Miracle-dz/UniFAR-FTUs huggingface dataset:
ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models

Parameter-efficient fine-tuning (PEFT) is widely studied for its effectiveness and efficiency in the era of large language models. Low-rank adaptation (LoRA) has demonstrated commendable performance as a popular and representative method. However, it is implemented with a fixed intrinsic rank that might not be the ideal setting for the downstream tasks. Recognizing the need for more flexible downstream task adaptation, we extend the methodology of LoRA to an innovative approach we call allocating low-rank adaptation (ALoRA) that enables dynamic adjustments to the intrinsic rank during the adaptation process. First, we propose a novel method, AB-LoRA, that can effectively estimate the importance score of each LoRA rank. Second, guided by AB-LoRA, we gradually prune abundant and negatively impacting LoRA ranks and allocate the pruned LoRA budgets to important Transformer modules needing higher ranks. We have conducted experiments on various tasks, and the experimental results demonstrate that our ALoRA method can outperform the recent baselines with comparable tunable parameters.
Centered Masking for Language-Image Pre-Training

We introduce Gaussian masking for Language-Image Pre-Training (GLIP) a novel, straightforward, and effective technique for masking image patches during pre-training of a vision-language model. GLIP builds on Fast Language-Image Pre-Training (FLIP), which randomly masks image patches while training a CLIP model. GLIP replaces random masking with centered masking, that uses a Gaussian distribution and is inspired by the importance of image patches at the center of the image. GLIP retains the same computational savings as FLIP, while improving performance across a range of downstream datasets and tasks, as demonstrated by our experimental results. We show the benefits of GLIP to be easy to obtain, requiring no delicate tuning of the Gaussian, and also applicable to data sets containing images without an obvious center focus.
Learning To Guide Human Decision Makers With Vision-Language Models

There is increasing interest in developing AIs for assisting human decision-making in high-stakes tasks, such as medical diagnosis, for the purpose of improving decision quality and reducing cognitive strain. Mainstream approaches team up an expert with a machine learning model to which safer decisions are offloaded, thus letting the former focus on cases that demand their attention. his separation of responsibilities setup, however, is inadequate for high-stakes scenarios. On the one hand, the expert may end up over-relying on the machine's decisions due to anchoring bias, thus losing the human oversight that is increasingly being required by regulatory agencies to ensure trustworthy AI. On the other hand, the expert is left entirely unassisted on the (typically hardest) decisions on which the model abstained. As a remedy, we introduce learning to guide (LTG), an alternative framework in which - rather than taking control from the human expert - the machine provides guidance useful for decision making, and the human is entirely responsible for coming up with a decision. In order to ensure guidance is interpretable} and task-specific, we develop SLOG, an approach for turning any vision-language model into a capable generator of textual guidance by leveraging a modicum of human feedback. Our empirical evaluation highlights the promise of \method on a challenging, real-world medical diagnosis task.
Asymptotics for the Ginzburg–Landau Equation in Arbitrary Dimensions

Let Ω be a bounded, simply connected, regular domain of RN, N⩾2. For 0<e<1, let ue: Ω→C be a smooth solution of the Ginzburg–Landau equation in Ω with Dirichlet boundary condition ge, i.e.,[formula] We are interested in the asymptotic behavior of ue as e goes to zero under the assumption that Ee(ue)⩽M0 |log e| and some conditions on ge which allow singularities of dimension N−3 on ∂Ω.
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data

