Miracle-dz/UniFAR-FTUs huggingface dataset:
Guiding Inference: Signaling intentions using efficient action

People have a remarkable capacity to infer others’ goals and intentions based on how they behave. Yet, humans are also motivated to ensure that others can infer their mental states easily and accurately. Past work has shown that people achieve this by introducing inefficiencies to their behavior, which reveal its underlying goal (e.g., exaggerating one’s movements so as to make their purpose obvious). We hypothesized that inefficiency is not a constitutive feature of signaling, and that people will often signal their goals and intentions solely through efficient action. We test this idea in a signal-design experiment where participants need to reach an instrumental goal while also making that goal as inferable as possible. In line with our hypothesis, people shape their behavior to increase inferability without jeopardizing efficiency (Experiment 1). Using a computational model, we show that these efficient signals are well-designed to guide observers’ inferences about the relevant instrumental goal. Moreover, observers’ intuitions about which paths were produced to signal correlate with the proportion of times that the paths were generated in the signaling condition of our first experiment (Experiment 2). Our results show that humans not only exploit opportunities to reveal their goals without deviating from efficient action, but that these signals allow observers to understand the instrumental and signaling goals underpinning the movement.
COMPARISON OF TETRAHEDRAL AND HEXAHEDRAL MESHES FOR FINITE ELEMENT SIMULATION OF CARDIAC ELECTRO-MECHANICS

The ﬁnite element method is an attractive method for solving the equations describing heart electrophysiology and mechanics. The ﬁnite element method with unstructured tetrahedral meshes is particularly suitable for representing complex geometrical shapes. On the other hand, hexahedral elements are generally thought to behave better for solid mechanics problems, and in particular for nearly incompressible, large-deformation hyperelasticity, which is normally encountered in the heart models. In this paper we compare the performance of tetrahedral and hexahedral elements for coupled electro-mechanics simulations, by running two different test cases chosen to represent different features of the coupled problem. A cuboid geometry was used to investigate the convergence of different elements for electrophysiology, while a truncated ellipsoid was used to assess the performance for active and passive mechanics. Both tests were run with linear and quadratic tetrahedral elements and with trilinear and triquadratic hexahedra. The results indicate that all four elements show very similar performance for the electrophysiology part of the problem, but linear tetrahedra stand out as the best choice. The differences are more substantial for the mechanics problem, where the linear tetrahedra showed severe mesh locking problems, and the trilinear hexahedra also performed poorly. The two quadratic elements behaved very similarly, with triquadratic hexahedra only slightly better. We may conclude based on these studies that quadratic tetrahedra
Detection Of Wild animals using image processing and PIR Sensor

: One such significant problem that results in fatalities on both sides is the battle between wild animals and common people. This study employs an animal detecting technology to see the stray animals that are entering a field.The technology is set up to constantly monitor the field for the presence of any animals. In this project, animal movement is detected using IR sensors and ultrasonic sensors, which then provide a signal to the controller.This is mainly used to protect the animals from danger posed by humans.The whole structure of the Image processing is detecting the images which enter the field and starts to capture the image and starts analysing with the data set stored in the cloud platform. Once analysed it will detect whether the given image is wild animal or not using search grid method due to its accuracy in this method, once it find the image captured is the wild animal then the algorithm will send the signal to the arduino where it generates the particular frequency which irritates the animal and emmits the frequency using PIR sensor. This type of IOT is used mainly in agriculture and forest areas where people live near forest and travel through forest roads. It wont physically affects the animals nor humans. This will save many animals from accident on forest road.
Deep Learning Super-Resolution Reconstruction of Wall Quantities

Accurate prediction of the fluctuations of wall quantities – wall pressure and wall shear stress – is important for many applications including acoustics, and is strongly influenced by the spatial resolution of the grid in the near-wall region. For large industrial flows at very high Reynolds number, computing a solution on a grid with enough resolution to adequately resolve the small-scale fluctuations that contribute to high-frequency sound is infeasible. As such, a solution with lower spatial resolution is computed and other methods such as reconstruction may be used to estimate the small-scale structures that were not resolved by the grid. Recently proposed reconstruction methods such as super-resolution use deep learning to estimate missing detail and have shown promising results. In this work, super-resolution is applied to reconstruct wall pressure and wall shear stress fluctuations learnt from snapshots of a direct numerical simulation (DNS) of turbulent channel flow at friction Reynolds number Re τ = 395 that were filtered and down-sampled by a factor of four. Mechanisms are identified that allow more generalised application of the method, including dataset preparation and training parameters. With these mechanisms, the super-resolution method is shown to reconstruct instantaneous fields and time-averaged statistics such as energy spectra that are in very good agreement with the DNS.
Bayesian estimation of the Cox model under different hazard rate shape assumptions via slice sampling

