Miracle-dz/UniFAR-FTUs huggingface dataset:
Specification Overfitting in Artificial Intelligence

Machine learning (ML) and artificial intelligence (AI) approaches are often criticized for their inherent bias and for their lack of control, accountability, and transparency. Consequently, regulatory bodies struggle with containing this technology's potential negative side effects. High-level requirements such as fairness and robustness need to be formalized into concrete specification metrics, imperfect proxies that capture isolated aspects of the underlying requirements. Given possible trade-offs between different metrics and their vulnerability to over-optimization, integrating specification metrics in system development processes is not trivial. This paper defines specification overfitting, a scenario where systems focus excessively on specified metrics to the detriment of high-level requirements and task performance. We present an extensive literature survey to categorize how researchers propose, measure, and optimize specification metrics in several AI fields (e.g., natural language processing, computer vision, reinforcement learning). Using a keyword-based search on papers from major AI conferences and journals between 2018 and mid-2023, we identify and analyze 74 papers that propose or optimize specification metrics. We find that although most papers implicitly address specification overfitting (e.g., by reporting more than one specification metric), they rarely discuss which role specification metrics should play in system development or explicitly define the scope and assumptions behind metric formulations.
Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation

Existing generative adversarial network (GAN) based conditional image generative models typically produce fixed output for the same conditional input, which is unreasonable for highly subjective tasks, such as large-mask image inpainting or style transfer. On the other hand, GAN-based diverse image generative methods require retraining/fine-tuning the network or designing complex noise injection functions, which is computationally expensive, task-specific, or struggle to generate high-quality results. Given that many deterministic conditional image generative models have been able to produce high-quality yet fixed results, we raise an intriguing question: is it possible for pre-trained deterministic conditional image generative models to generate diverse results without changing network structures or parameters? To answer this question, we re-examine the conditional image generation tasks from the perspective of adversarial attack and propose a simple and efficient plug-in projected gradient descent (PGD) like method for diverse and controllable image generation. The key idea is attacking the pre-trained deterministic generative models by adding a micro perturbation to the input condition. In this way, diverse results can be generated without any adjustment of network structures or fine-tuning of the pre-trained models. In addition, we can also control the diverse results to be generated by specifying the attack direction according to a reference text or image. Our work opens the door to applying adversarial attack to low-level vision tasks, and experiments on various conditional image generation tasks demonstrate the effectiveness and superiority of the proposed method.
Comparative Study of Transpiration in Cooling Effect of Tree Species in the Atmosphere

Trees create microclimate under their crowns in comparison to the outside ambient atmosphere, which is a result of physical as well as physiological functions of the tree. The cooling produced by trees varies with species due to variation in several anatomical, structural and physiological attributes of the species. Transpiration is one of the most significant physiological functions performed by plants, which affects cooling produced by a tree under its shade. When solar energy impinges on the leaf, water emerges from its surface through transpiration taking the latent heat to convert it into water vapour. This leads to a rise in humidity of the atmosphere and reduction in temperature of the leaf. To remain leaf in equilibrium, it takes heat from the surrounding atmosphere resulting in reduction in temperature of surroundings. Since, transpiration takes place through stomata which are normally located on the ventral side of the leaf, this reduction in temperature is more experienced beneath the crown of the tree. Therefore, the present study was carried out to analyze the role of transpiration in cooling effect of five forestry tree species. The cooling produced by tree species under their shades has been found positively correlated to the transpiration rate whereas the rate of transpiration has responded positively to the ambient temperature and water conductance. However, no definite relationship has been found between frequency of open stomata and the rate of transpiration.
ShadowRemovalNet: Efficient Real-Time Shadow Removal

