Miracle-dz/UniFAR-FTUs huggingface dataset:
FedLogic: Interpretable Federated Multi-Domain Chain-of-Thought Prompt Selection for Large Language Models

Leveraging ``chain-of-thought (CoT)'' reasoning to elicit rapid and precise responses from large language models (LLMs) is rapidly attracting research interest. A notable challenge here is how to design or select optimal prompts. The process of prompt selection relies on trial and error, involving continuous adjustments and combinations of input prompts by users based on the corresponding new responses generated from LLMs. Furthermore, minimal research has been conducted to explore how LLMs employ the mathematical problem-solving capabilities learned from user interactions to address issues in narrative writing. To improve interpretability and explore the balance principle between generality and personalization under a multi-domain CoT prompt selection scenario, we propose the Federated Logic rule learning approach (FedLogic). We introduce a theoretical formalization and interactive emulation of the multi-domain CoT prompt selection dilemma in the context of federated LLMs. We cast the problem of joint probability modeling as a bilevel program, where the CoT prompt selection intricacy can be likened to a fuzzy score-based rule selection with the LLMs function as rule generators. FedLogic solves this problem through variational expectation maximization (V-EM). In addition, we incorporate two KL-divergence constraints within this probabilistic modeling framework to surmount the intricacies of managing extensive search spaces and accomplishing cross-domain personalization of CoTs. To the best of our knowledge, FedLogic is the first interpretable and principled federated multi-domain CoT prompt selection approach for LLMs.
Multiwavelength Observations of a B-class Flare Using XSM, AIA, and XRT

We present multiwavelength observations by Chandrayaan-2/Solar X-ray Monitor, Solar Dynamics Observatory/Atmospheric Imaging Assembly, and Hinode/X-Ray Telescope (XRT) of a B-class flare observed on 2021 February 25, originating from an active region (AR 12804) near the northwest limb. The microflare lasts for ∼30 minutes and is composed of hot loops reaching temperatures of 10 MK. We report excellent agreement (within 20%) for the average effective temperatures obtained at the flare peak from all the three instruments, which have different temperature sensitivities. The XRT filter combination of Be-thin and Be-med provides an excellent opportunity to measure the high temperatures in such microflare events. The elemental abundances during the evolution of the microflare are also studied and observed to drop toward photospheric values at the flare peak time, compared to coronal values during the rise and decay phase. This is consistent with previous XSM studies.
Cross-Modal Retrieval Meets Inference: Improving Zero-Shot Classification with Cross-Modal Retrieval

Contrastive language-image pre-training (CLIP) has demonstrated remarkable zero-shot classification ability, namely image classification using novel text labels. Existing works have attempted to enhance CLIP by fine-tuning on downstream tasks, but these have inadvertently led to performance degradation on unseen classes, thus harming zero-shot generalization. This paper aims to address this challenge by leveraging readily available image-text pairs from an external dataset for cross-modal guidance during inference. To this end, we propose X-MoRe, a novel inference method comprising two key steps: (1) cross-modal retrieval and (2) modal-confidence-based ensemble. Given a query image, we harness the power of CLIP's cross-modal representations to retrieve relevant textual information from an external image-text pair dataset. Then, we assign higher weights to the more reliable modality between the original query image and retrieved text, contributing to the final prediction. X-MoRe demonstrates robust performance across a diverse set of tasks without the need for additional training, showcasing the effectiveness of utilizing cross-modal features to maximize CLIP's zero-shot ability.
Spherical solutions of the Schrödinger-Poisson system with core-tail structure