ABSTRACT In this paper, we provide a full Bayesian analysis for Cox's proportional hazards model under different hazard rate shape assumptions. To this end, we select the modified Weibull distribution family to model failure rates. A novel Markov chain Monte Carlo method allows one to tackle both exact and right-censored failure time data. Both simulated and real data are used to illustrate the methods.
Crustal Structure of the Western U.S. From Rayleigh and Love Wave Amplification Data

Surface wave amplification measurements have narrower depth sensitivity when compared to more traditional seismic observables such as surface wave dispersion measurements. In particular, Love wave amplification measurements have the advantage of strong sensitivity to the crust. For the first time, we explore the potential of Love wave amplification measurements to image crustal velocity in the western U.S. The effects of overtone interference, radial anisotropy and Moho depth are all explored. Consequently, we present SWUS‐crust, a three‐dimensional shear‐wave velocity model of crustal structure in the western U.S. We use Rayleigh wave amplification measurements in the period range of 38–114 s, along with Love wave amplification measurements in the period range of 38–62 s. We jointly invert over 6,400 multi‐frequency measurements using the Monte‐Carlo based Neighborhood Algorithm, which allows for uncertainty quantification. SWUS‐crust confirms several features observed in previous models, such as high‐velocity anomalies beneath the Columbia basin and low‐velocity anomalies beneath the Basin and Range province. Certain features are sharpened in our model, such as the northern border of the High‐Lava Plains in southern Oregon in the middle crust.
Low Latency Streaming Speech Selection

The field of automatic speech recognition (ASR) has experienced great progress within the past decade [5]. Most of this progress is due to the incorporation of deep learning (DL). The success of deep learning in ASR gave rise to popular, general purpose voice assistants backed by big tech companies such as Amazon, Apple, and Google, and the improvement in ASR technology has allowed voice to become a viable medium for human computer interaction. However, voice for human computer interaction has been largely limited to big tech platforms. While big tech voice assistants have proven to be useful, they tend to be deliberately confined to their respective platforms with similar and limited capabilities. Voice for human computer interaction has the potential to become useful across applications outside of big tech products, but so far adoption seems to be limited. Big tech companies also provide software-as-a-service (SaaS) APIs for ASR so that software developers with either limited expertise or resources can quickly integrate ASR into their applications. However, these APIs have a few considerable downsides. Most importantly from a software development standpoint, they are not transparent in their implementation, they are difficult to customize to fit the needs of the application, and there is no guarantee about the permanency of the API. These factors lead to little control over the quality of the voice recognition system. In a previous project (CSCI2592-C, Learning with Limited Data, Fall 2022), I showed that a custom ASR implementation using open source software and deep-NN models generally outperformed SaaS ASR APIs in the specific domain of speech selection, which will be described momentarily. In this project, I optimize the custom implementation for speech selection to be suited for online streaming. From the user’s perspective, a viable online streaming ASR system should have high accuracy and low latency.
Data reconstruction of two actuarial metrics by staking machine learning models

Objective: A large part of Brazilian’s health care is financed by health insurance plans, which readjustments have been questioned in the courts. The data from court cases tends to not be readily available. Therefore, in order to reconstruct the data, we developed a metric using Deep Learning techniques to obtain data estimations. Method: After analyzing the data obtained from the Regulatory Agency, we trained three different supervised learning algorithms aiming to obtain information through an optimization problem. We used the Augmented Lagrangian method aiming to include the constraints into the cost function and Simulated Annealing to minimize it. Results: Consistent as expected, the stacking performance outperformed the base learners. Conclusions: With the results obtained it was possible to obtain the retroactive average cost per claim and frequency information, fetched from the "health plan's past".
Endosperm Persistence in Arabidopsis Results in Seed Coat Fractures and Loss of Seed Longevity

Seeds are specialized plant organs that carry, nurture, and protect plant offspring. Developmental coordination between the three genetically distinct seed tissues (the embryo, endosperm, and seed coat) is crucial for seed viability. In this study, we explore the relationship between the TFs AtHB25 and ICE1. Previous results identified ICE1 as a target gene of AtHB25. In seeds, a lack of ICE1 (ice1-2) suppresses the enhanced seed longevity and impermeability of the overexpressing mutant athb25-1D, but surprisingly, seed coat lipid polyester deposition is not affected, as shown by the double-mutant athb25-1D ice1-2 seeds. zou-4, another mutant lacking the transcriptional program for proper endosperm maturation and for which the endosperm persists, also presents a high sensitivity to seed aging. Analysis of gso1, gso2, and tws1-4 mutants revealed that a loss of embryo cuticle integrity does not underlie the seed-aging sensitivity of ice1-2 and zou-4. However, scanning electron microscopy revealed the presence of multiple fractures in the seed coats of the ice1 and zou mutants. Thus, this study highlights the importance of both seed coat composition and integrity in ensuring longevity and demonstrates that these parameters depend on multiple factors.
D ON ’ T L OOK A G IFT H ORSE IN THE M OUTH : W HEN THE D ATA Y OU R ECEIVE IS N OT THE D ATA Y OU W ANT

