Miracle-dz/UniFAR-FTUs huggingface dataset:
I Don't Care Anymore: Identifying the Onset of Careless Responding

Questionnaires in the behavioral and organizational sciences tend to be lengthy: survey measures comprising hundreds of items are the norm rather than the exception. However, recent literature suggests that the longer a questionnaire takes, the higher the probability that participants lose interest and start responding carelessly. Consequently, in long surveys a large number of participants may engage in careless responding, posing a major threat to internal validity. We propose a novel method to identify the onset of careless responding (or an absence thereof) for each participant. Specifically, our method is based on combined measurements of up to three dimensions in which carelessness may manifest (inconsistency, invariability, fast responding). Since a structural break in either dimension is potentially indicative of carelessness, our method searches for evidence for changepoints along the three dimensions. Our method is highly flexible, based on machine learning, and provides statistical guarantees on its performance. In simulation experiments, we find that it achieves high reliability in correctly identifying carelessness onset, discriminates well between careless and attentive respondents, and can capture a wide variety of careless response styles, even in datasets with an overwhelming presence of carelessness. In addition, we empirically validate our method on a Big 5 measurement. Furthermore, we provide freely available software in R to enhance accessibility and adoption by empirical researchers.
Advantages of using gold hollow nanoshells in cancer photothermal therapy

Lots of studies have been conducted on the optical properties of gold nanoparticles in the first region of near infrared (650 nm–950 nm), however new findings show that the second region of near-infrared (1000 nm–1350 nm) penetrates to the deeper tissues of the human body. Therefore, using the above-mentioned region in photo-thermal therapy (PTT) of cancer will be more appropriate. In this paper, absorption efficiency is calculated for gold spherical and rod-shaped nanoshells by the finite element method (FEM). The results show that the surface plasmon frequency of these nanostructures is highly dependent on the dimension and thickness of shell and it can be adjusted to the second region of near-infrared. Thus, due to their optical tunability and their high absorption efficiency the hollow nanoshells are the most appropriate options for eradicating cancer tissues.
Token Sparsification for Faster Medical Image Segmentation

Can we use sparse tokens for dense prediction, e.g., segmentation? Although token sparsification has been applied to Vision Transformers (ViT) to accelerate classification, it is still unknown how to perform segmentation from sparse tokens. To this end, we reformulate segmentation as a sparse encoding ->token completion ->dense decoding (SCD) pipeline. We first empirically show that naively applying existing approaches from classification token pruning and masked image modeling (MIM) leads to failure and inefficient training caused by inappropriate sampling algorithms and the low quality of the restored dense features. In this paper, we propose Soft-topK Token Pruning (STP) and Multi-layer Token Assembly (MTA) to address these problems. In sparse encoding, STP predicts token importance scores with a lightweight sub-network and samples the topK tokens. The intractable topK gradients are approximated through a continuous perturbed score distribution. In token completion, MTA restores a full token sequence by assembling both sparse output tokens and pruned multi-layer intermediate ones. The last dense decoding stage is compatible with existing segmentation decoders, e.g., UNETR. Experiments show SCD pipelines equipped with STP and MTA are much faster than baselines without token pruning in both training (up to 120% higher throughput and inference up to 60.6% higher throughput) while maintaining segmentation quality.
A Human Subject Study of Named Entity Recognition in Conversational Music Recommendation Queries

We conducted a human subject study of named entity recognition on a noisy corpus of conversational music recommendation queries, with many irregular and novel named entities. We evaluated the human NER linguistic behaviour in these challenging conditions and compared it with the most common NER systems nowadays, fine-tuned transformers. Our goal was to learn about the task to guide the design of better evaluation methods and NER algorithms. The results showed that NER in our context was quite hard for both human and algorithms under a strict evaluation schema; humans had higher precision, while the model higher recall because of entity exposure especially during pre-training; and entity types had different error patterns (e.g. frequent typing errors for artists). The released corpus goes beyond predefined frames of interaction and can support future work in conversational music recommendation.
Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers

The complex events observed at the NOvA long-baseline neutrino oscillation experiment contain vital information for understanding the most elusive particles in the standard model. The NOvA detectors observe interactions of neutrinos from the NuMI beam at Fermilab. Associating the particles produced in these interaction events to their source particles, a process known as reconstruction, is critical for accurately measuring key parameters of the standard model. Events may contain several particles, each producing sparse high-dimensional spatial observations, and current methods are limited to evaluating individual particles. To accurately label these numerous, high-dimensional observations, we present a novel neural network architecture that combines the spatial learning enabled by convolutions with the contextual learning enabled by attention. This joint approach, TransformerCVN, simultaneously classifies each event and reconstructs every individual particle's identity. TransformerCVN classifies events with 90\% accuracy and improves the reconstruction of individual particles by 6\% over baseline methods which lack the integrated architecture of TransformerCVN. In addition, this architecture enables us to perform several interpretability studies which provide insights into the network's predictions and show that TransformerCVN discovers several fundamental principles that stem from the standard model.
Optimal parallel wire cutting without ancilla qubits