We construct spherically symmetric equilibrium solutions of the Schr\"odinger-Poisson (SP) system of equations with a core-tail structure that could serve as models of Fuzzy Dark Matter (FDM) halos. The core is assumed to be a solitonic ground state equilibrium configuration of the SP equations, and the tail is integrated from a transition radius onwards. The total mass of the system parametrizes the family of solutions and constrains the tail density profile. The tail has a radial velocity profile, whereas the core is stationary. We investigate the evolution of these equilibrium configurations and find that the tail initially perturbs the core, and consequently, the whole solution oscillates around a virialized solution that we call 'relaxed', whose average also has a core-tail structure. We measure the departure of the relaxed configuration from the equilibrium solution in order to estimate the utility of the latter. We also find that the core-halo scaling relation of equilibrium configurations has an exponent $\alpha=1/3$, whereas relaxed configurations exhibit a scaling with $\alpha=0.54$.
Ratio of $J/\Psi$ and $\Psi(2s)$ exclusive photoproduction cross-sections as an indicator for the presence of non-linear QCD evolution

We investigate the proposal that the rise with energy of the ratio of the exclusive photo-production cross-sections of vector mesons $\Psi(2s)$ and $J/\Psi$ can serve as an indicator for the presence of high gluon densities and associated non-linear high energy evolution; we study this proposal for both photoproduction on a proton and a lead nucleus. While previous studies were based on unintegrated gluon distributions subject to linear (Balitsky-Fadin-Kuraev-Lipatov) and non-linear (Balitsky-Kovchegov) evolution equations, the current study is based on the Golec-Biernat W\"usthoff (GBW) and Bartels Golec-Biernat Kowalski (BGK) models, which allow assessing more directly the relevance of non-linear corrections for the description of the energy dependence of the photoproduction cross-section. We find that the rise of the ratio is directly related to the presence of a node in the $\Psi(2s)$ wave function and only manifests itself for the complete non-linear models, while it is absent for their linearized versions. We further provide predictions based on leading order collinear factorization and examine to which extent such an approach can mimic a ratio rising with energy. We also provide a description of recent ALICE data on the energy dependence of the photonuclear $J/\Psi$ production cross-section and give predictions for the energy dependence of the ratio of $\Psi(2s)$ and $J/\Psi$ photoproduction cross-sections for both scattering on a proton and a lead nucleus.
KCNQ2 encephalopathy: Delineation of the electroclinical phenotype and treatment response

Neonatal-onset epilepsies are rare conditions, mostly genetically determined, that can have a benign or severe phenotype.1,2 There is recent recognition of de novo KCNQ2 mutations in patients with severe neonatal-onset epilepsy with intractable seizures and severe psychomotor impairment, termed KCNQ2 encephalopathy.3,4 This is a rare condition and all patients reported so far were diagnosed well after the neonatal period.3,4 We report on 3 new cases of KCNQ2 encephalopathy diagnosed in the neonatal period and studied with continuous video-EEG recording. We describe a distinct electroclinical phenotype and report on efficacy of antiepileptic drug (AED) therapies.
DigitalCommons@UNO DigitalCommons@UNO

Across social media platforms users (sub)consciously represent themselves in a way which is appropriate for their intended audience. This has unknown impacts on studies with unobtrusive designs based on digital (social) platforms, and studies of contemporary social phenomena in online settings. A lack of appropriate methods to identify, control for, and mitigate the effects of self-representation, the propensity to express socially responding characteristics or self-censorship in digital settings, hinders the ability of researchers to confidently interpret and generalize their findings. This article proposes applying boosted regression modelling to fill this research gap. A case study of paid Amazon Mechanical Turk workers (n = 509) is presented where workers completed psychometric surveys and provided anonymized access to their Facebook timelines. Our research finds indicators of self-representa-tion on Facebook, facilitating suggestions for its mitigation. We validate the use of LIWC for Facebook personality studies, as well as find discrepancies with extant literature about the use of LIWC-only approaches in unobtrusive designs. Using survey data and LIWC sentiment categories as predictors, the boosted regression model classified the Five Factor personality model with an average accuracy of 74.6%. The contribution of this work is an accurate prediction of psychometric information based on short, informal text.
Vector Search with OpenAI Embeddings: Lucene Is All You Need

