Miracle-dz/UniFAR-FTUs huggingface dataset:
How Reliable Are ATP Bioluminescence Meters in Assessing Decontamination of Environmental Surfaces in Healthcare Settings?

Background Meters based on adenosine triphosphate (ATP) bioluminescence measurements in relative light units (RLU) are often used to rapidly assess the level of cleanliness of environmental surfaces in healthcare and other settings. Can such ATP measurements be adversely affected by factors such as soil and cleaner-disinfectant chemistry? Objective This study tested a number of leading ATP meters for their sensitivity, linearity of the measurements, correlation of the readings to the actual microbial contamination, and the potential disinfectant chemicals’ interference in their readings. Methods First, solutions of pure ATP in various concentrations were used to construct a standard curve and determine linearity and sensitivity. Serial dilutions of a broth culture of Staphylococcus aureus, as a representative nosocomial pathogen, were then used to determine if a given meter’s ATP readings correlated with the actual CFUs. Next, various types of disinfectant chemistries were tested for their potential to interfere with the standard ATP readings. Results All four ATP meters tested herein demonstrated acceptable linearity and repeatability in their readings. However, there were significant differences in their sensitivity to detect the levels of viable microorganisms on experimentally contaminated surfaces. Further, most disinfectant chemistries tested here quenched the ATP readings variably in different ATP meters evaluated. Conclusions Apart from their limited sensitivity in detecting low levels of microbial contamination, the ATP meters tested were also prone to interference by different disinfectant chemistries.
Open Stamped Parts Dataset

We present the Open Stamped Parts Dataset (OSPD), featuring synthetic and real images of stamped metal sheets for auto manufacturing. The real part images, captured from 7 cameras, consist of 7,980 unlabeled images and 1,680 labeled images. In addition, we have compiled a defect dataset by overlaying synthetically generated masks on 10% of the holes. The synthetic dataset replicates the real manufacturing environment in terms of lighting and part placement relative to the cameras. The synthetic data includes 7,980 training images, 1,680 validation images and 1,680 test images, each with bounding box and segmentation mask annotations around all holes. 10% of the holes in the synthetic data mimic defects generated in the real image dataset. We trained a hole-detection model on the synthetic-OSPD, achieving a modified recall score of 67.2% and a precision of 94.4% . We anticipate researchers in the auto manufacturing and broader machine learning and computer vision communities using OSPD to advance the state of the art in defect detection of stamped holes in the metalsheet stamping process. The dataset is available for download at: https://tinyurl.com/hm6xatd7
Content-Split Based Effective String-Matching for Multi-Core Based Intrusion Detection Systems

We present a Content Split Approach (CSA), tailored specifically for signature-based network intrusion detection. This algorithm logically partition the content of IP Packets into three parts and internally uses boyer-moorehorspool algorithm to carry out string-matching simultaneously on these parts. Traditionally, skip based pattern matching algorithms use a single sliding window moving from left to right to detect a pattern to be matched, whereas CSA uses two sliding windows of the pattern simultaneously—one moving towards the right from the start position, towards the middle of the string, and the second starting from the middle and moving towards the end of the string. If both these moving patterns never find a match then CSA evaluates the middle of the string. In this paper, firstly we present our approach and experiments, secondly, we present an extension for Jumbo frames and finally, we present the application of our algorithm for multicore based Intrusion Detection System.
Synthesizing realistic facial expressions from photographs

We present new techniques for creating photorealistic textured 3D facial models from photographs of a human subject, and for creating smooth transitions between different facial expressions by morphing between these different models. Starting from several uncalibrated views of a human subject, we employ a user-assisted technique to recover the camera poses corresponding to the views as well as the 3D coordinates of a sparse set of chosen locations on the subject’s face. A scattered data interpolation technique is then used to deform a generic face mesh to fit the particular geometry of the subject’s face. Having recovered the camera poses and the facial geometry, we extract from the input images one or more texture maps for the model. This process is repeated for several facial expressions of a particular subject. To generate transitions between these facial expressions we use 3D shape morphing between the corresponding face models, while at the same time blending the corresponding textures. Using our technique, we have been able to generate highly realistic face models and natural looking animations. CR Categories: I.2. 10 [Artificial Intelligence]: Vision and Scene Understanding — Modeling and recovery of physical attributes; I.3. 7 [Computer Graphics]: Three-Dimensional Graphics — Animation; I.3. 7 [Computer Graphics]: Three-Dimensional Graphics — Color, shading, shadowing and texture. Additional
α-Synuclein Levels in Blood Plasma from LRRK2 Mutation Carriers