The circuit cutting techniques are widely used to execute quantum algorithms that require more qubits than physically available; the idea is to decompose the original circuit to smaller sub-circuits that are combined to simulate the original one by sampling. However, they suffer from an inevitable issue that the sampling cost exponentially grows with the number of cuts, and thus it is practically important to develop decomposition methods working with smaller sampling cost. This paper focuses on the parallel wire-cutting problem, meaning that the target to cut is the identity channel acting on multiple qubits in parallel. There are two previous studies. The first one is a teleportation-based method that achieves this task with the use of ancilla qubits, whose sampling cost was proven to be optimal. The second one uses the technique of classical shadow to solve the problem without any ancilla qubits, which however does not achieve the optimal sampling cost. This paper gives a definitive solution to this problem. That is, we develop the optimal parallel wire cutting without ancilla qubits, in the same setup as that taken in the above previous studies. Moreover, the developed ancilla-free method can be applied to the non-parallel case, which also improves the sampling cost obtained in the previous study.
Causal Mediation Analysis with a Three-Dimensional Image Mediator

Causal mediation analysis is increasingly abundant in biology, psychology, and epidemiology studies, etc. In particular, with the advent of the big data era, the issue of high-dimensional mediators is becoming more prevalent. In neuroscience, with the widespread application of magnetic resonance technology in the field of brain imaging, studies on image being a mediator emerged. In this study, a novel causal mediation analysis method with a three-dimensional image mediator is proposed. We define the average casual effects under the potential outcome framework, explore several sufficient conditions for the valid identification, and develop techniques for estimation and inference. To verify the effectiveness of the proposed method, a series of simulations under various scenarios is performed. Finally, the proposed method is applied to a study on the causal effect of mother$^{\prime}$s delivery mode on child$^{\prime}$s IQ development. It is found that the white matter in certain regions of the frontal-temporal areas has mediating effects.
Spatially resolved observations of outflows in the radio loud AGN of UGC 8782

We use optical Integral Field Spectroscopy (IFU) to study the gas emission structure and kinematics in the inner 3.4 × 4.9 kpc2 region of the galaxy UGC 8782 (3C 293), host of a radio loud Active Galactic Nucleus (AGN). The observations were performed with the GMOS-IFU on the Gemini North telescope, resulting in a spatial resolution of ∼725 pc at the distance of the galaxy. While the stars present ordered rotation following the orientation of the large scale disc, the gas shows a disturbed kinematics. The emission-line profiles present two kinematic components: a narrow (σ ≲ 200 km s−1) component associated with the gas in the disc of the galaxy and a broad (σ ≳ 200 km s−1) component produced by gas outflows. Emission-line ratio diagrams indicate that the gas in the disc is excited by the AGN radiation field, while the emission of the outflow includes additional contribution of shock excitation due to the interaction of the radio jet with the environment gas. Deviations from pure rotation, of up to 30 km s−1, are observed in the disc component and likely produced by a previous merger event. The broad component is blueshifted by ∼150–500 km s−1 relative to the systemic velocity of the galaxy in all locations. We construct radial profiles of the mass outflow rate and kinetic power of the ionized gas outflows, which have the maximum values at ∼1 kpc from the nucleus with peak values of $\dot{M}_{\rm out,\Delta R} = 0.5\pm 0.1$ M⊙ yr−1 and $\dot{K}_{\rm out,\Delta R} = (6.8\pm 1.1)\times 10^{41}$ erg s−1. The kinetic coupling efficiency of these outflows are in the range of 1–3 per cent, indicating that they could be powerful enough to affect the star formation in the host galaxy as predicted by theoretical simulations.
Primordial nucleosynthesis redux

The abundances of D, He-3, He-4, and Li-7, are presently recalculated within the framework of primordial nucleosynthesis in the standard hot big band model, in order to estimate the primordial abundances of the light elements. A comparison between theory and experiment demonstrates the consistency of standard model predictions; the baryon density parameter is constrained on the basis of a nucleon-to-photon ratio of 2.8-4.0. These bounds imply that the bulk of the baryons in the universe are dark, requiring that the universe be dominated by nonbaryonic matter.
A 10 micron survey of star formation in galactic nuclei Virgo spiral galaxies

