Miracle-dz/UniFAR-FTUs huggingface dataset:
Face-brain correlates as potential sex-specific biomarkers for schizophrenia and bipolar disorder.

Given the shared ectodermal origin and integrated development of the face and the brain, facial biomarkers emerge as potential candidates to assess vulnerability for disorders in which neurodevelopment is compromised, such as Schizophrenia (SZ) and bipolar disorder (BD). The sample comprised 188 individuals (67 SZ patients, 46 BD patients and 75 healthy controls (HC)). Using a landmark-based approach on 3D facial reconstructions, we quantified global and local facial shape differences between SZ/BD patients and HC using geometric morphometrics. We assessed correlations between facial and brain cortical measures. The analyses were performed separately by sex. Diagnosis explained 4.1% - 5.9% of global facial shape variance in males and females with SZ, and 4.5% - 4.1% in BD. Regarding local facial shape, we detected 43.2% of significantly different distances in males and 47.4% in females with SZ compared to HC, whereas in BD the percentages decreased to 35.8% and 26.8%, respectively. We detected that brain area and volume significantly explained 2.2% and 2% of facial shape variance in the male SZ - HC sample. Our results support facial shape as a neurodevelopmental marker for SZ and BD and reveal sex-specific pathophysiological mechanisms modulating the interplay between the brain and the face.
Overcoming the discrepancies between RCTs and real-world data by accounting for Selection criteria, Operations, and Measurements of Outcome (SOMO)

The potential of real-world data to inform clinical trial design and supplement control arms has gained much interest in recent years. The most common approach relies on matching real-world patient cohorts to clinical trial baseline covariates using propensity score techniques. However, recent studies pointed out that there is a lack of replicability, generalisability, and consensus. Further, few studies consider differences in operational processes. Systematically accounting for confounders, including hidden effects related to the clinical treatment process and clinical trial study protocol, would potentially allow for improved translation between clinical trial and real-world data and enable learning across translational activities. In this paper, we propose an approach that aims to explore and examine these confounders by investigating the impact of selection criteria and operations on the measurements of outcome. We tested the approach on small cell lung cancer patients receiving platinum-based chemotherapy regimens (n=1,224). The results showed that the discrepancy between real-world and clinical trial data potentially depends on differences in covariate characteristics and operations (e.g., censoring mechanism, the process of pre-trial patient selection related to ECOG-performance status 2 patients). This work builds on current approaches and suggests areas of improvement for systematically accounting for differences in outcomes between study cohorts. Continued development of the method presented here could pave the way for transferring learning across studies and developing mutual translation between the real-world and clinical trials to inform future studies design.
Genetic polymorphism and evidence of signatures of selection in the Plasmodium falciparum circumsporozoite protein gene in Tanzanian regions with different malaria endemicity