We provide a reproducible, end-to-end demonstration of vector search with OpenAI embeddings using Lucene on the popular MS MARCO passage ranking test collection. The main goal of our work is to challenge the prevailing narrative that a dedicated vector store is necessary to take advantage of recent advances in deep neural networks as applied to search. Quite the contrary, we show that hierarchical navigable small-world network (HNSW) indexes in Lucene are adequate to provide vector search capabilities in a standard bi-encoder architecture. This suggests that, from a simple cost-benefit analysis, there does not appear to be a compelling reason to introduce a dedicated vector store into a modern"AI stack"for search, since such applications have already received substantial investments in existing, widely deployed infrastructure.
CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs

Approximate Nearest Neighbor Search (ANNS) plays a critical role in various disciplines spanning data mining and artificial intelligence, from information retrieval and computer vision to natural language processing and recommender systems. Data volumes have soared in recent years and the computational cost of an exhaustive exact nearest neighbor search is often prohibitive, necessitating the adoption of approximate techniques. The balanced performance and recall of graph-based approaches have more recently garnered significant attention in ANNS algorithms, however, only a few studies have explored harnessing the power of GPUs and multi-core processors despite the widespread use of massively parallel and general-purpose computing. To bridge this gap, we introduce a novel parallel computing hardware-based proximity graph and search algorithm. By leveraging the high-performance capabilities of modern hardware, our approach achieves remarkable efficiency gains. In particular, our method surpasses existing CPU and GPU-based methods in constructing the proximity graph, demonstrating higher throughput in both large- and small-batch searches while maintaining compatible accuracy. In graph construction time, our method, CAGRA, is 2.2~27x faster than HNSW, which is one of the CPU SOTA implementations. In large-batch query throughput in the 90% to 95% recall range, our method is 33~77x faster than HNSW, and is 3.8~8.8x faster than the SOTA implementations for GPU. For a single query, our method is 3.4~53x faster than HNSW at 95% recall.
A Closer Look at the Security Risks in the Rust Ecosystem

Rust is an emerging programming language designed for the development of systems software. To facilitate the reuse of Rust code, crates.io, as a central package registry of the Rust ecosystem, hosts thousands of third-party Rust packages. The openness of crates.io enables the growth of the Rust ecosystem but comes with security risks by severe security advisories. Although Rust guarantees a software program to be safe via programming language features and strict compile-time checking, the unsafe keyword in Rust allows developers to bypass compiler safety checks for certain regions of code. Prior studies empirically investigate the memory safety and concurrency bugs in the Rust ecosystem, as well as the usage of unsafe keywords in practice. Nonetheless, the literature lacks a systematic investigation of the security risks in the Rust ecosystem. In this paper, we perform a comprehensive investigation into the security risks present in the Rust ecosystem, asking “what are the characteristics of the vulnerabilities, what are the characteristics of the vulnerable packages, and how are the vulnerabilities fixed in practice? ”. To facilitate the study, we first compile a dataset of 433 vulnerabilities, 300 vulnerable code repositories, and 218 vulnerability fix commits in the Rust ecosystem, spanning over 7 years. With the dataset, we characterize the types, life spans, and evolution of the disclosed vulnerabilities. We then characterize the popularity, categorization, and vulnerability density of the vulnerable Rust packages, as well as their versions and code regions affected by the disclosed vulnerabilities. Finally, we characterize the complexity of vulnerability fixes and localities of corresponding code changes, and inspect how practitioners fix vulnerabilities in Rust packages with various localities. We find that memory safety and concurrency issues account for nearly two thirds of the vulnerabilities in the Rust ecosystem. It takes over 2 years for the vulnerabilities to become publicly disclosed, and one third of the vulnerabilities have no fixes committed before their disclosure. In terms of vulnerability density, we observe a continuous upward trend at the package level over time, but a decreasing trend at the code level since August 2020. In the vulnerable Rust packages, the vulnerable code tends to be localized at the file level, and contains statistically significantly more unsafe functions and blocks than the rest of the code. More popular packages tend to have more vulnerabilities, while the less popular packages suffer from vulnerabilities for more versions. The vulnerability fix commits tend to be localized to a limited number of lines of code. Developers tend to address vulnerable safe functions by adding safe functions or lines to them, vulnerable unsafe blocks by removing them, and vulnerable unsafe functions by modifying unsafe trait implementations. Based on our findings, we discuss implications, provide recommendations for software practitioners, and outline directions for future research.
Another Look at the Linear Probability Model and Nonlinear Index Models

