Miracle-dz/UniFAR-FTUs huggingface dataset:
Causal Inference for Comprehensive Cohort Studies.

In a comprehensive cohort study of two competing treatments (say, A and B), clinically eligible individuals are first asked to enroll in a randomized trial and, if they refuse, are then asked to enroll in a parallel observational study in which they can choose treatment according to their own preference. We consider estimation of two estimands: (1) comprehensive cohort causal effect -- the difference in mean potential outcomes had all patients in the comprehensive cohort received treatment A vs. treatment B and (2) randomized trial causal effect -- the difference in mean potential outcomes had all patients enrolled in the randomized trial received treatment A vs. treatment B. For each estimand, we consider inference under various sets of unconfoundedness assumptions and construct semiparametric efficient and robust estimators. These estimators depend on nuisance functions, which we estimate, for illustrative purposes, using generalized additive models. Using the theory of sample splitting, we establish the asymptotic properties of our proposed estimators. We also illustrate our methodology using data from the Bypass Angioplasty Revascularization Investigation (BARI) randomized trial and observational registry to evaluate the effect of percutaneous transluminal coronary balloon angioplasty versus coronary artery bypass grafting on 5-year mortality. To evaluate the finite sample performance of our estimators, we use the BARI dataset as the basis of a realistic simulation study.
Identifying causal effects in maximally oriented partially directed acyclic graphs

We develop a necessary and sufficient causal identification criterion for maximally oriented partially directed acyclic graphs (MPDAGs). MPDAGs as a class of graphs include directed acyclic graphs (DAGs), completed partially directed acyclic graphs (CPDAGs), and CPDAGs with added background knowledge. As such, they represent the type of graph that can be learned from observational data and background knowledge under the assumption of no latent variables. Our identification criterion can be seen as a generalization of the g-formula of Robins (1986). We further obtain a generalization of the truncated factorization formula (Pearl, 2009) and compare our criterion to the generalized adjustment criterion of Perkovi\'c et al. (2017) which is sufficient, but not necessary for causal identification.
Photonic black resonators and photon stars in AdS5

Rapidly rotating Myers–Perry-AdS5 (MPAdS5) black holes are shown to be unstable against rotational superradiance of a Maxwell field. From the onset of the instability, time-periodic neutral black hole solutions equipped with a nontrivial electromagnetic wave are obtained, which we call photonic black resonators. In the horizonless limit, they reduce to geon solutions which may be called photon stars. Specifically, we introduce a cohomogeneity-1 ansatz for the metric and Maxwell field and construct such solutions with an isometry group. We compute thermodynamic quantities and obtain phase diagrams. It turns out that a photonic black resonator has a higher entropy than a MPAdS5 black hole, while it also has a smaller entropy than a black resonator without the Maxwell field. This suggests what is expected for nonlinear dynamics following the Maxwell superradiant instability with the isometry.
Extension Theory for Braided-Enriched Fusion Categories

For a braided fusion category $\mathcal{V}$, a $\mathcal{V}$-fusion category is a fusion category $\mathcal{C}$ equipped with a braided monoidal functor $\mathcal{F}:\mathcal{V} \to Z(\mathcal{C})$. Given a fixed $\mathcal{V}$-fusion category $(\mathcal{C}, \mathcal{F})$ and a fixed $G$-graded extension $\mathcal{C}\subseteq \mathcal{D}$ as an ordinary fusion category, we characterize the enrichments $\widetilde{\mathcal{F}}:\mathcal{V} \to Z(\mathcal{D})$ of $\mathcal{D}$ that are compatible with the enrichment of $\mathcal{C}$. We show that G-crossed extensions of a braided fusion category $\mathcal{C}$ are G-extensions of the canonical enrichment of $\mathcal{C}$ over itself. As an application, we parameterize the set of $G$-crossed braidings on a fixed $G$-graded fusion category in terms of certain subcategories of its center, extending Nikshych’s classification of the braidings on a fusion category.
Accretion disc by Roche lobe overflow in the supergiant fast X-ray transient IGR J08408−4503