A survey for 10 micron emission in the nuclei of spiral galaxies in the Virgo cluster is reported. It is found that the nuclei of most luminous spiral galaxies are active 10 micron emitters, and that the luminosity function for the sample can be represented by a simple power law over the entire range sampled. The most luminous sources at 10 microns show only a weak correlation with total optical luminosity, and no correlation with morphological type or location in the cluster. It is argued that the 10 micron flux from the Virgo galaxies arises from active star formation regions. A mean far-IR luminosity of two billion solar luminosities is inferred for the galactic nuclei; the required rate of massive star formation is 0.1 solar masses/yr. The results suggest that the rate of star formation in galactic nuclei has little relationship to the size of the nuclear bulge or the gravitational potential near the center.
Six Online Statistics Courses: Examination and Review

We extend George W. Cobb's evaluative framework of statistics textbooks to six online instructional materials that exemplify the diversity of introductory statistics materials available on the Internet. Materials range from course Web sites with limited interactive capabilities to courseware and electronic textbooks that make extensive use of interactive learning objects and environments. Instructional materials are examined in light of recent cognitive research that underscores the robustness of learning from examples, the importance of authentic problem solving in promoting knowledge in use and skill acquisition, and the use of feedback to maximize learning opportunities. Selected units that focus on statistical tools (measures of central tendency, simple linear regression, and one-way analysis of variance) are analyzed in terms of authenticity and diversity of examples, authenticity and cognitive complexity of exercises, and use of interactive learning objects and feedback. General conclusions and suggestions for future directions for online statistics instruction are presented.
BICEP /

We characterize Galactic dust ﬁlaments by correlating BICEP/Keck and Planck data with polarization templates based on neutral hydrogen (H i ) observations. Dust polarization is important for both our understanding of astrophysical processes in the interstellar medium (ISM) and the search for primordial gravitational waves in the cosmic microwave background (CMB). In the diﬀuse ISM,
Hot gas in elliptical galaxies and the formation of head-tail radio sources

Elliptical galaxies moving through clusters seem likely to retain a significant interstellar medium (ISM). A simple estimate gives this region a characteristic radius r/sub s/approx. =10 kpc. Within this radius the influence of the ISM must be included in any model of radio source structure. Since this scale is similar to that of the small-scale structure now being seen in head-tail radio sources, it suggests that the interstellar medium may be important in the formation of these sources. In addition, there are rather serious energetic difficulties with independent plasmoid models of such sources which have been considerably exacerbated by recent VLA observations. These difficulties are present with or without the inclusion of an ISM. An alternative model of head-tail sources is suggested in which nonrelativistic or mildly relativistic beams are deflected by transverse pressure gradients induced within the interstellar medium by the galactic motion. If the beam Mach number is not too large, the beam material may be deflected into the galactic wake. Such a model seems dynamically and energetically plausible. It is further suggested that the large-scale structure of a tailed source may be energized by turbulence in the galactic wake.
Definition, Häufigkeit und Ursachen der Dystokie beim Pferd – eine Literaturübersicht

Zusammenfassung Die Geburt beim Pferd wird dann als Dystokie bezeichnet, wenn eine Gefahr der Schädigung von Stute oder Fohlen durch den Geburtsvorgang ausgeht oder die Notwendigkeit der menschlichen Hilfe zur Beendigung der Geburt vorliegt oder zeitliche Abweichungen in der physiologischen Dauer der Öffnungs- und/oder Austreibungsphase auftreten. Dabei besitzt die Dauer der Austreibungsphase zur Erkennung der Dystokie herausragende Bedeutung, da diese am Verhalten der Stute gut erkannt werden kann. Die Dystokie beim Pferd wird als Notfall eingestuft, bei dem unmittelbare Lebensgefahr für die Stute und das Fohlen besteht. Die Angaben zur Dystokiehäufigkeit unterscheiden sich stark. In Untersuchungen, die in Gestüten durchgeführt wurden, tritt eine Dystokie in 2–13% bezogen auf alle Geburten ohne Berücksichtigung einer potenziellen Rassedisposition auf. Abweichungen von der gestreckten Haltung der Gliedmaßen und des Halses während der Geburt werden als häufigste Ursachen einer Schwergeburt beim Pferd klassifiziert. Der tierartspezifische lange Hals und die langen Gliedmaßen werden als Ursachen für diese Beobachtung angesehen.
Wafer-bonded surface plasmon waveguides

Direct wafer bonding and thinning were explored as an approach for constructing long-range surface plasmon waveguides. The structures consist of a thin metal stripe deposited into a shallow trench etched into one of the claddings, to which another cladding of the same material is directly bonded. The approach was developed first using Pyrex wafers in order to assess feasibility and then using lithium niobate wafers. Optical and electro-optical measurements validate the approach.
How could a weighted drug-drug network help improve adverse drug reaction predictions? Machine learning reveals the importance of edge weights