The diagnosis of Parkinson’s disease (PD) remains primarily a clinical issue, based mainly on phenotypic patterns. The identification of biomarkers capable of permitting the preclinical detection of PD is critically needed. α-Synuclein is a key protein in PD, with missense and multiplication mutations in the gene encoding α-synuclein (SNCA) having been reported in familial cases of PD, and accumulation of the protein identified in Lewy bodies (LBs) and Lewy neurites (LNs) in affected brain regions. With the objective of validating the use of α-synuclein as a clinical or progressive biomarker in an accessible tissue, we used an enzyme-linked immunosorbent assay (ELISA) to measure α-synuclein levels in the peripheral blood plasma of idiopathic PD and LRRK2 mutation carrier patients and compared our findings with healthy control subjects. Compared to healthy controls, we found a significant decrease in plasma total α-synuclein levels in idiopathic PD (iPD) patients (n = 134, p = 0.010). However, the reduction was less significant in patients who were LRRK2 mutation carriers (n = 32, p = 0.133). This lack of significance could be due to the small number of individuals employed in this group. No predictive value of total α-synuclein in the diagnosis of PD was found in a receiver operating characteristic (ROC) curve analysis. Although this is a pilot study requiring corroboration on a larger cohort of patients, our results highlight the possible use of plasma α-synuclein as a biomarker for PD.
Multiscale Matching Driven by Cross-Modal Similarity Consistency for Audio-Text Retrieval

Audio-text retrieval (ATR), which retrieves a relevant caption given an audio clip (A2T) and vice versa (T2A), has recently attracted much research attention. Existing methods typically aggregate information from each modality into a single vector for matching, but this sacrifices local details and can hardly capture intricate relationships within and between modalities. Furthermore, current ATR datasets lack comprehensive alignment information, and simple binary contrastive learning labels overlook the measurement of fine-grained semantic differences between samples. To counter these challenges, we present a novel ATR framework that comprehensively captures the matching relationships of multimodal information from different perspectives and finer granularities. Specifically, a fine-grained alignment method is introduced, achieving a more detail-oriented matching through a multiscale process from local to global levels to capture meticulous cross-modal relationships. In addition, we pioneer the application of cross-modal similarity consistency, leveraging intra-modal similarity relationships as soft supervision to boost more intricate alignment. Extensive experiments validate the effectiveness of our approach, outperforming previous methods by significant margins of at least 3.9% (T2A) / 6.9% (A2T) R@1 on the AudioCaps dataset and 2.9% (T2A) / 5.4% (A2T) R@1 on the Clotho dataset.
Constrained Multi-Relational Hyper-Graphons with Maximum Entropy

This work has two contributions. The first one is extending the Large Deviation Principle for uniform hyper-graphons from Lubetzky and Zhao \cite{lubetzky2015replica} to the multi-relational setting where each hyper-graphon can have different arities. This extension enables the formulation of the most typical possible world in Relational Probabilistic Logic with symmetric relational symbols in terms of entropy maximization subjected to constraints of quantum sub-hypergraph densities. The second contribution is to prove the most typical constrained multi-relational hyper-graphons (the most typical possible worlds) are computable by proving the solutions of the maximum entropy subjected by quantum sub-hypergraph densities in the space of multi-relational hyper-graphons are step functions except for in a zero measure set of combinations of quantum hyper-graphs densities with multiple relations. This result proves in a very general context the conjecture formulated by Radin et al.\ \cite{radin2014asymptotics} that states the constrained graphons with maximum entropy are step functions.
Neuronal Functional Connection Graphs among Multiple Areas of the Rat Somatosensory System during Spontaneous and Evoked Activities

Small-World Networks (SWNs) represent a fundamental model for the comprehension of many complex man-made and biological networks. In the central nervous system, SWN models have been shown to fit well both anatomical and functional maps at the macroscopic level. However, the functional microscopic level, where the nodes of a network are represented by single neurons, is still poorly understood. At this level, although recent evidences suggest that functional connection graphs exhibit small-world organization, it is not known whether and how these maps, potentially distributed in multiple brain regions, change across different conditions, such as spontaneous and stimulus-evoked activities. We addressed these questions by analyzing the data from simultaneous multi-array extracellular recordings in three brain regions of rats, diversely involved in somatosensory information processing: the ventropostero-lateral thalamic nuclei, the primary somatosensory cortex and the centro-median thalamic nuclei. From both spike and Local Field Potential (LFP) recordings, we estimated the functional connection graphs by using the Normalized Compression Similarity for spikes and the Phase Synchrony for LFPs. Then, by using graph-theoretical statistics, we characterized the functional topology both during spontaneous activity and sensory stimulation. Our main results show that: (i) spikes and LFPs show SWN organization during spontaneous activity; (ii) after stimulation onset, while substantial functional graph reconfigurations occur both in spike and LFPs, small-worldness is nonetheless preserved; (iii) the stimulus triggers a significant increase of inter-area LFP connections without modifying the topology of intra-area functional connections. Finally, investigating computationally the functional substrate that supports the observed phenomena, we found that (iv) the fundamental concept of cell assemblies, transient groups of activating neurons, can be described by small-world networks. Our results suggest that activity of neurons from multiple areas of the rat somatosensory system contributes to the integration of local computations arisen in distributed functional cell assemblies according to the principles of SWNs.
Partial-coherence abstractions for relaxed memory models

