Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts

Targeted Sentiment Analysis aims to extract sentiment towards a particular target from a given text. It is a field that is attracting attention due to the increasing accessibility of the Internet, which leads people to generate an enormous amount of data. Sentiment analysis, which in general requires annotated data for training, is a well-researched area for widely studied languages such as English. For low-resource languages such as Turkish, there is a lack of such annotated data. We present an annotated Turkish dataset suitable for targeted sentiment analysis. We also propose BERT-based models with different architectures to accomplish the task of targeted sentiment analysis. The results demonstrate that the proposed models outperform the traditional sentiment analysis models for the targeted sentiment analysis task.
Subcritical epidemics on random graphs

We study the contact process on random graphs with low infection rate $\lambda$. For random $d$-regular graphs, it is known that the survival time is $O(\log n)$ below the critical $\lambda_c$. By contrast, on the Erd\H{o}s-R\'enyi random graphs $\mathcal G(n,d/n)$, rare high-degree vertices result in much longer survival times. We show that the survival time is governed by high-density local configurations. In particular, we show that there is a long string of high-degree vertices on which the infection lasts for time $n^{\lambda^{2+o(1)}}$. To establish a matching upper bound, we introduce a modified version of the contact process which ignores infections that do not lead to further infections and allows for a shaper recursive analysis on branching process trees, the local-weak limit of the graph. Our methods, moreover, generalize to random graphs with given degree distributions that have exponential moments.
On Sea-Level Change in Coastal Areas

Variations in sea-level, based on tide gauge data (GSLTG) and on combining tide gauges and satellite data (GSLl), are subjected to singular spectrum analysis (SSA) to determine their trends and periodic or quasi-periodic components. GLSTG increases by 90 mm from 1860 to 2020, a contribution of 0.56 mm/yr to the mean rise rate. Annual to multi-decadal periods of ∼90/80, 60, 30, 20, 10/11, and 4/5 years are found in both GSLTG and GSLl. These periods are commensurable periods of the Jovian planets, combinations of the periods of Neptune (165 yr), Uranus (84 yr), Saturn (29 yr) and Jupiter (12 yr). These same periods are encountered in sea-level changes, the motion of the rotation pole RP and evolution of global pressure GP, suggesting physical links. The first SSA components comprise most of the signal variance: 95% for GSLTG, 89% for GSLl, 98% for GP and 75% for RP. Laplace derived the Liouville–Euler equations that govern the rotation and translation of the rotation axis of any celestial body. He emphasized that one must consider the orbital kinetic moments of all planets in addition to gravitational attractions and concluded that the Earth’s rotation axis should undergo motions that carry the combinations of periods of the Sun, Moon and planets. Almost all the periods found in the SSA components of sea-level (GSLl and GSLTG), global pressure (GP) and polar motion (RP), of their modulations and their derivatives can be associated with the Jovian planets. The trends themselves could be segments of components with still longer periodicities (e.g., 175 yr Jose cycle).
Some performance considerations when using multi-armed bandit algorithms in the presence of missing data

When comparing the performance of multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, it also affects their implementation where the simplest approach to overcome this is to continue to sample according to the original bandit algorithm, ignoring missing outcomes. We investigate the impact on performance of this approach to deal with missing data for several bandit algorithms through an extensive simulation study assuming the rewards are missing at random. We focus on two-armed bandit algorithms with binary outcomes in the context of patient allocation for clinical trials with relatively small sample sizes. However, our results apply to other applications of bandit algorithms where missing data is expected to occur. We assess the resulting operating characteristics, including the expected reward. Different probabilities of missingness in both arms are considered. The key finding of our work is that when using the simplest strategy of ignoring missing data, the impact on the expected performance of multi-armed bandit strategies varies according to the way these strategies balance the exploration-exploitation trade-off. Algorithms that are geared towards exploration continue to assign samples to the arm with more missing responses (which being perceived as the arm with less observed information is deemed more appealing by the algorithm than it would otherwise be). In contrast, algorithms that are geared towards exploitation would rapidly assign a high value to samples from the arms with a current high mean irrespective of the level observations per arm. Furthermore, for algorithms focusing more on exploration, we illustrate that the problem of missing responses can be alleviated using a simple mean imputation approach.
A Magnetogram-matching Method for Energizing Magnetic Flux Ropes Toward Eruption