Background: In 2021 and 2023, the World Health Organization approved RTS,S/AS01 and R21/Matrix malaria vaccines, respectively, for routine immunization of children in African countries with moderate to high transmission. These vaccines are made of Plasmodium falciparum circumsporozoite protein (Pfcsp) but polymorphisms in this gene raises concerns regarding strain-specific responses and the long-term efficacy of these vaccines. This study assessed the Pfcsp genetic diversity, population structure and signatures of selection among parasites from areas of different malaria transmission in mainland Tanzania, to generate baseline data before the introduction of the malaria vaccines in the country. Methods: The analysis involved 589 whole genome sequences generated by and as part of the MalariaGEN Community Project. The samples were collected between 2013 and January 2015 from five regions of mainland Tanzania: Morogoro and Tanga (Muheza) (moderate transmission areas), and Kagera (Muleba), Lindi (Nachingwea), and Kigoma (Ujiji) (high transmission areas). Wright's inbreeding coefficient (Fws), Wright's fixation index (FST), principal component analysis, nucleotide diversity, and Tajima's D were used to assess within-host parasite diversity, population structure and natural selection. Results: Based on Fws (< 0.95), there was high polyclonality (ranging from 69.23% in Nachingwea to 56.9% in Muheza). No population structure was detected in the Pfcsp gene in the five regions (mean FST= 0.0068). The average nucleotide diversity, nucleotide differentiation and haplotype diversity in the five regions were 4.19, 0.973 and 0.0035, respectively. The C-terminal region of Pfcsp showed high nucleotide diversity at Th2R and Th3R regions. Positive values for the Tajima's D were observed in the Th2R and Th3R regions consistent with balancing selection. The Pfcsp C-terminal sequences had 50 different haplotypes (H_1 to H_50) and only 2% of sequences matched the 3D7 strain haplotype (H_50). Conclusions: The findings demonstrate high diversity of the Pfcsp gene with limited population differentiation. The Pfcsp gene showed positive Tajima's D values for parasite populations, consistent with balancing selection for variants within Th2R and Th3R regions. This data is consistent with other studies conducted across Africa and worldwide, which demonstrate low 3D7 haplotypes and little population structure. Therefore, additional research is warranted, incorporating other regions and more recent data to comprehensively assess trends in genetic diversity within this important gene. Such insights will inform the choice of alleles to be included in future vaccines.
On $p$-adic Hurwitz-type spectral zeta functions

Let $\left\{E_n\right\}_{n=1}^{\infty}$ be the set of energy levels corresponding to a Hamiltonian $H$. Denote by $$\lambda_{0}=0~~\textrm{and}~~\lambda_{n}=E_{n}$$ for $n\in\mathbb N. $ In this paper, we shall construct and investigate the $p$-adic counterparts of the Hurwitz-type spectral zeta function \begin{equation} \zeta^{H}(s,\lambda)=\sum_{n=0}^{\infty}\frac{1}{(\lambda_{n}+\lambda)^{s}} \end{equation} and its alternating form \begin{equation} \zeta_{E}^{H}(s,\lambda)=2\sum_{n=0}^{\infty}\frac{(-1)^{n}}{(\lambda_{n}+\lambda)^{s}} \end{equation} in a parallel way.
A Comprehensive View of the Biases of Toxicity and Sentiment Analysis Methods Towards Utterances with African American English Expressions

Language is a dynamic aspect of our culture that changes when expressed in different technologies/communities. Online social networks have enabled the diffusion and evolution of different dialects, including African American English (AAE). However, this increased usage is not without barriers. One particular barrier is how sentiment (Vader, TextBlob, and Flair) and toxicity (Google's Perspective and the open-source Detoxify) methods present biases towards utterances with AAE expressions. Consider Google's Perspective to understand bias. Here, an utterance such as ``All n*ggers deserve to die respectfully. The police murder us. '' it reaches a higher toxicity than ``African-Americans deserve to die respectfully. The police murder us. ''. This score difference likely arises because the tool cannot understand the re-appropriation of the term ``n*gger''. One explanation for this bias is that AI models are trained on limited datasets, and using such a term in training data is more likely to appear in a toxic utterance. While this may be plausible, the tool will make mistakes regardless. Here, we study bias on two Web-based (YouTube and Twitter) datasets and two spoken English datasets. Our analysis shows how most models present biases towards AAE in most settings. We isolate the impact of AAE expression usage via linguistic control features from the Linguistic Inquiry and Word Count (LIWC) software, grammatical control features extracted via Part-of-Speech (PoS) tagging from Natural Language Processing (NLP) models, and the semantic of utterances by comparing sentence embeddings from recent language models. We present consistent results on how a heavy usage of AAE expressions may cause the speaker to be considered substantially more toxic, even when speaking about nearly the same subject. Our study complements similar analyses focusing on small datasets and/or one method only.
On Pigeonhole Principles and Ramsey in TFNP

