Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Choice Complexity on Design Efficiency in Conjoint Choice Experiments

Conjoint choice experiments have become a powerful tool to explore individual preferences. The consistency of respondents' choices depends on the choice complexity. For example, it is easier to make a choice between two alternatives with few attributes than between five alternatives with several attributes. In the latter case it will be much harder to choose the preferred alternative which is reflected in a higher response error. Several authors have dealt with this choice complexity in the estimation stage but very little attention has been paid to set up designs that take this complexity into account. The core issue of this paper is to find out whether it is worthwhile to take this complexity into account in the design stage. We construct efficient semi-Bayesian D-optimal designs for the heteroscedastic conditional logit model which is used to model the across respondent variability that occurs due to the choice complexity. The degree of complexity is measured by the entropy, as suggested by Swait and Adamowicz (2001). The proposed designs are compared with a semi-Bayesian D-optimal design constructed without taking the complexity into account. The simulation study shows that it is much better to take the choice complexity into account when constructing conjoint choice experiments.
Temporal-Difference Value Estimation via Uncertainty-Guided Soft Updates

Temporal-Difference (TD) learning methods, such as Q-Learning, have proven effective at learning a policy to perform control tasks. One issue with methods like Q-Learning is that the value update introduces bias when predicting the TD target of a unfamiliar state. Estimation noise becomes a bias after the max operator in the policy improvement step, and carries over to value estimations of other states, causing Q-Learning to overestimate the Q value. Algorithms like Soft Q-Learning (SQL) introduce the notion of a soft-greedy policy, which reduces the estimation bias via soft updates in early stages of training. However, the inverse temperature $\beta$ that controls the softness of an update is usually set by a hand-designed heuristic, which can be inaccurate at capturing the uncertainty in the target estimate. Under the belief that $\beta$ is closely related to the (state dependent) model uncertainty, Entropy Regularized Q-Learning (EQL) further introduces a principled scheduling of $\beta$ by maintaining a collection of the model parameters that characterizes model uncertainty. In this paper, we present Unbiased Soft Q-Learning (UQL), which extends the work of EQL from two action, finite state spaces to multi-action, infinite state space Markov Decision Processes. We also provide a principled numerical scheduling of $\beta$, extended from SQL and using model uncertainty, during the optimization process. We show the theoretical guarantees and the effectiveness of this update method in experiments on several discrete control environments.
Texture Image Classification and Retrieval Using Multi-resolution Radial Gradient Binary Pattern

ABSTRACT In image classification and retrieval applications, images are represented by a set of features. These features are extracted from both spatial and wavelet transformed input images. The wavelet transform decomposes images into multiple resolutions by separating smooth and sharp information in individual channels to give more details about the image. Each level of smooth and sharp decomposed channels is individually involved in texture feature extraction. It is difficult to achieve better results in image classification and retrieval applications with fewer discriminant details of the image. Even though each level of the smooth channel correlates with its successive decomposition levels, these feature extraction methods do not consider the relationship between the multiple levels of decomposed images. This motivates the proposed work to extract the correlation between the different levels of wavelet decomposed images. The proposed work (i) encodes the local difference obtained from multiple radii across the different levels of wavelet decomposed channels, (ii) assesses the proposed texture feature extraction method using classification and retrieval experiments with five different wavelet filters over twelve image databases and (iii) analyses the importance of similarity measures involved in the proposed feature-based image retrieval experiments.
Overload: The Impact of Incident Stressful Events on Antiretroviral Medication Adherence and Virologic Failure in a Longitudinal, Multi-site HIV Cohort Study