What do you do when the intervention you set out to measure did not make a difference, and the intervention you did not want to measure was more significant than you expected? As instruction librarians, like other teaching faculty, it is critical that we understand our contribution in the classroom: What is effective, and what is not? But unlike teaching faculty who have a full semester with their students, instruction librarians often work within the narrow constraints of a fifty-to-ninety minute one-shot session. It is therefore all the more pressing that we understand best practices to evaluate both our pedagogy and students’ learning experiences. One helpful way to assess our impact is to employ a quasi-experimental research design. Experiments in the classroom are most often used to measure the effect of a particular intervention. In this case, the intervention was an online video series that students watched in addition to receiving an in-person library session. What happens, though, when you advertise and execute a full-blown research study and the intervention measured was completely negligible? What do you do then? This paper will share the author’s experience of executing a quasi-experimental design with 400+ first-year writing students and the dilemma of unwelcomed research results. Roadblocks, lessons learned, and takeaways for other instruction librarians are provided.
observational data bases and for performing multivariate information analysis and log-linear contingency table analysis of sequential and nonsequential data

The sequential and nonsequential analysis of observa­ tional data requires the use of a mathematical model that is appropriate to nominal scale variables. Schlundt (Note 1) discusses this issue and presents multivariate information analysis of contingency tables as a general methodology for analyzing observational data. Infor­ mation theory statistics (Garner & McGill, 1956; McGill, 1954) can be used to partition the uncertainty of a dependent variable in a manner similar to the analysis of variance. A proportional reduction of uncer­ tainty measure can be computed that is analogous to a squared correlation or multiple correlation coefficient. Log-linear models are also used to analyze contin­ gency tables (Goodman, 1970). A log-linear analysis can be used to infer a set of social rules that describe a sample of behavioral interactions (Schlundt, Note 2). Essentially, the model expresses the logarithm of the probability of each cell in a contingency table as a linear combination of parameters. The parameters are analo­ gous to the main effects and interaction effects of analysis of variance. There are procedures for testing the hypothesis that each parameter is equal to zero (Goodman, 1973). Parameters that differ from zero are interpreted as if-then relationships between independent and dependent variables and can be treated as hypoth­ esized social rules. Schlundt (1982) generated samples of role-played heterosexual conversational interactions in which the affect expressed between the partners was systematically varied. He used multivariate information analysis and log-linear contingency table analysis to construct a formal model of the rules governing topic management, tum taking, and the expression of affect in dating conversations. Person and environmental variables were analyzed in conjunction with sequential behavioral variables.
Treatment of Patients with Community-Acquired Pneumonia: Oﬃcial Practice Guideline of the Infectious Diseases and Tropical Medicine Research Center

Context: Community-acquired pneumonia (CAP) refers to pneumonia attained outside the hospital or less than 48 hours before admission, which is a signiﬁcant cause of mortality and morbidity, especially in major comorbidities and older age. Several microorganisms contribute to developing CAP, primarily Streptococcus pneumonia , Haemophilus inﬂuenza , Moraxella catarrhalis , and atypical pathogens, e.g., Mycoplasma pneumonia . The incidence of these microorganisms depends on outpatient or inpatient settings. Administering appropriate treatment among available antibiotics is a critical issue aﬀecting patient survival. Methods: Withamultidisciplinarypanelexpert,thisdocumentoﬀersevidence-basedrecommendationsformanagingCAPinIran. Results: The document evaluated the availability of antimicrobial agents and local antibiotic resistance patterns based on 94 relevant published studies from Google Scholar, Scopus, PubMed, Scientiﬁc Information Database (SID), Iran Medex, Iran doc, Mag Iran, PubMed, and expert opinions. Conclusions: Thepaneladdressedtwomainpartsof rationalrecommendationsformanagingoutpatientsorhospitalizedpatients with CAP.
Mid-Air Haptics: Future Challenges and Opportunities

Ultrasound mid-air haptic technology has advanced in many ways over the past decade and has found meaningful application in a plethora of use cases. As the technology matures further and progresses from lab to market, in this chapter, we take a step back and discuss three specific directions that we think could result in the greatest impact. Namely, we highlight challenges and opportunities in improving (1) the hardware platforms used, (2) the rendering algorithms employed to create rich haptic sensations, and (3) the resulting user experience and added value the technology can instill to different end-user applications. We hope that this “wishlist” inspires the mid-air haptics and human computer interaction (HCI) community and others to join our efforts toward a deeper technology understanding, integration, and readiness.
A comparison of edge-preserving approaches for differential interference contrast microscopy