Supergiant fast X-ray transients (SFXTs) are X-ray binary systems with a supergiant companion and likely a neutron star, which show a fast (∼103 s) and high variability with a dynamic range up to 105−6. Given their extreme properties, they are considered among the most valuable laboratories to test accretion models. Recently, the orbital parameters of a member of this class, IGR J08408−4503, were obtained from optical observations. We used this information, together with X-ray observations from previous publications and new results from X-ray and optical data collected by INTEGRAL and presented in this work, to study the accretion mechanisms at work in IGR J08408−4503. We found that the high eccentricity of the compact object orbit and the large size of the donor star imply Roche lobe overflow (RLO) around the periastron. It is also likely that a fraction of the outer layers of the photosphere of the donor star are lost from the Lagrangian point L2 during the periastron passages. On the basis of these findings, we discuss the flaring variability of IGR J08408−4503 assuming the presence of an accretion disc. We point out that IGR J08408−4503 may not be the only SFXT with an accretion disc fueled by RLO. These findings open a new scenario for accretion mechanisms in SFXTs, since most of them have so far been based on the assumption of spherically symmetric accretion.
Semiconductor - Ferromagnetic Insulator - Superconductor Nanowires: Stray Field and Exchange Field.

Nanowires can serve as flexible substrates for hybrid epitaxial growth on selected facets, allowing for design of heterostructures with complex material combinations and geometries. In this work we report on hybrid epitaxy of free standing vapor-liquid-solid grown and in-plane selective area grown semiconductor - ferromagnetic insulator - superconductor (InAs/EuS/Al) nanowire heterostructures. We study the crystal growth and complex epitaxial matching of wurtzite and zinc-blende InAs / rock-salt EuS interfaces as well as rock-salt EuS / face-centered cubic Al interfaces. Because of the magnetic anisotropy originating from the nanowire shape, the magnetic structure of the EuS phase are easily tuned into single magnetic domains. This effect efficiently ejects the stray field lines along the nanowires. With tunnel spectroscopy measurements of the density of states, we show the material has a hard induced superconducting gap, and magnetic hysteretic evolution which indicates that the magnetic exchange fields are not negligible. These hybrid nanowires fulfill key material requirements for serving as a platform for spin-based quantum applications, such as scalable topological quantum computing.
Fake News Detection Using Deep Learning

News is a routine in everyone's life. It helps in enhancing the knowledge on what happens around the world. Fake news is a fictional information madeup with the intension to delude and hence the knowledge acquired becomes of no use. As fake news spreads extensively it has a negative impact in the society and so fake news detection has become an emerging research area. The paper deals with a solution to fake news detection using the methods, deep learning and Natural Language Processing. The dataset is trained using deep neural network. The dataset needs to be well formatted before given to the network which is made possible using the technique of Natural Language Processing and thus predicts whether a news is fake or not.
Error Probability Analysis and Power Allocation for Interference Exploitation Over Rayleigh Fading Channels

This paper considers the performance analysis of constructive interference (CI) precoding technique in multi-user multiple-input single-output (MU-MISO) systems with a finite constellation phase-shift keying (PSK) input alphabet. Firstly, analytical expressions for the moment generating function (MGF) and the average of the received signal-to-noise-ratio (SNR) are derived. Then, based on the derived MGF expression the average symbol error probability (SEP) for the CI precoder with PSK signaling is calculated. In this regard, new exact and very accurate asymptotic approximation for the average SEP are provided. Building on the new performance analysis, different power allocation schemes are considered to enhance the achieved SEP. In the first scheme, power allocation based on minimizing the sum symbol error probabilities (Min-Sum) is studied, while in the second scheme the power allocation based on minimizing the maximum SEP (Min-Max) is investigated. Furthermore, new analytical expressions of the throughput and power efficiency of the CI precoding in MU-MISO systems are also derived. The numerical results in this work demonstrate that, the CI precoding outperforms the conventional interference suppression precoding techniques with an up to 20 dB gain in the transmit SNR in terms of SEP, and up to 15 dB gain in the transmit SNR in terms of the throughput. In addition, the SEP-based power allocation schemes provide additional up to 13 dB gains in the transmit SNR compared to the conventional equal power allocation scheme.
Evolution of compact groups from intermediate to final stages