We reassess the use of linear models to approximate response probabilities of binary outcomes, focusing on average partial effects (APE). We confirm that linear projection parameters coincide with APEs in certain scenarios. Through simulations, we identify other cases where OLS does or does not approximate APEs and find that having large fraction of fitted values in [0, 1] is neither necessary nor sufficient. We also show nonlinear least squares estimation of the ramp model is consistent and asymptotically normal and is equivalent to using OLS on an iteratively trimmed sample to reduce bias. Our findings offer practical guidance for empirical research.
Lower Bounds on Number of QAOA Rounds Required for Guaranteed Approximation Ratios

The quantum alternating operator ansatz (QAOA) is a heuristic hybrid quantum-classical algorithm for finding high-quality approximate solutions to combinatorial optimization problems, such as Maximum Satisfiability. While QAOA is well-studied, theoretical results as to its runtime or approximation ratio guarantees are still relatively sparse. We provide some of the first lower bounds for the number of rounds (the dominant component of QAOA runtimes) required for QAOA. For our main result, (i) we leverage a connection between quantum annealing times and the angles of QAOA to derive a lower bound on the number of rounds of QAOA with respect to the guaranteed approximation ratio. We apply and calculate this bound with Grover-style mixing unitaries and (ii) show that this type of QAOA requires at least a polynomial number of rounds to guarantee any constant approximation ratios for most problems. We also (iii) show that the bound depends only on the statistical values of the objective functions, and when the problem can be modeled as a $k$-local Hamiltonian, can be easily estimated from the coefficients of the Hamiltonians. For the conventional transverse field mixer, (iv) our framework gives a trivial lower bound to all bounded occurrence local cost problems and all strictly $k$-local cost Hamiltonians matching known results that constant approximation ratio is obtainable with constant round QAOA for a few optimization problems from these classes. Using our novel proof framework, (v) we recover the Grover lower bound for unstructured search and -- with small modification -- show that our bound applies to any QAOA-style search protocol that starts in the ground state of the mixing unitaries.
Where Would I Go Next? Large Language Models as Human Mobility Predictors

Accurate human mobility prediction underpins many important applications across a variety of domains, including epidemic modelling, transport planning, and emergency responses. Due to the sparsity of mobility data and the stochastic nature of people's daily activities, achieving precise predictions of people's locations remains a challenge. While recently developed large language models (LLMs) have demonstrated superior performance across numerous language-related tasks, their applicability to human mobility studies remains unexplored. Addressing this gap, this article delves into the potential of LLMs for human mobility prediction tasks. We introduce a novel method, LLM-Mob, which leverages the language understanding and reasoning capabilities of LLMs for analysing human mobility data. We present concepts of historical stays and context stays to capture both long-term and short-term dependencies in human movement and enable time-aware prediction by using time information of the prediction target. Additionally, we design context-inclusive prompts that enable LLMs to generate more accurate predictions. Comprehensive evaluations of our method reveal that LLM-Mob excels in providing accurate and interpretable predictions, highlighting the untapped potential of LLMs in advancing human mobility prediction techniques. We posit that our research marks a significant paradigm shift in human mobility modelling, transitioning from building complex domain-specific models to harnessing general-purpose LLMs that yield accurate predictions through language instructions. The code for this work is available at https://github.com/xlwang233/LLM-Mob.
Population genetic study of eggplants (Solanum) species in Nigeria, Tropical West Africa, using molecular markers

