISM simulations: resolution, chemistry, and thermal conduction

Supernovae (SNe) generate hot gas in the interstellar medium (ISM), help setting the ISM structure, and support the driving of outflows. It is important to resolve the hot gas generation for galaxy formation simulations at solar mass and sub-parsec resolution that realize individual SN explosions with ambient densities varying by several orders of magnitude in a realistic multiphase ISM. We test resolution requirements by simulating SN blast waves at three metallicities (Z = 0.01, 0.1, and 1 Z⊙), six densities and their respective equilibrium chemical compositions (n = 0.001–100 cm−3), and four mass resolutions (0.1–100 M⊙), in three dimensions. We include non-equilibrium cooling and chemistry, a homogeneous interstellar radiation field, and shielding with a modern pressure–energy smoothed particle hydrodynamics method including isotropic thermal conduction and a meshless-finite-mass solver. We find stronger resolution requirements for chemistry and hot phase generation than for momentum generation. While at 10 M⊙ the radial momenta at the end of the Sedov phase start converging, the hot phase generation and chemistry require higher resolutions to represent the neutral-to-ionized hydrogen fraction at the end of the Sedov phase correctly. Thermal conduction typically reduces the hot phase by 0.2 dex and has little impact on the chemical composition. In general, our 1 and 0.1 M⊙ results agree well with previous numerical and analytic estimates. We conclude that for the thermal energy injection SN model presented here resolutions higher than 10 M⊙ are required to model the chemistry, momentum, and hot phase generation in the multiphase ISM.
Measuring the time a tunnelling atom spends in the barrier

Tunnelling is one of the most paradigmatic and evocative phenomena of quantum physics, underlying processes such as photosynthesis and nuclear fusion, as well as devices ranging from SQUID magnetometers to superconducting qubits for quantum computers. The question of how long a particle takes to tunnel, however, has remained controversial since the first attempts to calculate it, which relied on the group delay. It is now well understood that this delay (the arrival time of the transmitted wave packet peak at the far side of the barrier) can be smaller than the barrier thickness divided by the speed of light, without violating causality. There have been a number of experiments confirming this, and even a recent one claiming that tunnelling may take no time at all. There have also been efforts to identify another timescale, which would better describe how long a given particle spends in the barrier region. Here we present a direct measurement of such a time, studying Bose-condensed $^{87}$Rb atoms tunnelling through a 1.3-${\mu}$m thick optical barrier. By localizing a pseudo-magnetic field inside the barrier, we use the spin precession of the atoms as a clock to measure the time it takes them to cross the classically forbidden region. We find a traversal time of 0.62(7) ms and study its dependence on incident energy. In addition to finally shedding light on the fundamental question of the tunnelling time, this experiment lays the groundwork for addressing deep foundational questions about history in quantum mechanics: for instance, what can we learn about where a particle was at earlier times by observing where it is now?
Binary Component Decomposition Part I: The Positive-Semidefinite Case

This paper studies the problem of decomposing a low-rank positive-semidefinite matrix into symmetric factors with binary entries, either $\{\pm 1\}$ or $\{0,1\}$. This research answers fundamental questions about the existence and uniqueness of these decompositions. It also leads to tractable factorization algorithms that succeed under a mild deterministic condition. A companion paper addresses the related problem of decomposing a low-rank rectangular matrix into a binary factor and an unconstrained factor.
Noncommutative weak $(1,1)$ type estimate for a square function from ergodic theory

In this paper, we investigate the boundedness of a square function from ergodic theory on noncommutative $L_{p}$-spaces. The main result is a weak $(1,1)$ type estimate of this square function. We also show the $(L_{\infty},\mathrm{BMO})$ estimate, and thus strong $(L_{p},L_{p})$ estimate by interpolation. The main novel difficulty lies in the fact that the kernel of this square function does not enjoy any regularity, which is crucial in showing such endpoint estimates for standard noncommutative Calder\'on-Zygmund singular integral operators.
Convergence of the Ricci flow on asymptotically flat manifolds with integral curvature pinching