Context. Hickson Compact Group (HCG) 16 is a prototypical compact group of galaxies in an intermediate stage of the previously proposed evolutionary sequence, where its galaxies are losing gas to the intra-group medium (IGrM). The group hosts galaxies that are H I-normal, H I-poor, and centrally active with both AGNs and starbursts, in addition to a likely new member and a H I tidal feature of ∼160 kpc in length. Despite being a well-studied group at all wavelengths, no previous study of HCG 16 has focused on its extraordinary H I component. Aims. The characteristics of HCG 16 make it an ideal case study for exploring which processes are likely to dominate the late stages of evolution in compact groups, and ultimately determine their end states. In order to build a coherent picture of the evolution of this group we make use of the multi-wavelength data available, but focus particularly on H I as a tracer of interactions and evolutionary phase. Methods. We reprocess archival VLA L-band observations of HCG 16 using the multi-scale CLEAN algorithm to accurately recover diffuse features. Tidal features and galaxies are separated in three dimensions using the SlicerAstro package. The H I deficiency of the separated galaxies is assessed against the benchmark of recent scaling relations of isolated galaxies. This work has been performed with particular attention to reproducibility and is accompanied by a complete workflow to reproduce all the final data products, figures, and results. Results. Despite the clear disruption of the H I component of HCG 16 we find that it is not globally H I deficient, even though HCG 16a and b have lost the majority of their H I and almost 50% of the group’s H I is in the IGrM. The H I content of HCG 16d shows highly disturbed kinematics, with only a marginal velocity gradient that is almost perpendicular to its optical major axis. The tail of ∼160 kpc in length extending towards the southeast appears to be part of an even larger structure which spatially and kinematically connects NGC 848 to the northwest corner of the group. Conclusions. This study indicates that in the recent past (∼1 Gyr) galaxies HCG 16a and b likely underwent major interactions that unbound gas without triggering significant star formation. This gas was then swept away by a close, high-speed encounter with NGC 848. The starburst events HCG 16c and d, likely initiated by their mutual interaction, triggered galactic winds which, in the case of HCG 16d, appear to have disrupted its H I reservoir. The tidal features still connected to all these galaxies indicate that more H I will soon be lost to the IGrM, while that which remains in the discs will likely be consumed by star-formation episodes triggered by their ongoing interaction. This is expected to result in a collection of gas-poor galaxies embedded in a diffuse H I structure, which will gradually (over several Gyr) be evaporated by the UV background, resembling the final stage of the evolutionary model of compact groups.
Counterexamples on the Monotonicity of Delay Optimal Strategies for Energy Harvesting Transmitters

We consider cross-layer design of delay optimal transmission strategies for energy harvesting transmitters where the data and energy arrival processes are stochastic. Using Markov decision theory, we show that the value function is weakly increasing in the queue state and weakly decreasing in the battery state. It is natural to expect that the delay optimal policy should be weakly increasing in the queue and battery states. We show via counterexamples that this is not the case. In fact, we show that for some sample scenarios the delay optimal policy may perform 5–13% better than the best monotone policy.
Inferring Dynamical Systems with Long-Range Dependencies through Line Attractor Regularization

Vanilla RNN with ReLU activation have a simple structure that is amenable to systematic dynamical systems analysis and interpretation, but they suffer from the exploding vs. vanishing gradients problem. Recent attempts to retain this simplicity while alleviating the gradient problem are based on proper initialization schemes or orthogonality/unitary constraints on the RNN's recurrence matrix, which, however, comes with limitations to its expressive power with regards to dynamical systems phenomena like chaos or multi-stability. Here, we instead suggest a regularization scheme that pushes part of the RNN's latent subspace toward a line attractor configuration that enables long short-term memory and arbitrarily slow time scales. We show that our approach excels on a number of benchmarks like the sequential MNIST or multiplication problems, and enables reconstruction of dynamical systems which harbor widely different time scales.
Quantum encryption with certified deletion