We present an approach for automatic verification and fence inference in concurrent programs running under relaxed memory models. Verification under relaxed memory models is a hard problem. Given a finite state program and a safety specification, verifying that the program satisfies the specification under a sufficiently relaxed memory model is undecidable. For stronger models, the problem is decidable but has non-primitive recursive complexity. In this paper, we focus on models that have store-buffer based semantics, e.g., SPARC TSO and PSO. We use abstract interpretation to provide an effective verification procedure for programs running under this type of models. Our main contribution is a family of novel partial-coherence abstractions, specialized for relaxed memory models, which partially preserve information required for memory coherence and consistency. We use our abstractions to automatically verify programs under relaxed memory models. In addition, when a program violates its specification but can be fixed by adding fences, our approach can automatically infer a correct fence placement that is optimal under the abstraction. We implemented our approach in a tool called BLENDER and applied it to verify and infer fences in several concurrent algorithms.
H3K27 modifications define segmental regulatory domains in the Drosophila bithorax complex

The bithorax complex (BX-C) in Drosophila melanogaster is a cluster of homeotic genes that determine body segment identity. Expression of these genes is governed by cis-regulatory domains, one for each parasegment. Stable repression of these domains depends on Polycomb Group (PcG) functions, which include trimethylation of lysine 27 of histone H3 (H3K27me3). To search for parasegment-specific signatures that reflect PcG function, chromatin from single parasegments was isolated and profiled. The H3K27me3 profiles across the BX-C in successive parasegments showed a ‘stairstep’ pattern that revealed sharp boundaries of the BX-C regulatory domains. Acetylated H3K27 was broadly enriched across active domains, in a pattern complementary to H3K27me3. The CCCTC-binding protein (CTCF) bound the borders between H3K27 modification domains; it was retained even in parasegments where adjacent domains lack H3K27me3. These findings provide a molecular definition of the homeotic domains, and implicate precisely positioned H3K27 modifications as a central determinant of segment identity. DOI: http://dx.doi. org/10.7554/eLife.02833. 001
Compositionally Verifiable Vector Neural Lyapunov Functions for Stability Analysis of Interconnected Nonlinear Systems

While there has been increasing interest in using neural networks to compute Lyapunov functions, verifying that these functions satisfy the Lyapunov conditions and certifying stability regions remain challenging due to the curse of dimensionality. In this paper, we demonstrate that by leveraging the compositional structure of interconnected nonlinear systems, it is possible to verify neural Lyapunov functions for high-dimensional systems beyond the capabilities of current satisfiability modulo theories (SMT) solvers using a monolithic approach. Our numerical examples employ neural Lyapunov functions trained by solving Zubov's partial differential equation (PDE), which characterizes the domain of attraction for individual subsystems. These examples show a performance advantage over sums-of-squares (SOS) polynomial Lyapunov functions derived from semidefinite programming.
Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation

Synthetic users are cost-effective proxies for real users in the evaluation of conversational recommender systems. Large language models show promise in simulating human-like behavior, raising the question of their ability to represent a diverse population of users. We introduce a new protocol to measure the degree to which language models can accurately emulate human behavior in conversational recommendation. This protocol is comprised of five tasks, each designed to evaluate a key property that a synthetic user should exhibit: choosing which items to talk about, expressing binary preferences, expressing open-ended preferences, requesting recommendations, and giving feedback. Through evaluation of baseline simulators, we demonstrate these tasks effectively reveal deviations of language models from human behavior, and offer insights on how to reduce the deviations with model selection and prompting strategies.
Layer buckling and absence of superconductivity in LaNiO2

The recent observation of unconventional superconductivity in thin films of LaNiO2 (critical temperature, Tc~10 K) and in bulk single crystals of La3Ni2O7 (327) under pressure (Tc~80 K), has brought to light a long sought-after class of superconducting nickelates. Through structural measurements in the 327-system, it was shown that the absence of superconductivity is related to bending of the O-Ni-O bonds. Similarly, the bond bending may be linked to the absence of superconductivity in bulk LaNiO2. Neutron diffraction was used on bulk non-superconducting La(1-x)Sr(x)NiO(2) samples to show that the layers are naturally buckled, creating a Ni-O-Ni bond angle of 177 at 2 K and ambient pressure. The buckling angle increases to 170 on warming to room temperature. Furthermore, a broad paramagnetic continuum is observed that decreases in intensity on cooling from room temperature signaling a possible transition to a coherent state. However, no antiferromagnetic (AFM) peaks are detected, although enhancement of ferromagnetic (FM) correlations cannot be excluded.
Spatial and temporal dynamic of land-cover/land-use and carbon stocks in Eastern Cameroon: a case study of the teaching and research forest of the University of Dschang