Shadows significantly impact computer vision tasks, particularly in outdoor environments. State-of-the-art shadow removal methods are typically too computationally intensive for real-time image processing on edge hardware. We propose ShadowRemovalNet, a novel method designed for real-time image processing on resource-constrained hardware. ShadowRemovalNet achieves significantly higher frame rates compared to existing methods, making it suitable for real-time computer vision pipelines like those used in field robotics. Beyond speed, ShadowRemovalNet offers advantages in efficiency and simplicity, as it does not require a separate shadow mask during inference. ShadowRemovalNet also addresses challenges associated with Generative Adversarial Networks (GANs) for shadow removal, including artefacts, inaccurate mask estimations, and inconsistent supervision between shadow and boundary pixels. To address these limitations, we introduce a novel loss function that substantially reduces shadow removal errors. ShadowRemovalNet's efficiency and straightforwardness make it a robust and effective solution for real-time shadow removal in outdoor robotics and edge computing applications.
Speech Robust Bench: A Robustness Benchmark For Speech Recognition

As Automatic Speech Recognition (ASR) models become ever more pervasive, it is important to ensure that they make reliable predictions under corruptions present in the physical and digital world. We propose Speech Robust Bench (SRB), a comprehensive benchmark for evaluating the robustness of ASR models to diverse corruptions. SRB is composed of 69 input perturbations which are intended to simulate various corruptions that ASR models may encounter in the physical and digital world. We use SRB to evaluate the robustness of several state-of-the-art ASR models and observe that model size and certain modeling choices such as discrete representations, and self-training appear to be conducive to robustness. We extend this analysis to measure the robustness of ASR models on data from various demographic subgroups, namely English and Spanish speakers, and males and females, and observed noticeable disparities in the model's robustness across subgroups. We believe that SRB will facilitate future research towards robust ASR models, by making it easier to conduct comprehensive and comparable robustness evaluations.
Equivalence between Erd\H{o}s-Hajnal and polynomial R\"odl and Nikiforov conjectures

It is well-known that polynomial versions of theorems of R\"odl and Nikiforov, as conjectured by Fox and Sudakov and Nguyen, Scott and Seymour imply the classical Erd\H{o}s-Hajnal conjecture. In this note, we prove that these three conjectures are in fact equivalent, extending several previous particular results in this direction by Fox, Nguyen, Scott and Seymour; Nguyen, Scott and Seymour and Gishboliner and Shapira. We deduce that the family of string graphs satisfies the polynomial R\"odl conjecture.
Exponential Stability of Parametric Optimization-Based Controllers via Lur'e Contractivity

In this letter, we investigate sufficient conditions for the exponential stability of LTI systems driven by controllers derived from parametric optimization problems. Our primary focus is on parametric projection controllers, namely parametric programs whose objective function is the squared distance to a nominal controller. Leveraging the virtual system method of analysis and a novel contractivity result for Lur'e systems, we establish a sufficient LMI condition for the exponential stability of an LTI system with a parametric projection-based controller. Separately, we prove additional results for single-integrator systems. Finally, we apply our results to state-dependent saturated control systems and control barrier function-based control and provide numerical simulations.
CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language Model

Instruction tuning represents a prevalent strategy employed by Multimodal Large Language Models (MLLMs) to align with human instructions and adapt to new tasks. Nevertheless, MLLMs encounter the challenge of adapting to users' evolving knowledge and demands. Therefore, how to retain existing skills while acquiring new knowledge needs to be investigated. In this paper, we present a comprehensive benchmark, namely Continual Instruction tuNing (CoIN), to assess existing MLLMs in the sequential instruction tuning paradigm. CoIN comprises 10 commonly used datasets spanning 8 task categories, ensuring a diverse range of instructions and tasks. Besides, the trained model is evaluated from two aspects: Instruction Following and General Knowledge, which assess the alignment with human intention and knowledge preserved for reasoning, respectively. Experiments on CoIN demonstrate that current powerful MLLMs still suffer catastrophic forgetting, and the failure in intention alignment assumes the main responsibility, instead of the knowledge forgetting. To this end, we introduce MoELoRA to MLLMs which is effective to retain the previous instruction alignment. Experimental results consistently illustrate the forgetting decreased from this method on CoIN.
MineXR: Mining Personalized Extended Reality Interfaces