We prove a curvature pinching result for the Ricci flow on asymptotically flat manifolds: if an asymptotically flat manifold of dimension $n\geq 3$ has scale-invariant integral norm of curvature sufficiently pinched relative to the inverse of its Sobolev constant, then the Ricci flow starting from this manifold exists for all positive times and converges to flat Euclidean space. In particular our result implies that the initial manifold must have been diffeomorphic to $\mathbb{R}^n$.
Rapid Light Field Depth Estimation with Semi-Global Matching

Running time of the light field depth estimation algorithms is typically high. This assessment is based on the computational complexity of existing methods and the large amounts of data involved. The aim of our work is to develop a simple and fast algorithm for accurate depth computation. In this context, we propose an approach, which involves Semi-Global Matching for the processing of light field images. It forms on comparison of pixels’ correspondences with different metrics in the substantially bounded light field space. We show that our method is suitable for the fast production of a proper result in a variety of light field configurations.
Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity

Zeroth-order (gradient-free) method is a class of powerful optimization tool for many machine learning problems because it only needs function values (not gradient) in the optimization. In particular, zeroth-order method is very suitable for many complex problems such as black-box attacks and bandit feedback, whose explicit gradients are difficult or infeasible to obtain. Recently, although many zeroth-order methods have been developed, these approaches still exist two main drawbacks: 1) high function query complexity; 2) not being well suitable for solving the problems with complex penalties and constraints. To address these challenging drawbacks, in this paper, we propose a novel fast zeroth-order stochastic alternating direction method of multipliers (ADMM) method (\emph{i.e.}, ZO-SPIDER-ADMM) with lower function query complexity for solving nonconvex problems with multiple nonsmooth penalties. Moreover, we prove that our ZO-SPIDER-ADMM has the optimal function query complexity of $O(dn + dn^{\frac{1}{2}}\epsilon^{-1})$ for finding an $\epsilon$-approximate local solution, where $n$ and $d$ denote the sample size and dimension of data, respectively. In particular, the ZO-SPIDER-ADMM improves the existing best nonconvex zeroth-order ADMM methods by a factor of $O(d^{\frac{1}{3}}n^{\frac{1}{6}})$. Moreover, we propose a fast online ZO-SPIDER-ADMM (\emph{i.e.,} ZOO-SPIDER-ADMM). Our theoretical analysis shows that the ZOO-SPIDER-ADMM has the function query complexity of $O(d\epsilon^{-\frac{3}{2}})$, which improves the existing best result by a factor of $O(\epsilon^{-\frac{1}{2}})$. Finally, we utilize a task of structured adversarial attack on black-box deep neural networks to demonstrate the efficiency of our algorithms.
Non-negative Ollivier curvature on graphs, reverse Poincar\'e inequality, Buser inequality, Liouville property, Harnack inequality and eigenvalue estimates

We prove that for combinatorial graphs with non-negative Ollivier curvature, one has ‖Ptμ− Ptν‖1 ≤ W1(μ, ν) √ t for all probability measures μ, ν where Pt is the heat semigroup and W1 is the l1-Wasserstein distance. This turns out to be an equivalent formulation of a version of reverse Poincaré inequality. Furthermore, this estimate allows us to prove Buser inequality, Liouville property and the the eigenvalue estimate λ1 ≥ log(2)/diam.
On the heat content for the Poisson kernel over the unit ball in the euclidean space

This paper studies, by employing analytical tools, the small‐time behavior of the heat content for the Poisson kernel over the unit ball in Rd , d⩾2 by working with its related set covariance function. As a result, we obtain a representation for the third term in the expansion of the heat content over the unit ball and provide the explicit form of this term in the particular cases d=2 and d=3 .
He I λ 10 830 Å in the transmission spectrum of HD209458 b