Recent years have witnessed the booming data of drugs and their associated adverse drug reactions (ADRs), resulting in a comparatively high hospitalization rate worldwide. Therefore, a tremendous amount of research has been done to predict ADRs to keep the risks at a minimum. Due to the nature of the lab experiments being costly and time-consuming, researchers are looking forward to more extensive use of data mining and machine learning techniques in this field. This paper constructs a weighted drug-drug network based on an integration of various data sources. The network presents underlying relationships between drugs by creating connections between them according to their common ADRs. Then multiple node-level and graph-level network features are extracted from this network, e.g. weighted degree centrality, weighted PageRanks etc. By concatenating these features to the original drug features, it could be made possible to train and test seven classical machine learning algorithms, e.g. Logistic Regression, Random Forest, Support Vector Machine, etc. The experiments conclude that all the tested machine learning methods would benefit from adding those network measures, and the logistic regression (LR) model provides the highest mean AUROC score (0.821) across all ADRs in the experiment. Weighted degree centrality and weighted PageRanks are identified to be the most important network features in the LR classifier. These shreds of evidence strongly support that the network approach could be fundamental in future ADR prediction, and the network edge weights are important in the logistic regression model.
TTPHunter: Automated Extraction of Actionable Intelligence as TTPs from Narrative Threat Reports

With the proliferation of attacks from various Advanced Persistent Threats (APT) groups, it is essential to comprehend the threat actor’s attack patterns to accelerate threat detection and response. The MITRE ATT&CK framework’s Tactics, Techniques, and Procedures (TTPs) help to decipher attack patterns. The APT reports, published by security firms, contain rich information on tools and techniques used by threat actors. These reports are available in unstructured and natural language texts. There is a need for an automated tool to extract TTPs present in natural language text. However, there are few tools available in the literature, but their performance is not very satisfactory. In this work, we propose TTPHunter, to extract TTPs from APT reports by mapping sentence context to relevant TTPs. We fine-tune linear classifiers, which take input as BERT (Bidirectional Encoder Representations from Transformers) embeddings of sentences. We create two datasets: sentence-based (8,387 sentence samples) and document-based (50 threat reports) to validate TTPHunter. TTPHunter achieves the F1-score of 88% and 75% for both datasets, respectively. We compare the TTPHunter with rcATT and AttacKG baseline models, and it outperforms both baselines.
A Generalized Methodology for Well Island Generation and Well-tap Insertion in Analog/Mixed-signal Layouts

Well island generation and well tap placement is an important problem in analog/mixed-signal (AMS) circuits. Well taps can only prevent latchups within a certain radius of influence within a well island, and hence must be appropriately inserted to cover all devices. However, existing automated AMS layout paradigms typically defer the insertion of well taps and creation of well islands to a post-processing step after placement. This alters the placement, resulting in increased area and wire length, as well as circuit performance degradation. Therefore, there is a strong need for a solution that generates well islands and inserts well taps during placement so the placer can account for well overheads in optimizing placement metrics. In this work, we propose a modular solution using a graph-based optimization scheme that can be used within multiple placement paradigms with minimal intrusion. We demonstrate the integration of this scheme into stochastic, analytical, and designer-driven row-based placement. The method is demonstrated in advanced FinFET technologies. Layouts generated using this scheme show better area, wire length, and performance metrics at the cost of a marginal runtime degradation when compared to the post-processing approach. Using our scheme, there is an average improvement of 3% and 4% and a maximum improvement of 23% and 11% in area and wirelength, respectively, of layouts of various classes of AMS circuits at the cost of 17% average and 29% maximum increase in total runtime.
AProctor - A practical on-device antidote for Android malware

As the number of smartphone users increases, the attacker’s interest in breaching Android security also increases. To protect the user from malware attacks and enhance his mobile security, we present AProctor, an Android application capable of differentiating between benign and malware applications on the Android device. AProctor is a serverless solution. Hence it is a complete on-device solution. Indeed famous anomaly-based detection can protect the user from new and unknown malware, but it has its downsides. Anomaly-based algorithms demand a significant amount of energy and resource, but the platform for which we are developing malware detectors is resource-constrained Android phones. So for this, we first implemented a fast and efficient way to extract features in the Android device. We collect the dataset from various repositories and categorize them according to their packaging time. We use the feature importance technique to reduce the feature set to build lightweight models. We train our model on the server and deploy it on the device. The detection rate of AProctor is 97% when the training and testing sets are of the same period. Moreover, the detection rate comes to 92% when the test set is of a different period, i.e., for unknown APKs.