Background— HIV-infected individuals frequently experience traumatic and stressful events such as sexual and physical assault; housing instability; and major financial, employment, and legal difficulties. Past trauma history predicts poorer medication adherence and health outcomes, yet little research has examined the influence of incident stressful experiences on antiretroviral medication adherence and treatment outcomes. Methods— We prospectively measured incident stressful and traumatic events, medication adherence, and viral load over 27 months in an 8-site, 5-state study. Using multivariable logistic and generalized estimating equation modeling, we assessed the impact of incident stressful events on 27-month changes in self-reported medication adherence and virologic failure (viral load ≥400 c/mL). Results— Of 474 participants on antiretroviral therapy at baseline, 289 were interviewed and still on treatment at 27 months. Participants experiencing the median number of incident stressful events (n=9) had over twice the predicted odds (OR=2.32) of antiretroviral medication non-adherence at follow-up compared to those with no events. Stressful events also predicted increased odds of virologic failure during follow-up (OR=1.09 per event). Conclusions— Incident stressful events are exceedingly common in the lives of HIV-infected individuals and negatively impact antiretroviral medication adherence and treatment outcomes. Interventions to address stress and trauma are needed to improve HIV
On strict extensions of semigroups.

Ideal extensions of semigroups were introduced by A. H. Clifford (see, e. g. [1]) and are of fundamental importance for such questions s the structure of finite semigroups. Among the most useful kinds of extensions are the extensions determined by a partial homomorphism ([! ]) and the more general strict and strongly strict extensions introduced in [2] and [3]. We also consider fairly strict extensions which were implicitely introduced in [3] and are intermediate between strict and strongly strict extensions. We first show that these fairly strict extensions share many properties with strongly strict extensions. Next we characterize the semigroups S such that every strict (or fairly strict, or strongly strict) extension of S is fairly strict (or strongly strict, or determined by a partial homomorphism).
Speed of convergence for the realization of an effective subshift by a multidimensional SFT or Sofic

Realization of $d$-dimensional effective subshifts as projective sub-actions of $d+d'$-dimensional sofic subshifts for $d'\geq 1$ is now well know~\cite{Hochman-2009,Durand-Romashchenko-Shen-2010,Aubrun-Sablik-2010}. In this paper we are interested in the speed of convergence of this realization. That is to say given an effective subshift $\Sigma$ realized as projective sub-action of a sofic $\T$, we study the function which on input an integer $k$ returns the smallest width of the strip which verify the local rules of $\T$ necessary to obtain exclusively the language of size $k$ of $\Sigma$ in the central row of the strip. We study this topological conjugacy invariant for effective subshifts in order to exhibit algorithmic properties of these subshifts.
Toward a numerical theory of ampleness

Introduction Chapter I. Intersection Numbers ? 1. The polynomial theorem of Snapper ? 2. The definition and some properties of intersection numbers ? 3. Degrees and Hilbert polynomials ? 4. Numerically effective and numerically trivial invertible sheaves Chapter II. Some Vanishing Theorems and Related Results ? 1. M-families ? 2. Some vanishing theorems and related results Chapter III. Ampleness and Pseudoampleness ? 1. The numerical characterization of ampleness ? 2. Pseudoampleness Chapter IV. The Ample Cone ? 1. The vector space A'(V) ? 2. The ample cone ? 3. Polarization ? 4. Relativization ? 5. The characteristic cone of a proper morphism Bibliography
Development and Stem Cells Research Article

INTRODUCTION The mammalian cerebral cortex is organized tangentially into specific functional domains and radially into six neuronal layers. In mouse, projection neurons are generated between embryonic day 11 (E11) and E17 (Takahashi et al., 1996b) from precursors surrounding the lateral ventricles. Initially, radial glial cells (RGCs) that comprise the pseudostratified ventricular zone (VZ) divide asymmetrically at the apical surface, producing neurons for all layers (Heins et al. As neurogenesis proceeds, RGCs produce intermediate precursor cells (IPCs) that localize basally in subventricular zone (SVZ) and undergo symmetric neurogenic divisions (Haubensak et al.
Interactive effects of non-fodder litter and fungal species on soil enzymes: A microcosm temporal assessment from Indian arid zone