Given a ciphertext, is it possible to prove the deletion of the underlying plaintext? Since classical ciphertexts can be copied, clearly such a feat is impossible using classical information alone. In stark contrast to this, we show that quantum encodings enable certified deletion. More precisely, we show that it is possible to encrypt classical data into a quantum ciphertext such that the recipient of the ciphertext can produce a classical string which proves to the originator that the recipient has relinquished any chance of recovering the plaintext should the decryption key be revealed. Our scheme is feasible with current quantum technology: the honest parties only require quantum devices for single-qubit preparation and measurements; the scheme is also robust against noise in these devices. Furthermore, we provide an analysis that is suitable in the finite-key regime.
Measuring Attack Surface Reduction in the Presence of Code (Re-)Randomization

Just-in-time return-oriented programming (JIT-ROP) technique allows one to dynamically discover instruction pages and launch code reuse attacks, effectively bypassing most fine-grained address space layout randomization (ASLR) protection. However, in-depth questions regarding the impact of code (re-)randomization on code reuse attacks have not been studied. For example, how do starting pointers in JIT-ROP impact gadget availability? ; how would one compute the re-randomization interval effectively to defeat JIT-ROP attacks? what impact do fine-grained randomization and re-randomization have on the Turing completeness of JIT-ROP payloads? We conduct a comprehensive measurement study on the effectiveness of fine-grained code randomization and re-randomization, with 5 tools, 13 applications, and 19 dynamic libraries. We provide methodologies to measure JIT-ROP gadget availability, quality, and their Turing completeness, as well as to empirically determine the upper bound of re-randomization intervals in re-randomization schemes. Experiments show that instruction reordering is the only fine-grained single-round randomization approach that thwarts current gadget finding techniques under the JIT-ROP threat model. Our results also show that the locations of leaked pointers used in JIT-ROP attacks have no impacts on gadget availability, suggesting high pointer-based connectivity among code pages.
Recurrence is required to capture the representational dynamics of the human visual system

Significance Understanding the computational principles that underlie human vision is a key challenge for neuroscience and could help improve machine vision. Feedforward neural network models process their input through a deep cascade of computations. These models can recognize objects in images and explain aspects of human rapid recognition. However, the human brain contains recurrent connections within and between stages of the cascade, which are missing from the models that dominate both engineering and neuroscience. Here, we measure and model the dynamics of human brain activity during visual perception. We compare feedforward and recurrent neural network models and find that only recurrent models can account for the dynamic transformations of representations among multiple regions of visual cortex. The human visual system is an intricate network of brain regions that enables us to recognize the world around us. Despite its abundant lateral and feedback connections, object processing is commonly viewed and studied as a feedforward process. Here, we measure and model the rapid representational dynamics across multiple stages of the human ventral stream using time-resolved brain imaging and deep learning. We observe substantial representational transformations during the first 300 ms of processing within and across ventral-stream regions. Categorical divisions emerge in sequence, cascading forward and in reverse across regions, and Granger causality analysis suggests bidirectional information flow between regions. Finally, recurrent deep neural network models clearly outperform parameter-matched feedforward models in terms of their ability to capture the multiregion cortical dynamics. Targeted virtual cooling experiments on the recurrent deep network models further substantiate the importance of their lateral and top-down connections. These results establish that recurrent models are required to understand information processing in the human ventral stream.
Research on the Concept of Liquid State Machine

Liquid State Machine (LSM) is a neural model with real time computations which transforms the time varying inputs stream to a higher dimensional space. The concept of LSM is a novel field of research in biological inspired computation with most research effort on training the model as well as finding the optimum learning method. In this review, the performance of LSM model was investigated using two learning method, online learning and offline (batch) learning methods. The review revealed that optimal performance of LSM was recorded through online method as computational space and other complexities associated with batch learning is eliminated.
When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in Spanish