The generalized pigeonhole principle says that if tN + 1 pigeons are put into N holes then there must be a hole containing at least t + 1 pigeons. Let t-PPP denote the class of all total NP-search problems reducible to finding such a t-collision of pigeons. We introduce a new hierarchy of classes defined by the problems t-PPP. In addition to being natural problems in TFNP, we show that classes in and above the hierarchy are related to the notion of multi-collision resistance in cryptography, and contain the problem underlying the breakthrough average-case quantum advantage result shown by Yamakawa&Zhandry (FOCS 2022). Finally, we give lower bound techniques for the black-box versions of t-PPP for any t. In particular, we prove that RAMSEY is not in t-PPP, for any t that is sub-polynomial in log (N), in the black-box setting. Goldberg and Papadimitriou conjectured that RAMSEY reduces to 2-PPP, we thus refute it and more in the black-box setting. We also provide an ensemble of black-box separations which resolve the relative complexity of the t-PPP classes with other well-known TFNP classes.
A parametrix method for elliptic surface PDEs

Elliptic problems along smooth surfaces embedded in three dimensions occur in thin-membrane mechanics, electromagnetics (harmonic vector fields), and computational geometry. In this work, we present a parametrix-based integral equation method applicable to several forms of variable coefficient surface elliptic problems. Via the use of an approximate Green's function, the surface PDEs are transformed into well-conditioned integral equations. We demonstrate high-order numerical examples of this method applied to problems on general surfaces using a variant of the fast multipole method based on smooth interpolation properties of the kernel. Lastly, we discuss extensions of the method to surfaces with boundaries.
Center stable manifolds for the radial semi-linear wave equation outside a ball

We consider the nonlinear wave equation, with a large exponent, power-like non-linearity, outside a ball of the Euclidean 3-dimensional space. In a previous article, we have proved that any global solution converges, up to a radiation term, to a stationary solution of the equation. In this work, we construct the center-stable manifold associated to each of the stationary solution, giving a complete description of the dynamics of global solutions. We also study the behavior of solutions close to each of the center-stable manifold.
LATE PLEISTOCENE EQUILIBRIUM-LINE ALTITJDES AND MODERN SNOW ACCUMULATION PATTERNS, SAN JUAN MOUNTAINS, COLORADO, U.S.A.

Equilibrium-line altitudes (ELAs) are determined for 85 reconstructed late Pleistocene glaciers in the San Juan Mountains of Colorado. Paleo-ELA contours rise broadly from west to east across the range and wrap around the range's central core, where the highest ELA values are found. Detailed comparison of modern and paleosnowline variations within the range is not possible because there are too few modern snow fields. Snow survey data, however, allow comparison of modern snow accumulation patterns with paleo-ELAs. When the effect of elevation on accumulation is removed, modern accumulation patterns bear striking similarlity to paleo-ELA patterns, with lowest late Pleistocene ELAs in areas of highest modern snow accumulation and vice versa. Modern accumulation contours show the same west to east gradient and also wrap around the high, dry, central core of the range. Accumulation data suggest that "theoretical" modern ELAs would increase by approximately 600 m across the range, a figure closely comparable to cross-range Pleistocene ELA differences. These similarities suggest that accumulation-season general circulation patterns and moisture sources did not alter greatly between the time of the late Pleistocene glacial maximum and the present. They appear to rule out any strong increase in fall, winter, or spring southeasterly (Gulf of Mexico) flow as a mechanism for enhancement of precipitation in the range.
Eliashberg theory for bilayer exciton condensation

We study the effect of dynamical screening of interactions on the transition temperatures ($T_c$) of exciton condensation in a symmetric bilayer of quadratically dispersing electrons and holes by solving the linearized Eliashberg equations for the anomalous interlayer Green's functions. We find that $T_c$ is finite for the range of density and layer separations studied, decaying exponentially with interlayer separation. $T_c$ is suppressed well below that predicted by a Hartree Fock mean field theory with unscreened Coulomb interaction, but is above the estimates from the statically screened Coulomb interaction. Furthermore, using a diagramatic framework, we show that the system is always an exciton condensate at zero temperature but $T_c$ is exponentially small for large interlayer separation.
On positively divisible non-Markovian processes