We propose a new “helicity-pumping” method for energizing coronal equilibria that contain a magnetic flux rope (MFR) toward an eruption. We achieve this in a sequence of magnetohydrodynamics relaxations of small line-tied pulses of magnetic helicity, each of which is simulated by a suitable rescaling of the current-carrying part of the field. The whole procedure is “magnetogram-matching” because it involves no changes to the normal component of the field at the photospheric boundary. The method is illustrated by applying it to an observed force-free configuration whose MFR is modeled with our regularized Biot–Savart law method. We find that, in spite of the bipolar character of the external field, the MFR eruption is sustained by two reconnection processes. The first, which we refer to as breakthrough reconnection, is analogous to breakout reconnection in quadrupolar configurations. It occurs at a quasi-separator inside a current layer that wraps around the erupting MFR and is caused by the photospheric line-tying effect. The second process is the classical flare reconnection, which develops at the second quasi-separator inside a vertical current layer that is formed below the erupting MFR. Both reconnection processes work in tandem with the magnetic forces of the unstable MFR to propel it through the overlying ambient field, and their interplay may also be relevant for the thermal processes occurring in the plasma of solar flares. The considered example suggests that our method will be beneficial for both the modeling of observed eruptive events and theoretical studies of eruptions in idealized magnetic configurations.
Learning Disentangled Textual Representations via Statistical Measures of Similarity

When working with textual data, a natural application of disentangled representations is the fair classification where the goal is to make predictions without being biased (or influenced) by sensible attributes that may be present in the data (e.g., age, gender or race). Dominant approaches to disentangle a sensitive attribute from textual representations rely on learning simultaneously a penalization term that involves either an adversary loss (e.g., a discriminator) or an information measure (e.g., mutual information). However, these methods require the training of a deep neural network with several parameter updates for each update of the representation model. As a matter of fact, the resulting nested optimization loop is both times consuming, adding complexity to the optimization dynamic, and requires a fine hyperparameter selection (e.g., learning rates, architecture). In this work, we introduce a family of regularizers for learning disentangled representations that do not require training. These regularizers are based on statistical measures of similarity between the conditional probability distributions with respect to the sensible attributes. Our novel regularizers do not require additional training, are faster and do not involve additional tuning while achieving better results both when combined with pretrained and randomly initialized text encoders.
BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck

Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machine learning-based classifiers using functional connectivity (FC) for psychiatric disorders and healthy controls are developed to identify brain markers. However, existing machine learningbased diagnostic models are prone to over-fitting (due to insufficient training samples) and perform poorly in new test environment. Furthermore, it is difficult to obtain explainable and reliable brain biomarkers elucidating the underlying diagnostic decisions. These issues hinder their possible clinical applications. In this work, we propose BrainIB, a new graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI), by leveraging the famed Information Bottleneck (IB) principle. BrainIB is able to identify the most informative edges in the brain (i.e., subgraph) and generalizes well to unseen data. We evaluate the performance of BrainIB against 8 popular brain network classification methods on two multi-site, largescale datasets and observe that our BrainIB always achieves the highest diagnosis accuracy. It also discovers the subgraph biomarkers which are consistent to clinical and neuroimaging findings.
Strong Neel Ordering and Luminescence Correlation in a Two‐Dimensional Antiferromagnet

Magneto‐optical effect has been widely used in light modulation, optical sensing, and information storage. Recently discovered 2D van der Waals layered magnets are considered as promising platforms for investigating novel magneto‐optical phenomena and devices, due to the long‐range magnetic ordering down to atomically thin thickness, rich species, and tunable properties. However, majority 2D antiferromagnets suffer from low luminescence efficiency which hinders their magneto‐optical investigations and applications. This work uncovers strong light‐magnetic ordering interactions in 2D antiferromagnetic MnPS3 using a newly‐emerged near‐infrared photoluminescence (PL) mode far below its intrinsic bandgap. This ingap PL mode shows strong correlation with the Neel ordering and persists down to monolayer thickness. Combining the density‐functional theory (DFT), scanning transmission electron microscopy (STEM), and X‐ray photoelectron spectroscopy (XPS), this work illustrates the origin of the PL mode and its correlation with Neel ordering, which can be attributed to the oxygen ion‐mediated states. Moreover, the PL strength can be further tuned and enhanced using ultraviolet‐ozone (UVO) treatment. The studies offer an effective approach to investigate light‐magnetic ordering interactions in 2D antiferromagnetic semiconductors.
Confining strings, axions and glueballs in the planar limit

We present recent results on the spectrum of a conﬁning ﬂux-tube that is closed around a spatial torus as a function of its length as well as the spectrum of glueballs. The extraction of the spectra has been realized by simulating four dimensional 𝑆𝑈 ( 𝑁 ) gauge theories and performing measurements using lattice techniques. Regarding ﬂux-tubes, we have performed calculations for 𝑁 = 3 , 5 , 6 and for various values of spin, parity and longitudinal momentum. Long ﬂux-tubes can be thought of as inﬁnitesimally thin strings; hence their spectrum is expected to be described by an eﬀective string theory. Furthermore, the ﬂux-tube’s internal structure makes possible the existence of massive states in addition to string modes. Our calculations demonstrate that although most states exhibit a spectrum which can be approximated adequately by Nambu-Goto there is strong evidence for the existence of a massive axion on the world-sheet of the QCD ﬂux-tube as well as a bound state of two such axions. Regarding glueballs, we extracted spectra from 𝑁 = 2 to 𝑁 = 12 which enables us to extrapolate to 𝑁 = ∞ . Our main aim was to calculate the lightest glueball masses for all diﬀerent conﬁgurations of the quantum numbers of spin, parity and charge conjugation. This provides a major update on the spectrum of glueballs in the planar limit.
Complementary-Switchable Dual-Mode SHF Scandium Aluminum Nitride BAW Resonator