In this paper we address the problem of estimating the phase from color images acquired with differential-interference-contrast microscopy. In particular, we consider the nonlinear and nonconvex optimization problem obtained by regularizing a least-squares-like discrepancy term with an edge-preserving functional, given by either the hypersurface potential or the total variation one. We investigate the analytical properties of the resulting objective functions, proving the existence of minimum points, and we propose effective optimization tools able to obtain in both the smooth and the nonsmooth case accurate reconstructions with a reduced computational demand.
The Role of Media Use in Political Mobilization: A Comparison of Free and Restrictive Countries

Studies examining the link between media use and political mobilization focus on contexts with high levels of democracy and freedom. This mixed-methods study investigates whether intentions for collective action are predicted by media use and past political participation offline and online in countries categorized as free versus restrictive . Quantitative analyses show that intensive users of offline media were more inclined toward future political
zPoseScore model for accurate and robust protein–ligand docking pose scoring in CASP15

We introduce a deep learning‐based ligand pose scoring model called zPoseScore for predicting protein–ligand complexes in the 15th Critical Assessment of Protein Structure Prediction (CASP15). Our contributions are threefold: first, we generate six training and evaluation data sets by employing advanced data augmentation and sampling methods. Second, we redesign the “zFormer” module, inspired by AlphaFold2's Evoformer, to efficiently describe protein–ligand interactions. This module enables the extraction of protein–ligand paired features that lead to accurate predictions. Finally, we develop the zPoseScore framework with zFormer for scoring and ranking ligand poses, allowing for atomic‐level protein–ligand feature encoding and fusion to output refined ligand poses and ligand per‐atom deviations. Our results demonstrate excellent performance on various testing data sets, achieving Pearson's correlation R = 0.783 and 0.659 for ranking docking decoys generated based on experimental and predicted protein structures of CASF‐2016 protein–ligand complexes. Additionally, we obtain an averaged local distance difference test (lDDT pli = 0.558) of AIchemy LIG2 in CASP15 for de novo protein–ligand complex structure predictions. Detailed analysis shows that accurate ligand binding site prediction and side‐chain orientation are crucial for achieving better prediction performance. Our proposed model is one of the most accurate protein–ligand pose prediction models and could serve as a valuable tool in small molecule drug discovery.
A Bayesian Optimization Approach for Data-Efficient Flexibility Analysis of Expensive Black-Box Models

The performance of a variety of emerging “next-generation” biochemical systems rests on their potential to quickly and accurately adapt to many different sources of uncertainties. Flexibility analysis represents a quantitative framework for determining if a system can maintain safe and feasible operation despite the presence of these uncertainties. The majority of work on flexibility analysis assume access to equation-oriented (white-box) models, which can be very difficult to obtain in many practical applications, especially those involving complex multi-scale models defined in terms of expensive computer simulations. In this paper, we propose a novel black-box flexibility analysis method that can overcome this challenge by simultaneously accounting for uncertain and recourse variables. The proposed method relies on a probabilistic surrogate to jointly model the effect of uncertain and recourse variables on a smooth approximation of the constraint aggregation function (which represents the objective function of interest in flexibility analysis problems). By utilizing confidence bounds predicted from the probabilistic surrogate, we can sequentially design our next sample points in a way that tradeoffs between exploration and exploitation of the unknown system model in the overall search space. Lastly, the advantages of the proposed method are demonstrated on two benchmark problems.
Review Article A Multilevel Framework for Complex Care: A Critical Interpretive Synthesis

Health systems are poorly equipped to respond to complex health and social needs, which span sectors and diagnoses. Tis study puts forward a framework for complex care policy. Te framework was developed using critical interpretive synthesis, a method for developing theory on the basis of a transparent search and critical analysis of a heterogenous body of the literature. Seventy-three results were included from a systematic search. We suggested that complex needs can be understood as a pattern of unmet needs occurring at the intersection of fragmented health systems and services, multimorbidity, and social marginalization. We proposed a multilevel framework to inform complex care policy design that accounts for each of these issues and their intersections at the individual, service, and system level. We further identifed fve principles that have relevance at all levels of complex care. Our framework centres clients and their relationships with providers and suggests how services and systems can support client-level interactions. Conceptualizing complex care policy as a multilevel intervention ofers a tool for understanding unexpected efects. Further work is needed to test and refne this framework and to contextualize it for particular populations and settings.
On local finite-time stabilization of the Viscous Burgers equation via boundary switched linear feedback

— This paper considers the problem of local ﬁnite-time stabilization of the viscous Burgers equation. A boundary switched linear control with state dependent switching law is designed based on the Backstepping approach. The strategy builds on discontinuous kernels which render the control function a piecewise continuous one. It is proved that such a control stabilizes locally the viscous Burgers equation and that the settling time depends on initial conditions. A simulation result is provided to validate the theoretical results.