Extended Reality (XR) interfaces offer engaging user experiences, but their effective design requires a nuanced understanding of user behavior and preferences. This knowledge is challenging to obtain without the widespread adoption of XR devices. We introduce MineXR, a design mining workflow and data analysis platform for collecting and analyzing personalized XR user interaction and experience data. MineXR enables elicitation of personalized interfaces from participants of a data collection: for any particular context, participants create interface elements using application screenshots from their own smartphone, place them in the environment, and simultaneously preview the resulting XR layout on a headset. Using MineXR, we contribute a dataset of personalized XR interfaces collected from 31 participants, consisting of 695 XR widgets created from 178 unique applications. We provide insights for XR widget functionalities, categories, clusters, UI element types, and placement. Our open-source tools and data support researchers and designers in developing future XR interfaces.
MGIC: A Multi-Label Gradient Inversion Attack based on Canny Edge Detection on Federated Learning

As a new distributed computing framework that can protect data privacy, federated learning (FL) has attracted more and more attention in recent years. It receives gradients from users to train the global model and releases the trained global model to working users. Nonetheless, the gradient inversion (GI) attack reflects the risk of privacy leakage in federated learning. Attackers only need to use gradients through hundreds of thousands of simple iterations to obtain relatively accurate private data stored on users' local devices. For this, some works propose simple but effective strategies to obtain user data under a single-label dataset. However, these strategies induce a satisfactory visual effect of the inversion image at the expense of higher time costs. Due to the semantic limitation of a single label, the image obtained by gradient inversion may have semantic errors. We present a novel gradient inversion strategy based on canny edge detection (MGIC) in both the multi-label and single-label datasets. To reduce semantic errors caused by a single label, we add new convolution layers' blocks in the trained model to obtain the image's multi-label. Through multi-label representation, serious semantic errors in inversion images are reduced. Then, we analyze the impact of parameters on the difficulty of input image reconstruction and discuss how image multi-subjects affect the inversion performance. Our proposed strategy has better visual inversion image results than the most widely used ones, saving more than 78% of time costs in the ImageNet dataset.
A Decade's Battle on Dataset Bias: Are We There Yet?

We revisit the"dataset classification"experiment suggested by Torralba and Efros a decade ago, in the new era with large-scale, diverse, and hopefully less biased datasets as well as more capable neural network architectures. Surprisingly, we observe that modern neural networks can achieve excellent accuracy in classifying which dataset an image is from: e.g., we report 84.7% accuracy on held-out validation data for the three-way classification problem consisting of the YFCC, CC, and DataComp datasets. Our further experiments show that such a dataset classifier could learn semantic features that are generalizable and transferable, which cannot be simply explained by memorization. We hope our discovery will inspire the community to rethink the issue involving dataset bias and model capabilities.
Knowledge Conflicts for LLMs: A Survey

This survey provides an in-depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge. Our focus is on three categories of knowledge conflicts: context-memory, inter-context, and intra-memory conflict. These conflicts can significantly impact the trustworthiness and performance of LLMs, especially in real-world applications where noise and misinformation are common. By categorizing these conflicts, exploring the causes, examining the behaviors of LLMs under such conflicts, and reviewing available solutions, this survey aims to shed light on strategies for improving the robustness of LLMs, thereby serving as a valuable resource for advancing research in this evolving area.
Low-Mass X-Ray Binaries and Globular Clusters in Centaurus A