Crop classification is of critical importance due to its role in studying crop pattern changes, resource management, and carbon sequestration. When employing data-driven techniques for its prediction, utilizing various temporal data sources is necessary. Deep learning models have proven to be effective for this task by mapping time series data to high-level representation for prediction. However, they face substantial challenges when dealing with multiple input patterns. The literature offers limited guidance for Multi-View Learning (MVL) scenarios, as it has primarily focused on exploring fusion strategies with specific encoders and validating them in local regions. In contrast, we investigate the impact of simultaneous selection of the fusion strategy and the encoder architecture evaluated on a global-scale cropland and crop-type classifications. We use a range of five fusion strategies (Input, Feature, Decision, Ensemble, Hybrid) and five temporal encoder architectures (LSTM, GRU, TempCNN, TAE, L-TAE) as possible MVL model configurations. The validation is on the CropHarvest dataset that provides optical, radar, and weather time series, and topographic information as input data. We found that in scenarios with a limited number of labeled samples, a unique configuration is insufficient for all the cases. Instead, a specialized combination, including encoder and fusion strategy, should be meticulously sought. To streamline this search process, we suggest initially identifying the optimal encoder architecture tailored for a particular fusion strategy, and then determining the most suitable fusion strategy for the classification task. We provide a technical framework for researchers exploring crop classification or related tasks through a MVL approach.
A Semi-Lagrangian Approach for Time and Energy Path Planning Optimization in Static Flow Fields

Efficient path planning for autonomous mobile robots is a critical problem across numerous domains, where optimizing both time and energy consumption is paramount. This paper introduces a novel methodology that considers the dynamic influence of an environmental flow field and considers geometric constraints, including obstacles and forbidden zones, enriching the complexity of the planning problem. We formulate it as a multi-objective optimal control problem, propose a novel transformation called Harmonic Transformation, and apply a semi-Lagrangian scheme to solve it. The set of Pareto efficient solutions is obtained considering two distinct approaches: a deterministic method and an evolutionary-based one, both of which are designed to make use of the proposed Harmonic Transformation. Through an extensive analysis of these approaches, we demonstrate their efficacy in finding optimized paths.
Design and Performance of Resonant Beam Communications -- Part I: Quasi-Static Scenario

This two-part paper studies a point-to-point resonant beam communication (RBCom) system, where two separately deployed retroreflectors are adopted to generate the resonant beam between the transmitter and the receiver, and analyzes the transmission rate of the considered system under both the quasi-static and mobile scenarios. Part I of this paper focuses on the quasi-static scenario where the locations of the transmitter and the receiver are relatively fixed. Specifically, we propose a new information-bearing scheme which adopts a synchronization-based amplitude modulation method to mitigate the echo interference caused by the reflected resonant beam. With this scheme, we show that the quasi-static RBCom channel is equivalent to a Markov channel and can be further simplified as an amplitude-constrained additive white Gaussian noise channel. Moreover, we develop an algorithm that jointly employs the bisection and exhaustive search to maximize its capacity upper and lower bounds. Finally, numerical results validate our analysis. Part II of this paper discusses the performance of the RBCom system under the mobile scenario.
MOGAM: A Multimodal Object-oriented Graph Attention Model for Depression Detection

Early detection plays a crucial role in the treatment of depression. Therefore, numerous studies have focused on social media platforms, where individuals express their emotions, aiming to achieve early detection of depression. However, the majority of existing approaches often rely on specific features, leading to limited scalability across different types of social media datasets, such as text, images, or videos. To overcome this limitation, we introduce a Multimodal Object-Oriented Graph Attention Model (MOGAM), which can be applied to diverse types of data, offering a more scalable and versatile solution. Furthermore, to ensure that our model can capture authentic symptoms of depression, we only include vlogs from users with a clinical diagnosis. To leverage the diverse features of vlogs, we adopt a multimodal approach and collect additional metadata such as the title, description, and duration of the vlogs. To effectively aggregate these multimodal features, we employed a cross-attention mechanism. MOGAM achieved an accuracy of 0.871 and an F1-score of 0.888. Moreover, to validate the scalability of MOGAM, we evaluated its performance with a benchmark dataset and achieved comparable results with prior studies (0.61 F1-score). In conclusion, we believe that the proposed model, MOGAM, is an effective solution for detecting depression in social media, offering potential benefits in the early detection and treatment of this mental health condition.
Angular constraints on planar frameworks