The interactive effects of three non-fodder Indian arid plant species, Tephrosia purpurea, Aerva persica, and Calotropis procera, and four Aspergillus fungal species on soil enzymes (acid and alkaline phosphatase, -glucosidase, dehydrogenase, urease, and amidase activities) were temporally assessed (15 and 30 days withdrawals). The results were statistically analysed using ANOVA, Principal Component Analysis (PCA), and Canonical Correlation Analysis (CCoA). Aside from these, a biochemical soil quality index was created by assigning a weighted score to each enzyme and analysing it using PCA. This study found that various litter-fungal species complexes acted differently and that their effects changed over time, specifically for acid phosphatase, alkaline phosphatase, beta-glucosidase, and amidase. Dehydrogenase and urease activities increased with predictors over time. With temporal backwash, all four fungal species with C. procera inhibit acid phosphatase, alkaline phosphatase, and beta-glucosidase activities (i.e., more at 15 days and lesser after 30 days). Our current findings suggest that (a) urease activities were modulated by A. persica in cooperation with fungi like A. terreus, A. niger, and A. flavus at specific enzyme levels; (b) In assistance with fungi such as A. fumigatus, A. niger, and A. persica, amidase concentration was successfully managed through litter of the legume plant species T. purpuria. (c) When C. procera and A. fumigatus, A. niger, and A. flavus worked together, they were most effective at supporting beta-glucosidase and dehydrogenase (d) Alkaline phosphatase and (e) acid phosphatase was more responsive to T. purpurea-A. terreus complexes than were T. purpurea-A. flavus and C. procera-A. terreus complexes.
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation

Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in the future using their historical Electronic Health Records (EHR). Researchers have developed several risk prediction models to handle the unique challenges of EHR data, such as its sequential nature, high dimensionality, and inherent noise. These models have yielded impressive results. Nonetheless, a key issue undermining their effectiveness is data insufficiency. A variety of data generation and augmentation methods have been introduced to mitigate this issue by expanding the size of the training data set through the learning of underlying data distributions. However, the performance of these methods is often limited due to their task-unrelated design. To address these shortcomings, this paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. Furthermore, MedDiffusion discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data. Experimental evaluation on four real-world medical datasets demonstrates that MedDiffusion outperforms 14 cutting-edge baselines in terms of PR-AUC, F1, and Cohen's Kappa. We also conduct ablation studies and benchmark our model against GAN-based alternatives to further validate the rationality and adaptability of our model design. Additionally, we analyze generated data to offer fresh insights into the model's interpretability.
ROCCO: a robust method for detection of open chromatin via convex optimization

Motivation Analysis of open chromatin regions across multiple samples from two or more distinct conditions can determine altered gene regulatory patterns associated with biological phenotypes and complex traits. The ATAC-seq assay allows for tractable genome-wide open chromatin profiling of large numbers of samples. Stable, broadly applicable genomic annotations of open chromatin regions are not available. Thus, most studies first identify open regions using peak calling methods for each sample independently. These are then heuristically combined to obtain a consensus peak set. Reconciling sample-specific peak results post hoc from larger cohorts is particularly challenging, and informative spatial features specific to open chromatin signals are not leveraged effectively. Results We propose a novel method, ROCCO, that determines consensus open chromatin regions across multiple samples simultaneously. ROCCO employs robust summary statistics and solves a constrained optimization problem formulated to account for both enrichment and spatial dependence of open chromatin signal data. We show this formulation admits attractive theoretical and conceptual properties as well as superior empirical performance compared to current methodology. Availability and Implementation Source code, documentation, and usage demos for ROCCO are available on GitHub at: https://github.com/nolan-h-hamilton/ROCCO. ROCCO can also be installed as a standalone binary utility using pip/PyPI. Contact nolanh@email.unc. edu or tsfurey@email.unc. edu. Supplementary Information Supplementary material is available with this submission. Additional resources that may aid readers are available in the ROCCO GitHub repository.
Fogyasztói értékdimenziók vizsgálata a falusi turizmusban = Analyzing the consumer value dimensions of rural tourism