Context. Recently, the He I triplet at 10 830 Å was rediscovered as an excellent probe of the extended and possibly evaporating atmospheres of close-in transiting planets. This has already resulted in detections of this triplet in the atmospheres of a handful of planets, both from space and from the ground. However, while a strong signal is expected for the hot Jupiter HD 209458 b, only upper limits have been obtained so far. Aims. Our goal is to measure the helium excess absorption from HD 209458 b and assess the extended atmosphere of the planet and possible evaporation. Methods. We obtained new high-resolution spectral transit time-series of HD 209458 b using CARMENES at the 3.5 m Calar Alto telescope, targeting the He I triplet at 10 830 Å at a spectral resolving power of 80 400. The observed spectra were corrected for stellar absorption lines using out-of-transit data, for telluric absorption using the MOLECFIT software, and for the sky emission lines using simultaneous sky measurements through a second fibre. Results. We detect He I absorption at a level of 0.91 ± 0.10% (9 σ) at mid-transit. The absorption follows the radial velocity change of the planet during transit, unambiguously identifying the planet as the source of the absorption. The core of the absorption exhibits a net blueshift of 1.8 ± 1.3 km s−1. Possible low-level excess absorption is seen further blueward from the main absorption near the centre of the transit, which could be caused by an extended tail. However, this needs to be confirmed. Conclusions. Our results further support a close relation between the strength of planetary absorption in the helium triplet lines and the level of ionising, stellar X-ray, and extreme-UV irradiation.
Generating and Detecting High Frequency Liquid-Based Sound Resonances with Nanoplasmonics.

We use metal nanostructures (nanoplasmonics) excited with dual frequency lasers to generate and detect high frequency ($>10$ GHz) sound wave resonances in water. The difference frequency between the two lasers causes beating in the intensity, which results in a drop in the transmission through the nanostructure when an acoustic resonance is excited. By observing the resonance frequency shifts with changing nanostructure size, the transition from slow to fast sound in water is inferred, which has been measured by inelastic scattering methods in the past. The observed behavior shows remarkable similarities to finite element simulations using a simple Debye model for sound velocity (without fitting parameters).
EnclaveDom: Privilege Separation for Large-TCB Applications in Trusted Execution Environments

Trusted executions environments (TEEs) such as Intel(R) SGX provide hardware-isolated execution areas in memory, called enclaves. By running only the most trusted application components in the enclave, TEEs enable developers to minimize the TCB of their applications thereby helping to protect sensitive application data. However, porting existing applications to TEEs often requires considerable refactoring efforts, as TEEs provide a restricted interface to standard OS features. To ease development efforts, TEE application developers often choose to run their unmodified application in a library OS container that provides a full in-enclave OS interface. Yet, this large-TCB development approach now leaves sensitive in-enclave data exposed to potential bugs or vulnerabilities in third-party code imported into the application. Importantly, because the TEE libOS and the application run in the same enclave address space, even the libOS management data structures (e.g. file descriptor table) may be vulnerable to attack, where in traditional OSes these data structures may be protected via privilege isolation. We present EnclaveDom, a privilege separation system for large-TCB TEE applications that partitions an enclave into tagged memory regions, and enforces per-region access rules at the granularity of individual in-enclave functions. EnclaveDom is implemented on Intel SGX using Memory Protection Keys (MPK) for memory tagging. To evaluate the security and performance impact of EnclaveDom, we integrated EnclaveDom with the Graphene-SGX library OS. While no product or component can be absolutely secure, our prototype helps protect internal libOS management data structures against tampering by application-level code. At every libOS system call, EnclaveDom then only grants access to those internal data structures which the syscall needs to perform its task.
Capsule Networks Need an Improved Routing Algorithm

In capsule networks, the routing algorithm connects capsules in consecutive layers, enabling the upper-level capsules to learn higher-level concepts by combining the concepts of the lower-level capsules. Capsule networks are known to have a few advantages over conventional neural networks, including robustness to 3D viewpoint changes and generalization capability. However, some studies have reported negative experimental results. Nevertheless, the reason for this phenomenon has not been analyzed yet. We empirically analyzed the effect of five different routing algorithms. The experimental results show that the routing algorithms do not behave as expected and often produce results that are worse than simple baseline algorithms that assign the connection strengths uniformly or randomly. We also show that, in most cases, the routing algorithms do not change the classification result but polarize the link strengths, and the polarization can be extreme when they continue to repeat without stopping. In order to realize the true potential of the capsule network, it is essential to develop an improved routing algorithm.
Fast Solution Methods for Convex Fractional Differential Equation Optimization