Consider a collection of points and the sets of slopes or directions of the lines between pairs of points. It is known that the algebraic matroid on this set of elements is the well studied 2-dimensional rigidity matroid. This article analyzes a construction on top of the set of slopes given by an angle constraint system of incidences and angles. In this setting we provide a matricial rigidity formulation of the problem for colored graphs, an algebro-geometric reformulation, precise necessary conditions and a combinatorial characterization of the generic behaviour for a special case.
Playing With Neuroscience: Past, Present and Future of Neuroimaging and Games

Videogames have been a catalyst for advances in many research fields, such as artificial intelligence, human-computer interaction or virtual reality. Over the years, research in fields such as artificial intelligence has enabled the design of new types of games, while games have often served as a powerful tool for testing and simulation. Can this also happen with neuroscience? What is the current relationship between neuroscience and games research? what can we expect from the future? In this article, we'll try to answer these questions, analysing the current state-of-the-art at the crossroads between neuroscience and games and envisioning future directions.
Is there a role for retinoids to treat minimal residual disease in neuroblastoma?

A variety of pre-clinical and clinical data point toward high drug levels of retinoids being required to achieve optimal efficacy against neuroblastoma. The results of the Kohler trial reported in this issue demonstrate that low-dose 13-cis-RA does not have clinical efficacy against neuroblastoma in a setting of minimal residual disease. A comparison of the Kohler trial with the US CCG trial provides clinical evidence that high-dose levels of retinoids are optimal for treating minimal residual disease in neuroblastoma. The comparison of high-dose and low-dose 13-cis-RA studies in neuroblastoma suggests the intriguing possibility that high dose, pulse schedules of other retinoids could be effective as therapeutic and chemopreventive agents in diseases where low-dose, chronic retinoid administration was not effective. Pre-clinical and perhaps clinical studies of the latter concept should be considered. © 2000 Cancer Research Campaign
Endometrial vascular and glandular expression of integrin α v β 3 in women with and without endometriosis

The integrin αvβ3 functions in both cell–cell and cell– extracellular matrix adhesion, and has reported roles in platelet aggregation, immune function, tissue repair, tumour invasion, angiogenesis and uterine receptivity. The aim of this study was to use immunohistochemistry to describe the vascular and glandular expression of integrin αvβ3 in formalin fixed, paraffin embedded endometrium obtained from women with (n J 29) and without (n J 24) endometriosis. The results showed a significant increase in the percentage of vessels expressing αvβ3 in the endometrium of women with endometriosis compared with controls (PJ 0.0001). This difference was more pronounced in the secretory phase ( P J 0.001) than the proliferative phase (P 5 0.016). There was no correlation between vascular αvβ3 expression and the endothelial cell proliferation index (P > 0.05). Vascular sprouts were not observed in any of the 53 endometrial tissues obtained from women with or without endometriosis throughout the menstrual cycle. Results from semi-quantitative scoring of gland immunostaining showed that neither controls ( P J 0.3329) nor the endometriosis group (P J 0.2260) had any significant changes in terms ofαvβ3 expression between the different stages of the menstrual cycle. There was also no difference in glandular αvβ3 expression between women with and without endometriosis (P J 0.4302). These results provide evidence for increased endometrial angiogenesis in women with endometriosis compared with controls, and suggest that glandular expression ofαvβ3 is not related to uterine receptivity per se.
The Influence of Culture on Trust Judgments in Customer Relationship Development by Ethnic Minority Small Businesses**

This paper explores the influence of culture on trust judgments in customer relationship development by ethnic minority small businesses (EMSBs). hao and oon's “cultural mosaic” of overlapping “cultural tiles” is used to develop a generic conceptual model regarding the relationships between multiple cultural identities, trust, and customer relationship development. Drawing on qualitative data from semistructured interviews with 134 Turkish EMSBs in ondon (United Kingdom), our findings contribute to the understanding of how different cultural tiles influence the dimensions of trust judgments in relationship development between EMSBs and different customer groups. Seven propositions and a theoretical framework regarding these relationships are offered for future research.
Electron scattering on microscopic corrugations in graphene