We present results of Hubble Space Telescope and Chandra X-Ray Observatory observations of globular clusters (GCs) and low-mass X-ray binaries (LMXBs) in the central regions of Centaurus A. Out of 440 GC candidates, we find 41 host X-ray point sources that are most likely LMXBs. We fit King models to our GC candidates in order to measure their structural parameters. We find that GCs that host LMXBs are denser and more compact and have higher encounter rates and concentrations than the GC population as a whole. We show that the higher concentrations and masses are a consequence of the dependence of LMXB incidence on central density and size plus the general trend for denser GCs to have higher masses and concentrations. We conclude that neither concentration nor mass is a fundamental variable in determining the presence of LMXBs in GCs and that the more fundamental parameters relate to central density and size.
Exploring Factors Associated with Quality of Life in Caregivers of Children and Adolescents with Sickle Cell Disease and HIV: A Comparative Analysis

Introduction Paediatric HIV and sickle cell disease (SCD) are two stigmatising and potentially fatal illnesses that place a significant burden on families. HIV patients benefit from a longstanding free-service national programme in Cameroon, and this could considerably alleviate burden of care on HIV caregivers, possibly leading to better quality of life (QoL) in HIV caregivers compared to SCD caregivers. Our study aimed to compare the QoL between caregivers of children and adolescents with SCD and HIV and explore factors associated with this QoL in Cameroon. Methods and Materials We conducted a hospital-based cross-sectional analytic study at Douala Laquintinie Hospital from February to May 2023. A questionnaire was administered to caregivers of paediatric patients (≤18 years) with SCD and HIV. The Pediatrics Quality of Life-Family Impact Module (PedsQL FIM), the 7-item Generalized Anxiety Disorder (GAD-7), and the 9-item Patient Health Question (PHQ-9) tools were used as measures of quality of life, anxiety, and depression, respectively. Multivariable linear regression was used to determine factors associated with quality of life. A significance level was set at p < 0.05. Results We included 199 caregivers: SCD = 104 and HIV = 95. The mean age of caregivers in our sample was 40.47 ± 10.18 years. Caregivers of paediatric patients with HIV had a better mean quality of life than SCD (93.01 ± 7.35SD versus 64.86 ± 9.20SD, p < 0.001). PHQ-9 score (B = −1.52, 95% CI = [-2.08; −0.96], p=<0.001), GAD-7 score (B = −1.46, 95% CI = [-2.09; −0.83], p=<0.001), spending less than 75 000 FCFA on medications monthly (B = 12.13, 95% CI = [5.73; 18.94], p=<0.001), and being a SCD caregiver (B = −11.62, 95% CI = [-18.46; −4.78], p=0.001) were factors independently associated with quality of life on multivariable analysis. Conclusion Quality of life is lower in caregivers of children and adolescents with SCD than with HIV. Preventing depression and anxiety as well as advocating for the subsidization of medications through a national SCD program may improve quality of life in SCD caregivers.
MLL1 regulates cytokine-driven cell migration and metastasis

Cell migration is a critical requirement for cancer metastasis. Cytokine production and its role in cancer cell migration have been traditionally associated with immune cells in the tumor microenvironment. MLL1 is a histone methyltransferase that controls 3D cell migration via the secretion of cytokines, IL-6 and TGF-β1, by the cancer cells themselves. In vivo, MLL1 depletion reduced metastatic burden and prolonged survival. MLL1 exerts its effects with its scaffold protein, Menin. Mechanistically, the MLL1-Menin interaction controls actin filament assembly via the IL-6/pSTAT3/Arp3 axis and acto-myosin contractility via the TGF-β1/Gli2/ROCK1/2/pMLC2 axis, which regulate dynamic protrusion generation and 3D cell migration. MLL1 also regulates cell proliferation via mitosis-based and cell cycle-related pathways. Combining an MLL1-Menin inhibitor with Paclitaxel, a standard chemotherapeutic, abrogated tumor growth and metastasis in a syngeneic model. These results highlight the potential of targeting the MLL1 in metastasis prevention and its potential to be combined with currently administered chemotherapeutics. Statement of Significance We identify MLL1 as being vital to metastasis, which causes the vast majority of cancer-related deaths. MLL1 controls cell migration, a requirement for metastasis, by regulating the secretion of cytokines. MLL1 inhibition lowers metastatic burden independent of its impact on primary tumor growth, highlighting its anti-metastatic potential in TNBC.
Metabolic reprogramming and signalling cross-talks in tumour–immune interaction: a system-level exploration