Abstract This study was carried out in the teaching and research forest of the University of Dschang in Belabo, with the aim of analysing land-cover and land-use changes as well as carbon stocks dynamic. The databases used are composed of three Landsat satellite images (5TM of 1984, 7ETM + of 2000 and 8OLI of 2016), enhanced by field missions. Satellite images were processed using ENVI and ArcGIS software. Interview, focus group discussion methods and participatory mapping were used to identify the activities carried out by the local population. An inventory design consisting of four transects was used to measure dendrometric parameters and to identify land-use types. An estimation of carbon stocks in aboveground and underground woody biomass was made using allometric models based on non-destructive method. Dynamic of land-cover showed that the average annual rate of deforestation is 0.48%. The main activities at the base of this change are agriculture, house built-up and logging. Seven types of land-use were identified; adult secondary forests (64.10%), young secondary forests (7.54%), wetlands (7.39%), fallows (3.63%), savannahs (9.59%), cocoa farms (4.28%) and mixed crop farms (3.47%). Adult secondary forests had the highest amount of carbon (250.75 t C ha−1). This value has decreased by more than 60% for mixed crop farms (94.67 t C ha−1), showing the impact of agricultural activities on both forest cover and carbon stocks. Agroforestry systems that allow conservation and introduction of woody species should be encouraged as part of a participatory management strategy of this forest.
NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices

Real-time high-accuracy optical flow estimation is a crucial component in various applications, including localization and mapping in robotics, object tracking, and activity recognition in computer vision. While recent learning-based optical flow methods have achieved high accuracy, they often come with heavy computation costs. In this paper, we propose a highly efficient optical flow architecture, called NeuFlow, that addresses both high accuracy and computational cost concerns. The architecture follows a global-to-local scheme. Given the features of the input images extracted at different spatial resolutions, global matching is employed to estimate an initial optical flow on the 1/16 resolution, capturing large displacement, which is then refined on the 1/8 resolution with lightweight CNN layers for better accuracy. We evaluate our approach on Jetson Orin Nano and RTX 2080 to demonstrate efficiency improvements across different computing platforms. We achieve a notable 10x-80x speedup compared to several state-of-the-art methods, while maintaining comparable accuracy. Our approach achieves around 30 FPS on edge computing platforms, which represents a significant breakthrough in deploying complex computer vision tasks such as SLAM on small robots like drones. The full training and evaluation code is available at https://github.com/neufieldrobotics/NeuFlow.
Fisher Mask Nodes for Language Model Merging

Fine-tuning pre-trained models provides significant advantages in downstream performance. The ubiquitous nature of pre-trained models such as BERT and its derivatives in natural language processing has also led to a proliferation of task-specific fine-tuned models. As these models typically only perform one task well, additional training or ensembling is required in multi-task scenarios. The growing field of model merging provides a solution, dealing with the challenge of combining multiple task-specific models into a single multi-task model. In this study, we introduce a novel model merging method for Transformers, combining insights from previous work in Fisher-weighted averaging and the use of Fisher information in model pruning. Utilizing the Fisher information of mask nodes within the Transformer architecture, we devise a computationally efficient weighted-averaging scheme. Our method exhibits a regular and significant performance increase across various models in the BERT family, outperforming full-scale Fisher-weighted averaging in a fraction of the computational cost, with baseline performance improvements of up to +6.5 and a speedup of 57.4x in the biggest model. Our results prove the potential of our method in current multi-task learning environments and suggest its scalability and adaptability to new model architectures and learning scenarios.
Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification

Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be used in practice. To define a reliable learning framework for classification from the very beginning of its design, the concept of scalable classifier was introduced to generalize the concept of classical classifier by linking it to statistical order theory and probabilistic learning theory. In this paper, we analyze the similarities between scalable classifiers and conformal predictions by introducing a new definition of a score function and defining a special set of input variables, the conformal safety set, which can identify patterns in the input space that satisfy the error coverage guarantee, i.e., that the probability of observing the wrong (possibly unsafe) label for points belonging to this set is bounded by a predefined $\varepsilon$ error level. We demonstrate the practical implications of this framework through an application in cybersecurity for identifying DNS tunneling attacks. Our work contributes to the development of probabilistically robust and reliable machine learning models.