We discuss various scattering mechanisms for Dirac fermions in single-layer graphene. It is shown that scattering on a short-range potential (e.g. due to neutral impurities) is mostly irrelevant for electronic quality of graphene, which is likely to be controlled by charged impurities and ripples (microscopic corrugations of a graphene sheet). The latter are an inherent feature of graphene due to its two-dimensional nature and can also be an important factor in defining the electron mean-free path. We show that certain types of ripples create a long-range scattering potential, similar to Coulomb scatterers, and result in charge-carrier mobility practically independent of carrier concentration, in agreement with experimental observations.
Performance analysis of identity management in the Session Initiation Protocol (SIP)

The Session Initiation Protocol (SIP) is a standard for managing IP multimedia sessions in the Internet. Identity management in SIP is a crucial security field that deals with identifying users in SIP networks and controlling their access to the corresponding resources. RFC 4474 describes a mechanism, based on certificates, for dealing with the SIP users identities. This RFC recommends the use of the RSA algorithm as it is currently the most popular public key cryptography system. The proliferation of small and simple devices as well as the need to increase the capacity of the SIP servers to handle the increasing VoIP traffic will make continued reliance on RSA more challenging over time. In this paper, we describe our implementation of the current RFC 4474, our integration of elliptic curves cryptography into this RFC and show that the corresponding performance is much more significant than the one where RSA is used. This paper can be considered as a first step in standardizing the use of elliptic curves in the identity management for SIP.
The $L^p$ restriction bounds for Neumann data on surface

Let $\{u_\lambda\}$ be a sequence of $L^2$-normalized Laplacian eigenfunctions on a compact two-dimensional smooth Riemanniann manifold $(M,g)$. We seek to get an $L^p$ restriction bounds of the Neumann data $ \lambda^{-1} \partial_\nu u_{\lambda}\,\vline_\gamma$ along a unit geodesic $\gamma$. Using the $T$-$T^*$ argument one can transfer the problem to an estimate of the norm of a Fourier integral operator and show that such bound is $O(\lambda^{-\frac{1}p+\frac{3}2})$. The Van De Corput theorem (Lemma 2.1) plays the crucial role in our proof. Moreover, this upper bound is shown to be optimal.
Altered patterning of neural activity in a tauopathy mouse model

Alzheimer’s disease (AD) is a complex neurodegenerative condition that manifests at multiple levels and involves a spectrum of abnormalities ranging from the cellular to cognitive. Here, we investigate the impact of AD-related tau-pathology on hippocampal circuits in mice engaged in spatial navigation, and study changes of neuronal firing and dynamics of extracellular fields. While most studies are based on analyzing instantaneous or time-averaged characteristics of neuronal activity, we focus on intermediate timescales—spike trains and waveforms of oscillatory potentials, which we consider as single entities. We find that, in healthy mice, spike arrangements and wave patterns (series of crests or troughs) are coupled to the animal’s location, speed, and acceleration. In contrast, in tau-mice, neural activity is structurally disarrayed: brainwave cadence is detached from locomotion, spatial selectivity is lost, the spike flow is scrambled. Importantly, these alterations start early and accumulate with age, which exposes progressive disinvolvement the hippocampus circuit in spatial navigation. These features highlight qualitatively different neurodynamics than the ones provided by conventional analyses, and are more salient, thus revealing a new level of the hippocampal circuit disruptions. Significance We expose differences in WT and tau brains, emerging at the circuit level, using a novel, morphological perspective on neural activity. This approach allows identifying qualitative changes in spiking patterns and in extracellular field oscillations, that are not discernible through traditional time-localized or time-averaged analyses. In particular, analyses of activity patterns facilitates detection of neurodegenerative deviations, conspicuously linking their effects to behavior and locomotion, thus opening a new venue for understanding how the architecture of neural activity shifts from normal to pathological.
Pmt-Mediated O Mannosylation Stabilizes an Essential Component of the Secretory Apparatus, Sec20p, in Candida albicans