Population genetics of eggplants (Solanum species) was studied using RAPD (Random Amplified Polymorphic DNA) markers in six states (populations) within Nigeria, Tropical West Africa. The aim was to estimate the actual amount of polymorphism in each population and the overall population combined together. Binary matrices were manually computed from amplified DNA and resolved DNA bands. Statistical operation was performed using POPGEN32 software (1.31 versions) to generate population genetic parameters and dendrogram. Results showed that the mean effective number of alleles for Nasarawa State, FCT, Niger State, and Plateau State are 1.542, 1.137, 1.361 and 1.425 respectively. Kogi and Benue States had a close value of 1.2912 and 1.255 respectively. The number of polymorphic loci among the eggplants ranged from 5 in FCT to 19 in Nasarawa State. Nasarawa State had the highest percentage polymorphism of 63.3% followed by Plateau State (53.3%) and Niger State (43.3%). Kogi and Benue States recorded the same polymorphism of 40% while FCT was the lowest (16.67%). However, the multiple population Genic Variation Statistics (GVS) showed a total number of 25 polymorphic loci among the eggplants with a combined polymorphism of 83.33%. Benue and Niger States had the lowest genetic distance of 1.56 which was very far from FCT that recorded the highest distance of 7.016. Plateau and Nasarawa States had a genetic distance of 5.45 and 5.91 respectively. Consequently, Nasarawa state as a region of eggplant diversity could be explored by plant breeders and taxonomists while the rare genotypes of FCT, Benue state and Kogi states require urgent conservation measures as well as intensive cultivation of varied species.
Robust Zeeman-type band splitting in sliding ferroelectrics

Transition metal dichalcogenides exhibit giant spin-orbit coupling, and intriguing spin-valley effects, which can be harnessed through proximity in van der Waals (vdW) heterostructures. Remarkably, due to the prismatic crystal field, the Zeeman-type band splitting of valence bands reach values of several hundreds of meV. While this effect is suppressed in the commonly studied hexagonal (H)-stacked bilayers due to the presence of inversion symmetry, the recent discovery of sliding ferroelectricity in rhombohedral (R-)stacked MX$_2$ bilayers (M=Mo, W; X=S, Se) suggests that the Zeeman effect could be present in these non-centrosymmetric configurations, making it even more intriguing to investigate how the spin-resolved bands would evolve during the phase transition. Here, we perform density functional theory calculations complemented by symmetry analysis to unveil the evolution of ferroelectricity during sliding and the behavior of Zeeman splitting along the transition path. While the evolution of the out-of-plane component of the electric polarization vector resembles the conventional ferroelectric transition, we observe significant in-plane components parallel to the sliding direction, reaching their maximum at the intermediate state. Moreover, we demonstrate that the R-stacked bilayers exhibit persistent Zeeman-type band splitting throughout the transition path, allowed by the lack of inversion symmetry. Further analysis of different stacking configurations generated by sliding along various directions confirms that the Zeeman effect in MX$_2$, primarily arising from the polarity of prismatic ligand coordination of the metal atom, is remarkably robust and completely governs the spin polarization of bands, independently of the sliding direction. This resilience promises robust spin transport in vdW based MX$_2$ bilayers, opening new opportunities for ferroelectric spintronics.
Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding

The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making. However, existing brain-inspired spiking neural networks (SNNs) have struggled to match their biological counterpart in modeling long-term temporal relationships. To address this problem, this paper presents a novel Contextual Embedding Leaky Integrate-and-Fire (CE-LIF) spiking neuron model. Specifically, the CE-LIF model incorporates a meticulously designed contextual embedding component into the adaptive neuronal firing threshold, thereby enhancing the memory storage of spiking neurons and facilitating effective sequential modeling. Additionally, theoretical analysis is provided to elucidate how the CE-LIF model enables long-term temporal credit assignment. Remarkably, when compared to state-of-the-art recurrent SNNs, feedforward SNNs comprising the proposed CE-LIF neurons demonstrate superior performance across extensive sequential modeling tasks in terms of classification accuracy, network convergence speed, and memory capacity.