There are some positively divisible non-Markovian processes whose transition matrices satisfy the Chapman-Kolmogorov equation. These processes should also satisfy the Kolmogorov consistency conditions, an essential requirement for a process to be classified as a stochastic process. Combining the Kolmogorov consistency conditions with the Chapman-Kolmogorov equation, we derive a necessary condition for positively divisible stochastic processes on a finite sample space. This necessary condition enables a systematic approach to the manipulation of certain Markov processes in order to obtain a positively divisible non-Markovian process. We illustrate this idea by an example and, in addition, analyze a classic example given by Feller in the light of our approach.
Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?

As the scale of vision models continues to grow, the emergence of Visual Prompt Tuning (VPT) as a parameter-efficient transfer learning technique has gained attention due to its superior performance compared to traditional full-finetuning. However, the conditions favoring VPT (the ``when") and the underlying rationale (the ``why") remain unclear. In this paper, we conduct a comprehensive analysis across 19 distinct datasets and tasks. To understand the ``when"aspect, we identify the scenarios where VPT proves favorable by two dimensions: task objectives and data distributions. We find that VPT is preferrable when there is 1) a substantial disparity between the original and the downstream task objectives (e.g., transitioning from classification to counting), or 2) a similarity in data distributions between the two tasks (e.g., both involve natural images). In exploring the ``why"dimension, our results indicate VPT's success cannot be attributed solely to overfitting and optimization considerations. The unique way VPT preserves original features and adds parameters appears to be a pivotal factor. Our study provides insights into VPT's mechanisms, and offers guidance for its optimal utilization.
Can Large Language Models Write Parallel Code?

Large Language Models are becoming an increasingly popular tool for software development. Their ability to model and generate source code has been demonstrated in a variety of contexts, including code completion, summarization, translation, and lookup. However, they often struggle to generate code for more complex tasks. In this paper, we explore the ability of state-of-the-art language models to generate parallel code. We propose a benchmark, PCGBench, consisting of a set of 420 tasks for evaluating the ability of language models to generate parallel code, and we evaluate the performance of several state-of-the-art open- and closed-source language models on these tasks. We introduce novel metrics for comparing parallel code generation performance and use them to explore how well each LLM performs on various parallel programming models and computational problem types.
Spectra and pseudo-spectra of tridiagonal $k$-Toeplitz matrices and the topological origin of the non-Hermitian skin effect

We establish new results on the spectra and pseudo-spectra of tridiagonal $k$-Toeplitz operators and matrices. In particular, we prove the connection between the winding number of the eigenvalues of the symbol function and the exponential decay of the associated eigenvectors (or pseudo-eigenvectors). Our results elucidate the topological origin of the non-Hermitian skin effect in general one-dimensional polymer systems of subwavelength resonators with imaginary gauge potentials, proving the observation and conjecture in arXiv:2307.13551. We also numerically verify our theory for these systems.
Benders decomposition for congested partial set covering location with uncertain demand

In this paper, we introduce a mixed integer quadratic formulation for the congested variant of the partial set covering location problem, which involves determining a subset of facility locations to open and efficiently allocating customers to these facilities to minimize the combined costs of facility opening and congestion while ensuring target coverage. To enhance the resilience of the solution against demand fluctuations, we address the case under uncertain customer demand using $\Gamma$-robustness. We formulate the deterministic problem and its robust counterpart as mixed-integer quadratic problems. We investigate the effect of the protection level in adapted instances from the literature to provide critical insights into how sensitive the planning is to the protection level. Moreover, since the size of the robust counterpart grows with the number of customers, which could be significant in real-world contexts, we propose the use of Benders decomposition to effectively reduce the number of variables by projecting out of the master problem all the variables dependent on the number of customers. We illustrate how to incorporate our Benders approach within a mixed-integer second-order cone programming (MISOCP) solver, addressing explicitly all the ingredients that are instrumental for its success. We discuss single-tree and multi-tree approaches and introduce a perturbation technique to deal with the degeneracy of the Benders subproblem efficiently. Our tailored Benders approaches outperform the perspective reformulation solved using the state-of-the-art MISOCP solver Gurobi on adapted instances from the literature.
Minimizing the Age of Two Heterogeneous Sources With Packet Drops Via Cyclic Schedulers