ABSTRACT Sec20p is an essential endoplasmic reticulum (ER) membrane protein in yeasts, functioning as a tSNARE component in retrograde vesicle traffic. We show that Sec20p in the human fungal pathogen Candida albicans is extensively O mannosylated by protein mannosyltransferases (Pmt proteins). Surprisingly, Sec20p occurs at wild-type levels in a pmt6 mutant but at very low levels in pmt1 and pmt4 mutants and also after replacement of specific Ser/Thr residues in the lumenal domain of Sec20p. Pulse-chase experiments revealed rapid degradation of unmodified Sec20p (38.6 kDa) following its biosynthesis, while the stable O-glycosylated form (50 kDa) was not formed in a pmt1 mutant. These results suggest a novel function of O mannosylation in eukaryotes, in that modification by specific Pmt proteins will prevent degradation of ER-resident membrane proteins via ER-associated degradation or a proteasome-independent pathway.
Controllable Freezing Transparency for Water Ice on Scalable Graphene Films on Copper

Control of water ice formation on surfaces is of key technological and economic importance, but the fundamental understanding of ice nucleation and growth mechanisms and the design of surfaces for controlling water freezing behaviour remain incomplete. Graphene is a two-dimensional (2D) material that has been extensively studied for its peculiar wetting properties with liquid water incl. a heavily debated wetting transparency. Furthermore, graphene is the parent structure of soot particles that are heavily implicated as nuclei in atmospheric ice formation and consequently graphene is often used as a model surface for computational ice nucleation studies. Despite this, to date experimental reports on ice formation on scalable graphene films remain missing. Towards filling this gap, we here report on the water freezing behaviour on scalably grown chemical vapour deposited (CVD) graphene films on application-relevant polycrystalline copper (Cu). We find that as-grown CVD graphene on Cu can be (as we term it) freezing transparent i.e. the graphene presence does not change the freezing temperature curves of liquid water to solid ice on Cu in our measurements. Such freezing transparency has to date not been considered. We also show that chemical functionalization of the graphene films can result in controllable changes to the freezing behaviour to lower/higher temperatures and that also the observed freezing transparency can be lifted via functionalization. Our work thereby introduces the concept of freezing transparency of graphene on a metal support and also introduces scalable CVD graphene/Cu as an ultimately thin platform towards control of ice nucleation behaviour on a technologically highly relevant metal.
SoK: An Essential Guide For Using Malware Sandboxes In Security Applications: Challenges, Pitfalls, and Lessons Learned

Malware sandboxes provide many benefits for security applications, but they are complex. These complexities can overwhelm new users in different research areas and make it difficult to select, configure, and use sandboxes. Even worse, incorrectly using sandboxes can have a negative impact on security applications. In this paper, we address this knowledge gap by systematizing 84 representative papers for using x86/64 malware sandboxes in the academic literature. We propose a novel framework to simplify sandbox components and organize the literature to derive practical guidelines for using sandboxes. We evaluate the proposed guidelines systematically using three common security applications and demonstrate that the choice of different sandboxes can significantly impact the results. Specifically, our results show that the proposed guidelines improve the sandbox observable activities by at least 1.6x and up to 11.3x. Furthermore, we observe a roughly 25% improvement in accuracy, precision, and recall when using the guidelines to help with a malware family classification task. We conclude by affirming that there is no"silver bullet"sandbox deployment that generalizes, and we recommend that users apply our framework to define a scope for their analysis, a threat model, and derive context about how the sandbox artifacts will influence their intended use case. Finally, it is important that users document their experiment, limitations, and potential solutions for reproducibility