This article presents a bulk acoustic wave (BAW) resonator with complementary switchable operation in the first and second thickness extensional modes (TE<sub>1</sub> and TE<sub>2</sub>) at 7.04 and 13.4 GHz. Two ferroelectric scandium aluminum nitride (Sc<sub>0.28</sub>Al<sub>0.72</sub>N) layers are alternatively stacked with three molybdenum electrodes, creating a laminated BAW resonator with independent switchability of polarization in constituent transducers. This enables intrinsic switchability of the resonator in TE<sub>1</sub> and TE<sub>2</sub> modes, when the ferroelectric Sc<sub>0.28</sub>Al<sub>0.72</sub>N layers are poled in the same or opposite directions, respectively. A generalized analytical proof of complementary switchable operation, extended to laminated BAW resonators consisting of arbitrary number of Sc<sub><italic>x</italic></sub>Al<sub>1-<italic>x</italic></sub>N layers, is presented. For the demonstrated prototype, electromechanical coupling coefficients (<inline-formula> <tex-math notation="LaTeX">${k}_{t}^{2}$ </tex-math></inline-formula>) of 10.1% and 10.7%, and quality factors (<inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>) of 115 and 151, are measured for TE<sub>1</sub> and TE<sub>2</sub> modes, respectively, when the resonator is configured in the corresponding operation states. Besides showing intrinsically configurable operation in super-high-frequency regime with high <inline-formula> <tex-math notation="LaTeX">${k}_{t}^{\textbf {2}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>, a laminated Sc<sub>0.28</sub>Al<sub>0.72</sub>N BAW resonator exhibits repeatable operation under switching cycles.
Damage Maximization for Combat Network With Limited Costs

Maximizing the damage by attacking specific nodes of the combat network can efficiently disrupt enemies’ defense capability, protect our critical units, and enhance the resistance to the destruction of system-of-system (SOS). However, the modeling of the combat network damage is not practical enough. In this paper, we report a more realistic model to study the combat network damage maximization problems. By analyzing realistic situations, the cost of damage is redefined based on the network topology and the functional characteristics of nodes. The damage effect is also updated according to the combat network topology and operational capability. Hence, a cost-limited damage maximization model for the combat network is constructed. In addition, to obtain optimal solutions, an improved genetic algorithm (IPGA) based on prior information is proposed. As a result, our method has a significant advantage in the feasibility and effectiveness compared with other algorithms in experiments. The attack pattern of the combat network and the convergence and complexity of the proposed algorithm are further explored. The improved model and algorithm, as well as the mined attack patterns, can provide support for military decisions.
The First Short GRB Millimeter Afterglow: The Wide-angled Jet of the Extremely Energetic SGRB 211106A

We present the discovery of the first millimeter afterglow of a short-duration γ-ray burst (SGRB) and the first confirmed afterglow of an SGRB localized by the GUANO system on Swift. Our Atacama Large Millimeter/Sub-millimeter Array (ALMA) detection of SGRB 211106A establishes an origin in a faint host galaxy detected in Hubble Space Telescope imaging at 0.7 ≲ z ≲ 1.4. From the lack of a detectable optical afterglow, coupled with the bright millimeter counterpart, we infer a high extinction, A V ≳ 2.6 mag along the line of sight, making this one of the most highly dust-extincted SGRBs known to date. The millimeter-band light curve captures the passage of the synchrotron peak from the afterglow forward shock and reveals a jet break at tjet=29.2−4.0+4.5 days. For a presumed redshift of z = 1, we infer an opening angle, θ jet = (15.°5 ± 1.°4), and beaming-corrected kinetic energy of log(EK/erg)=51.8±0.3 , making this one of the widest and most energetic SGRB jets known to date. Combining all published millimeter-band upper limits in conjunction with the energetics for a large sample of SGRBs, we find that energetic outflows in high-density environments are more likely to have detectable millimeter counterparts. Concerted afterglow searches with ALMA should yield detection fractions of 24%–40% on timescales of ≳2 days at rates of ≈0.8–1.6 per year, outpacing the historical discovery rate of SGRB centimeter-band afterglows.
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation

Standard automatic metrics, e.g. BLEU, are not reliable for document-level MT evaluation. They can neither distinguish document-level improvements in translation quality from sentence-level ones, nor identify the discourse phenomena that cause context-agnostic translations. This paper introduces a novel automatic metric BlonDe to widen the scope of automatic MT evaluation from sentence to document level. BlonDe takes discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans. We conduct extensive comparisons on a newly constructed dataset BWB. The experimental results show that BlonDe possesses better selectivity and interpretability at the document-level, and is more sensitive to document-level nuances. In a large-scale human study, BlonDe also achieves significantly higher Pearson’s r correlation with human judgments compared to previous metrics.
Extending mixture of experts model to investigate heterogeneity of trajectories: When, where, and how to add which covariates.