In a communication setting where multiple sources share a single channel to provide status updates to a remote monitor, source transmissions need to be scheduled appropriately to maintain timely communication between each of the sources and the monitor. We consider age-agnostic scheduling policies which are advantageous due to their simplicity of implementation. Further, we focus on a special class of age-agnostic policies, called cyclic schedulers, where each source is scheduled based on a fixed cyclic pattern. We use weighted average age of information (AoI) to quantify the timeliness of communication. We develop a Markov chain formulation to compute the exact mean AoI for the case of two-source cyclic schedulers. Based on the obtained age expression, we develop an algorithm that generates near-optimal cyclic schedulers to minimize the weighted average AoI for two heterogeneous sources, in the presence of channel errors.
Flow structure beneath periodic waves with constant vorticity under strong horizontal electric Fields

While several articles have been written on Electrohydrodynamics (EHD) flows or flows with constant vorticity separately, little is known about the extent to which the combined effects of EHD and constant vorticity affect the flow. This study aims to fill this gap by investigating how a horizontal electric field and constant vorticity jointly influence the free surface and the emergence of stagnation points. Using the Euler equations framework, we employ conformal mapping and pseudo-spectral numerical methods. Our findings reveal that increasing the electric field intensity eliminates stagnation points and smoothen the wave profile. This implies that a horizontal electric field acts as a mechanism for the elimination of stagnation points within the fluid body.
Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation

Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. While modeling pairwise relations has been widely studied in multi-agent interacting systems, the ability to capture larger-scale group-wise activities is limited. In this paper, we propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures, and we demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation. In addition to the edges between pairs of nodes (i.e., agents), we propose to infer hyperedges that adaptively connect multiple nodes to enable group-wise reasoning in an unsupervised manner. Our approach infers dynamically evolving relation graphs and hypergraphs to capture the evolution of relations, which the trajectory predictor employs to generate future states. Meanwhile, we propose to regularize the sharpness and sparsity of the learned relations and the smoothness of the relation evolution, which proves to enhance training stability and model performance. The proposed approach is validated on synthetic crowd simulations and real-world benchmark datasets. Experiments demonstrate that the approach infers reasonable relations and achieves state-of-the-art prediction performance. In addition, we present a deep reinforcement learning (DRL) framework for social robot navigation, which incorporates relational reasoning and trajectory prediction systematically. In a group-based crowd simulation, our method outperforms the strongest baseline by a significant margin in terms of safety, efficiency, and social compliance in dense, interactive scenarios.
Examining the Role of Peer Acknowledgements on Social Annotations: Unraveling the Psychological Underpinnings

This study explores the impact of peer acknowledgement on learner engagement and implicit psychological attributes in written annotations on an online social reading platform. Participants included 91 undergraduates from a large North American University. Using log file data, we analyzed the relationship between learners' received peer acknowledgement and their subsequent annotation behaviours using cross-lag regression. Higher peer acknowledgements correlate with increased initiation of annotations and responses to peer annotations. By applying text mining techniques and calculating Shapley values to analyze 1,969 social annotation entries, we identified prominent psychological themes within three dimensions (i.e., affect, cognition, and motivation) that foster peer acknowledgment in digital social annotation. These themes include positive affect, openness to learning and discussion, and expression of motivation. The findings assist educators in improving online learning communities and provide guidance to technology developers in designing effective prompts, drawing from both implicit psychological cues and explicit learning behaviours.