The recognition of pharmacological substances, compounds and proteins is an essential preliminary work for the recognition of relations between chemicals and other biomedically relevant units. In this paper, we describe an approach to Task 1 of the PharmaCoNER Challenge, which involves the recognition of mentions of chemicals and drugs in Spanish medical texts. We train a state-of-the-art BiLSTM-CRF sequence tagger with stacked Pooled Contextualized Embeddings, word and sub-word embeddings using the open-source framework FLAIR. We present a new corpus composed of articles and papers from Spanish health science journals, termed the Spanish Health Corpus, and use it to train domain-specific embeddings which we incorporate in our model training. We achieve a result of 89.76% F1-score using pre-trained embeddings and are able to improve these results to 90.52% F1-score using specialized embeddings.
Optimal Controller and Actuator Design for Nonlinear Parabolic Systems

Many physical systems are modeled by nonlinear parabolic differential equations, such as the Kuramoto-Sivashinsky (KS) equation. In this paper, the existence of a concurrent optimal controller and actuator design is established for semilinear parabolic systems. Optimality equations are provided. The results are shown to apply to optimal controller/actuator design for the Kuramoto-Sivashinsky equation and also nonlinear diffusion.
Channel Aware Sparse Transmission for Ultra Low-Latency Communications in TDD Systems

Major goal of ultra reliable and low latency communication (URLLC) is to reduce the latency down to a millisecond (ms) level while ensuring reliability of the transmission. Since the current uplink transmission scheme requires a complicated handshaking procedure to initiate the transmission, to meet this stringent latency requirement is a challenge in wireless system design. In particular, in the time division duplexing (TDD) systems, supporting the URLLC is difficult since the mobile device has to wait until the transmit direction is switched to the uplink. In this paper, we propose a new approach to support a low latency access in TDD systems, called channel aware sparse transmission (CAST). Key idea of the proposed scheme is to encode a grant signal in a form of sparse vector. This together with the fact that the sensing mechanism preserves the energy of the sparse vector allows us to use the compressed sensing (CS) technique in CAST decoding. From the performance analysis and numerical evaluations, we demonstrate that the proposed CAST scheme achieves a significant reduction in access latency over the 4G LTE-TDD and 5G NR-TDD systems.
NuSTAR and Chandra Observations of New X-Ray Transients in the Central Parsec of the Galaxy

We report NuSTAR and Chandra observations of two X-ray transients, SWIFT J174540.7−290015 (T15) and SWIFT J174540.2−290037 (T37), which were discovered by the Neil Gehrels Swift Observatory in 2016 within r ∼ 1 pc of Sgr A*. NuSTAR detected bright X-ray outbursts from T15 and T37, likely in the soft and hard states, with 3–79 keV luminosities of 8 × 1036 and 3 × 1037 erg s−1, respectively. No X-ray outbursts have previously been detected from the two transients and our Chandra ACIS analysis puts an upper limit of LX ≲ 2 × 1031 erg s−1 on their quiescent 2–8 keV luminosities. No pulsations, significant quasi-periodic oscillations, or type I X-ray bursts were detected in the NuSTAR data. While T15 exhibited no significant red noise, the T37 power density spectra are well characterized by three Lorentzian components. The declining variability of T37 above ν ∼ 10 Hz is typical of black hole (BH) transients in the hard state. NuSTAR spectra of both transients exhibit a thermal disk blackbody, X-ray reflection with broadened Fe atomic features, and a continuum component well described by Comptonization models. Their X-ray reflection spectra are most consistent with high BH spin (a* ≳ 0.9) and large disk density (ne ∼ 1021 cm−3). Based on the best-fit ionization parameters and disk densities, we found that X-ray reflection occurred near the inner-disk radius, which was derived from the relativistic broadening and thermal disk component. These X-ray characteristics suggest the outbursting BH-low-mass X-ray binary scenario for both transients and yield the first BH spin measurements from X-ray transients in the central 100 pc region.