Korunk uj turisztikai trendjeit figyelembe veve egyre nagyobb fogyasztoi es kutatoi figyelem fordult a falusi turizmusra. A falusi turizmus versenykepessegere es az ertekalkotasra, amely friss kutatasi teruletkent azonosithato. A temahoz fűződő szemelyes elkoteleződesen tul, a kutatas gyakorlati szuksege is osztonozte munkamat: hazankban a falusi turizmus versenykepessege es menedzsmentje a kozelmultban kerult a turisztikai szakma fokuszaba, leginkabb a videkfejlesztesi kerdesek kapcsan. Magyarorszag kulturalis es orokseg ertekeinek megőrzese es megismertetese vegett a falusi turizmus letjogosultsagat alapvetőnek velem. Mindezen osztonzők miatt a falusi turizmus kereslet oldali megkozelitesere fokuszaltam, a hangsulyt a „Mi jelent erteket a turistaknak a falusi turizmusban? ” kerdesre helyezve. A gyakorlati szempontok altal is vezerelt kutatasomban ugy veltem, a fogyasztoi ertekek feltarasa hasznos tanulsagokat hordozhat a kinalati oldal marketing es menedzsment dontesei soran. Ezert kutatasomat a fogyasztoi ertekek falusi turizmusban megjelenő dimenzioinak vizsgalatara fokuszaltam. A kutatasi kerdeskor vizsgalatahoz haromfazisu kvalitativ es kvantitativ elemeket tartalmazo vizsgalatot vegeztem. Előszor a tema relevans indikatorait tartam fel szakirodalom-kutatassal es szemi-kvalitativ kritikus eset technikara epitő kismintas (N=30) fogyasztoi megkerdezesekkel, majd az igy kialakitott indikatorlista hazai relevanciajat szakertői megkerdezesek segitsegevel ellenőriztem, vegul ezeket nagy mintas kerdőives felmeres (N=352) kereteben teszteltem. A kutatas eredmenyekent a falusi turizmus fogyasztoi ertekfaktorainak es ertekdimenzioinak hianypotlo feltarasara es rangsorolasara kerult sor, hozzajarulva ezzel a tudomanyos es gyakorlati eletben tortenő felhasznalasahoz, termekfejlesztesi strategiak kialakitasahoz. Vegeredmenyben a hazai falusi turizmusban fellelhető fogyasztoi ertek olyan elmeleti ertelmezesi kerete kerult kialakitasra, amely remenyeim szerint hasznos lehet a falusi turizmus menedzsmentjenek es marketingjenek jobb kialakitasaban, az elmenykinalat megvalositasaban es ezaltal a kozos ertekek, illetve a versenykepesseg siker- es kudarctenyezőinek azonositasaban.
, Wit, Stupidity – from Peirce to Freud Uwe Wirth The Commens Encyclopedia The Digital Encyclopedia of Peirce Studies

: According to Kant, human stupidity reveals, a lack of “power of judgement”, or as Peirce might say, a lack of abductive competence that confuses the relevant and the irrelevant. Peirce anticipated this idea. He writes: “I have come to the conclusion that it is folly to attempt to set limits which human stupidity cannot overpass (CP 4.321). Apparently both, Semiosis and human Stupidity are unlimited. The only two possible reactions to this condition humain are either critical reasoning or laughing. It was Aristotle who first defined man as at the same time animal syllogans and as animal ridens: an animal that can reason and that can laugh. I want to highlight a particular aspect of this very general double determination, namely the relation between the Peircean logic of abduction and the Freudian “logic” of the comic.
Ternary-type Opacity and Hybrid Odometry for RGB-only NeRF-SLAM