Tumour-immune microenvironment (TIME) is pivotal in tumour progression and immunoediting. Within TIME, immune cells undergo metabolic adjustments impacting nutrient supply and the anti-tumour immune response. Metabolic reprogramming emerges as a promising approach to revert the immune response towards a pro-inflammatory state and conquer tumour dominance. This study proposes immunomodulatory mechanisms based on metabolic reprogramming and employs the regulatory flux balance analysis modelling approach, which integrates signalling, metabolism and regulatory processes. For the first time, a comprehensive system-level model is constructed to capture signalling and metabolic cross-talks during tumour–immune interaction and regulatory constraints are incorporated by considering the time lag between them. The model analysis identifies novel features to enhance the immune response while suppressing tumour activity. Particularly, altering the exchange of succinate and oxaloacetate between glioma and macrophage enhances the pro-inflammatory response of immune cells. Inhibition of glutamate uptake in T-cells disrupts the antioxidant mechanism of glioma and reprograms metabolism. Metabolic reprogramming through adenosine monophosphate (AMP)-activated protein kinase (AMPK), coupled with glutamate uptake inhibition, was identified as the most impactful combination to restore T-cell function. A comprehensive understanding of metabolism and gene regulation represents a favourable approach to promote immune cell recovery from tumour dominance.
The Effect of Simulation-Based Education on Patient Training Correct Inhaler Technique of Nursing Students: A Randomized Controlled Trial

Introduction Teaching inhaler techniques to nursing students is crucial. Objective This study aimed to investigate the effect of simulation-based education on patient training correct inhaler technique in nursing students. Methods This is a single-blind, randomized controlled trial in which nursing students are randomly allocated into two groups of control (n = 41) and intervention (n = 40). The students’ mean score of correct inhaler technique was measured before and one week after intervention, using checklists of inhaler techniques. Results The mean score of control group was significantly lower than the intervention group (p < .001). The results also revealed that there was no significant difference in students’ level of confidence regarding the ability to educate patients on inhaler techniques before the intervention, while all students were shown to have higher levels of confidence in this term after the intervention. Conclusions Simulation is an effective way to train nursing students in the correct inhaler techniques.
Examining the association between speed and myoelectric activity: Time-based differences and muscle group balance

The purpose of this study is to investigate the relationship between speed and myoelectric activity, measured during an incremental 25m shuttle running test, exploring the time-based variations and assessing muscle group balance within the context of this association. Twelve male young soccer players (n = 12) aged 18±1.2 years, with an average body mass of 68.4±5.8kg and average body height of 1.72±0.08m, from a professional Italian youth team (Italian “Primavera”), volunteered as participants for this study. The speed of each player during testing was measured using GPS technology, sampling at 50Hz. Myoelectrical activities of the gluteus, hamstrings, and quadriceps muscles were recorded through wearable sEMG devices, sampled at 100Hz. To ensure alignment of the sampling frequencies, the sEMG data was resampled to 50Hz, matching the GPS data sampling rate. This allowed for direct comparison and analysis of the data obtained from both measurement systems. The collected data were then analyzed to determine the relationship between the investigated variables and any potential differences associated with different sides of the body. The results revealed a robust correlation (r2≈0.97) between the speed of the participants (m·s-1) and their myoelectrical activity (μV) during the test. Factorial ANOVA 2x11 showed no significant differences between the sides of the analyzed muscles (p>0.05). The interpolation lines generated by the association of speed and sEMG exhibit very similar angular coefficients (0.9 to 0.12) in all six measurements obtained from electromyography of the three investigated muscle groups on each side of the body. In conclusion, the concurrent validity between the two instruments in this study indicates that GPS and sEMG are valid and consistent in estimating external load and internal load during incremental shuttle running.