Researchers are usually interested in examining the impact of covariates when separating heterogeneous samples into latent classes that are more homogeneous. The majority of theoretical and empirical studies with such aims have focused on identifying covariates as predictors of class membership in the structural equation modeling framework. In other words, the covariates only indirectly affect the sample heterogeneity. However, the covariates' influence on between-individual differences can also be direct. This article presents a mixture model that investigates covariates to explain within-cluster and between-cluster heterogeneity simultaneously, known as a mixture-of-experts (MoE) model. This study aims to extend the MoE framework to investigate heterogeneity in nonlinear trajectories: to identify latent classes, covariates as predictors to clusters, and covariates that explain within-cluster differences in change patterns over time. Our simulation studies demonstrate that the proposed model generally estimates the parameters unbiasedly, precisely, and exhibits appropriate empirical coverage for a nominal 95% confidence interval. This study also proposes implementing structural equation model forests to shrink the covariate space of the proposed mixture model. We illustrate how to select covariates and construct the proposed model with longitudinal mathematics achievement data. Additionally, we demonstrate that the proposed mixture model can be further extended in the structural equation modeling framework by allowing the covariates that have direct effects to be time-varying. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Centimeter-wave Free-space Neural Time-of-Flight Imaging

Depth sensors have emerged as a cornerstone sensor modality with diverse applications in personal hand-held devices, robotics, scientific imaging, autonomous vehicles, and more. In particular, correlation Time-of-Flight (ToF) sensors have found widespread adoption for meter-scale indoor applications such as object tracking and pose estimation. While they offer high depth resolution at competitive costs, the precision of these indirect ToF sensors is fundamentally limited by their modulation contrast, which is in turn limited by the effects of photo-conversion noise. In contrast, optical interferometric methods can leverage short illumination modulation wavelengths to achieve depth precision three orders of magnitude greater than ToF, but typically find their range is restricted to the sub-centimeter. In this work, we merge concepts from both correlation ToF design and interferometric imaging; a step towards bridging the gap between these methods. We propose a computational ToF imaging method that optically computes the GHz ToF correlation signal in free space before photo-conversion. To acquire a depth map, we scan a scene point-wise and computationally unwrap the collected correlation measurements. Specifically, we repurpose electro-optical modulators used in optical communication for ToF imaging with centimeter-wave signals, and achieve all-optical correlation at 7.15 GHz and 14.32 GHz modulation frequencies. While GHz modulation frequencies increase depth precision, these high modulation rates also pose a technical challenge. They result in dozens of wraps per meter which cannot be estimated robustly by existing phase unwrapping methods. We tackle this problem with a proposed segmentation-inspired phase unwrapping network, which exploits the correlation of adjacent GHz phase measurements to classify regions into their respective wrap counts. We validate this method in simulation and experimentally, and demonstrate precise depth sensing using centimeter wave modulation that is robust to surface texture and ambient light. Compared to existing analog demodulation methods, the proposed system outperforms all of them across all tested scenarios. CCS Concepts: • Computing methodologies;
The mosaic of AII amacrine cell bodies in rat retina is indistinguishable from a random distribution

Abstract The vertebrate retina contains a large number of different types of neurons that can be distinguished by their morphological properties. Assuming that no location should be without a contribution from the circuitry and function linked to a specific type of neuron, it is expected that the dendritic trees of neurons belonging to a type will cover the retina in a regular manner. Thus, for most types of neurons, the contribution to visual processing is thought to be independent of the exact location of individual neurons across the retina. Here, we have investigated the distribution of AII amacrine cells in rat retina. The AII is a multifunctional amacrine cell found in mammals and involved in synaptic microcircuits that contribute to visual processing under both scotopic and photopic conditions. Previous investigations have suggested that AIIs are regularly distributed, with a nearest-neighbor distance regularity index of ~4. It has been argued, however, that this presumed regularity results from treating somas as points, without taking into account their actual spatial extent which constrains the location of other cells of the same type. When we simulated random distributions of cell bodies with size and density similar to real AIIs, we confirmed that the simulated distributions could not be distinguished from the distributions observed experimentally for AIIs in different regions and eccentricities of the retina. The developmental mechanisms that generate the observed distributions of AIIs remain to be investigated.