The opacity of rigid 3D scenes with opaque surfaces is considered to be of a binary type. However, we observed that this property is not followed by the existing RGB-only NeRF-SLAM. Therefore, we are motivated to introduce this prior into the RGB-only NeRF-SLAM pipeline. Unfortunately, the optimization through the volumetric rendering function does not facilitate easy integration of the desired prior. Instead, we observed that the opacity of ternary-type (TT) is well supported. In this work, we study why ternary-type opacity is well-suited and desired for the task at hand. In particular, we provide theoretical insights into the process of jointly optimizing radiance and opacity through the volumetric rendering process. Through exhaustive experiments on benchmark datasets, we validate our claim and provide insights into the optimization process, which we believe will unleash the potential of RGB-only NeRF-SLAM. To foster this line of research, we also propose a simple yet novel visual odometry scheme that uses a hybrid combination of volumetric and warping-based image renderings. More specifically, the proposed hybrid odometry (HO) additionally uses image warping-based coarse odometry, leading up to an order of magnitude final speed-up. Furthermore, we show that the proposed TT and HO well complement each other, offering state-of-the-art results on benchmark datasets in terms of both speed and accuracy.
17.5 A 3T1R nonvolatile TCAM using MLC ReRAM with Sub-1ns search time

Many big-data (BD) processors reduce power consumption by employing ternary content-addressable-memory (TCAM) [1-2] with pre-stored signature patterns as filters to reduce the amount of data sent for processing in the following stage (i.e., wireless transmission). To further reduce standby power, BD-processors commonly use nonvolatile memory (NVM) to back up the signature patterns of SRAM-based TCAM (sTCAM) [3] during power interruptions or frequent-off operations. However, this 2-macro (sTCAM + NVM) scheme suffers long delays and requires considerable energy for wake-up operations, due to the word-by-word serial transfer of data between NVM and TCAM macros. Most of the signature patterns are seldom updated (written); therefore, single-macro nonvolatile TCAM (nvTCAM) can be used for BD-processors to reduce area and facilitate fast/low-power wake-up operations, compared to the 2-macro approach. Previous nvTCAMs were designed using diode-connected 4T2R with STT-MTJ (D4T2R) [4], 2T2R with PCM [5], and 4T2R with ReRAM [2]; however, they suffer the following issues: (1) large cell area (A) and high write energy (Ew) due to the use of two NVM (2R) devices; (2) limited word-length (WDL, /k-bits) caused by small current-ratio (I-ratio= IML-MIS/(K×IML)) between match-line (ML) mismatch current (IML-MIS) and ML leakage current of k matched cells (k × IML-MIS); (3) Long search delays (TSD) and excessive search energy (Es) due to large ML parasitic load (CML) and small I-ratio. ReRAM is promising for nvTCAM due to its low Ew, high resistance-ratio (R-ratio), and multiple-level cell (MLC) capability. To overcome issue (1) to (3), this study develops an MLC-based 3T1R nvTCAM with bi-directional voltage-divider control (BVDC). A 2×64×64b 3T1R nvTCAM macro is fabricated using back-end-of-line (BEOL) ReRAM [6] and a 90nm CMOS process, with 2.27× cell size reduction as compared with sTCAM using the same technology and the TSD (=0.96ns) for WDL=64b.
The Bologna Process's Model of Mobility in Europe: The Relationship of its Spatial and Social Dimensions

Cross-border mobility is among the pillars of internationality in higher education. Understood as central to educational and economic growth for individuals and societies, mobility also should facilitate social cohesion. Yet those who can afford spatial mobility are unevenly distributed; elites benefit in far greater measure. Policymakers in Europe aim to bolster the competitiveness and attractiveness of European higher education, especially through enhanced mobility of students and staff. Extending beyond the successes of Erasmus, the Bologna process defines a new model of mobility in higher education to foster spatial mobility, but how is the social selectivity of spatial mobility addressed? Based on a theory-guided content analysis of official Bologna policy documents, the authors examine the principles and standards of mobility. Which dimensions of mobility are mentioned in these declarations and communiqués from 1998 to 2012? To what extent are spatial mobility's social significance and selection processes reflected? The authors find that the dimensions, benefits and effects of spatial mobility have been mainly taken for granted, and both its social selectivity and its effects on social mobility understated. However, if the Bologna process is to facilitate social inclusion, inequalities must be addressed. The authors argue that if the 47 signatory countries to the Bologna process simply follow the principles espoused in this model, considerable disparities in participation in international exchange are likely to persist, reproducing social reproduction of dis/advantages.