In this paper, we present numerical methods suitable for solving convex Fractional Differential Equation (FDE) optimization problems, with potential box constraints on the state and control variables. First we derive powerful multilevel circulant preconditioners, which may be embedded within Krylov subspace methods, for solving equality constrained FDE optimization problems. We then develop an Alternating Direction Method of Multipliers (ADMM) framework, which uses preconditioned Krylov solvers for the resulting sub-problems. The latter allows us to tackle FDE optimization problems with box constraints, posed on space-time domains, that were previously out of the reach of state-of-the-art preconditioners. Discretized versions of FDEs involve large dense linear systems. In order to overcome this difficulty, we design a recursive linear algebra, which is based on the fast Fourier transform (FFT), and our proposed multilevel circulant preconditioners are suitable for approximating discretized FDEs of arbitrary dimension. Focusing on time-dependent 2-dimensional FDEs, we manage to keep the storage requirements linear, with respect to the grid size N, while ensuring an order N log N computational complexity per iteration of the Krylov solver. We implement the proposed method, and demonstrate its scalability, generality, and efficiency, through a series of experiments over different setups of the FDE optimization problem.
A note on transition in discrete gauge groups in F-theory

We observe a new puzzling physical phenomenon in F-theory on the multisection geometry, wherein a model without a gauge group transitions to another model with a discrete $\mathbb{Z}_n$ gauge group via Higgsing. This phenomenon may suggest an unknown aspect of F-theory compactification on multisection geometry lacking a global section. A possible interpretation of this puzzling physical phenomenon is proposed in this note. We also propose a possible interpretation of another unnatural physical phenomenon observed for F-theory on four-section geometry, wherein a discrete $\mathbb{Z}_2$ gauge group transitions to a discrete $\mathbb{Z}_4$ gauge group via Higgsing as described in the previous literature.
Overcoming Catastrophic Forgetting by Neuron-level Plasticity Control

To address the issue of catastrophic forgetting in neural networks, we propose a novel, simple, and effective solution called neuron-level plasticity control (NPC). While learning a new task, the proposed method preserves the existing knowledge from the previous tasks by controlling the plasticity of the network at the neuron level. NPC estimates the importance value of each neuron and consolidates important neurons by applying lower learning rates, rather than restricting individual connection weights to stay close to the values optimized for the previous tasks. The experimental results on the several datasets show that neuron-level consolidation is substantially more effective compared to connection-level consolidation approaches.
Deep Learning Training on the Edge with Low-Precision Posits

Recently, the posit numerical format has shown promise for DNN data representation and compute with ultra-low precision ([5.8]-bit). However, majority of studies focus only on DNN inference. In this work, we propose DNN training using posits and compare with the floating point training. We evaluate on both MNIST and Fashion MNIST corpuses, where 16-bit posits outperform 16-bit floating point for end-to-end DNN training.
Self-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification

Person re-identification (Re-ID) has achieved great improvement with deep learning and a large amount of labelled training data. However, it remains a challenging task for adapting a model trained in a source domain of labelled data to a target domain of only unlabelled data available. In this work, we develop a self-training method with progressive augmentation framework (PAST) to promote the model performance progressively on the target dataset. Specially, our PAST framework consists of two stages, namely, conservative stage and promoting stage. The conservative stage captures the local structure of target-domain data points with triplet-based loss functions, leading to improved feature representations. The promoting stage continuously optimizes the network by appending a changeable classification layer to the last layer of the model, enabling the use of global information about the data distribution. Importantly, we propose a new self-training strategy that progressively augments the model capability by adopting conservative and promoting stages alternately. Furthermore, to improve the reliability of selected triplet samples, we introduce a ranking-based triplet loss in the conservative stage, which is a label-free objective function based on the similarities between data pairs. Experiments demonstrate that the proposed method achieves state-of-the-art person Re-ID performance under the unsupervised cross-domain setting. Code is available at: tinyurl.com/PASTReID