Miracle-dz/UniFAR-FTUs huggingface dataset:
Identiﬁcation of the Bacterial Microﬂora in Dairy Products by Temporal Temperature Gradient Gel Electrophoresis

Numerous microorganisms, including bacteria, yeasts, and molds, are present in cheeses, forming a complex ecosystem. Among these organisms, bacteria are responsible for most of the physicochemical and aromatic transformations that are intrinsic to the cheesemaking process. Identiﬁcation of the bacteria that constitute the cheese ecosystem is essential for understanding their individual contributions to cheese production. We used temporal temperature gradient gel electrophoresis (TTGE) to identify different bacterial species present in several dairy products, including members of the genera Lactobacillus , Lactococcus , Leuconostoc , Enterococcus , Pediococcus , Streptococcus , and Staphylococcus . The TTGE technique is based on electrophoretic separation of 16S ribosomal DNA (rDNA) fragments by using a temperature gradient. It was optimized to reveal differences in the 16S rDNA V3 regions of bacteria with low-G (cid:1) C-content genomes. Using multiple control strains, we ﬁrst set up a species database in which each species (or group of species) was characterized by a speciﬁc TTGE ﬁngerprint. TTGE was then applied to controlled dairy ecosystems with deﬁned compositions, Invitrogen BV, Groningen, The Netherlands), and transformed into Escherichia coli , as described by the manufacturer. Cloning of the PCR products was necessary because TTGE analysis revealed weak bands in addition to the excised bands after reampliﬁcation. The resulting plasmids were used as a matrix to reamplify the insert with primers HDA1-GC and HDA2. The ampli- cons were subjected to TTGE analysis to conﬁrm their relative positions. The cloned fragments that comigrated with the original bands were then sequenced by using an ABI PRISM dye terminator kit (7) and primer M13 (Invitrogen BV). Sequences were compared to the Ribosomal Database Project sequences (29) for species assignment.
The Dust & Gas Properties of M83

We examine the dust and gas properties of the nearby, barred galaxy M83, which is part of the Very Nearby Galaxy Survey. Using images from the PACS and SPIRE instruments of Herschel, we examine the dust temperature and dust mass surface density distribution. We find that the nuclear, bar and spiral arm regions exhibit higher dust temperatures and masses compared to interarm regions. However, the distribution of dust temperature and mass are not spatially coincident. Assuming a trailing spiral structure, the dust temperature peaks in the spiral arms lie ahead of the dust surface density peaks. The dust mass surface density correlates well with the distribution of molecular gas as traced by CO (J=3-2) images (JCMT) and the star formation rate as traced by H? 2 with a correction for obscured star formation using 24 micron emission. Using HI images from THINGS to trace the atomic gas component, we make total gas mass surface density maps and calculate the gas-to-dust ratio. We find a mean gas-to-dust ratio of 84 \pm 4 with higher values in the inner region assuming a constant CO-to-H2 conversion factor. We also examine the gas-to-dust ratio using CO-to-H2 conversion factor that varies with metallicity.
Napat Amornratananont, M.D., Premsant Sangkum, M.D., The Impact of Body Mass Index on Perioperative Outcomes in Patients Undergoing Percutaneous Nephrolithotomy

Background : To date, percutaneous nephrolithotomy (PCNL) is the gold standard treatment for patients with large renal calculi (RC). Whether obesity has an adverse effect on its success remains controversial. Objective : To compare the perioperative outcomes of PCNL among Thai individuals with various body mass indices (BMI). Material and method : A retrospective, cross-sectional study was conducted among 205 patients who underwent PCNL in Ramathibodi Hospital between January 2011 and August 2019. The patients were classified into normal weight (n=98, BMI 18.5 - 24.9 kg/m 2 ); overweight (n=76, BMI 25.0 - 29.9 kg/m 2 ); and obese group (n=31, BMI ≥30.0 kg/m 2 ). Perioperative outcomes were statistically compared between groups. Results : Stone-free status in the obese group was not different from the normal weight and overweight group (64.5%, 58.2% and 50.0% respectively, p=0.338). There were no significant differences in blood loss (p=0.966), operative time (p=0.663), percentage change of eGFR (p=0.259), length of stay (p=0.381) and postoperative complications (p=0.435) between the three groups. Conclusion : BMI did not affect the perioperative outcomes of PCNL. Its outcomes in obese patients were not different from those of normal-weight patients.
Salinity E(cid:128)ects on the Activity of Plasma Membrane H (cid:135) and Ca 2 (cid:135) Transporters in Bean Leaf Mesophyll: Masking Role of the Cell Wall

Net ﬂuxes of H (cid:135) and Ca 2 (cid:135) were measured in the mesophyll tissue of broad bean ( Vicia faba L.) leaves and in protoplasts derived from these cells. NaCl at 90 m M enhanced H (cid:135) extrusion in both protoplasts and tissue, but in di(cid:128)erent ways. Proton extrusion was inhibited by vanadate, suggesting the involvement of the plasma membrane H (cid:135) - ATPase in cell responses to salinity. There was virtually no e(cid:128)ect of NaCl on the net Ca 2 (cid:135) ﬂux in protoplasts, while in the tissue a large transient Ca 2 (cid:135) e(cid:130)ux followed the salt treatment. Salt-induced Ca 2 (cid:135) e(cid:130)ux was essentially independent of external Ca 2 (cid:135) concentrations in the range 0 . 1 to 10 m M . Also, Ca 2 (cid:135) ﬂux responses were ‘saturated’ above 50 m M NaCl. It is suggested that almost all the measured Ca 2 (cid:135) ﬂux originates from Na (cid:135) /Ca 2 (cid:135) and H (cid:135) /Ca 2 (cid:135) ion exchange in the cell wall. This conclusion was supported by the results of modelling cation exchange in the cell wall.
Evaluating Plant Disease Detection Mobile Applications: Quality and Limitations

In this technologically advanced era, with the proliferation of artificial intelligence, many mobile apps are available for plant disease detection, diagnosis, and treatment, each with a variety of features. These apps need to be categorized and reviewed following a proper framework that ensures their quality. This study aims to present an approach to evaluating plant disease detection mobile apps, which includes providing ratings of distinct features of the apps and insights into the exploitation of artificial intelligence used in plant disease detection. The applicability of these apps for pathogen or disease detection, identification, and treatment will be assessed along with significant insights garnered. For this purpose, plant disease detection apps were searched in three prominent app stores (the Google Play store, Apple App store, and Microsoft store) using a set of keywords. A total of 606 apps were found and from them, 17 relevant apps were identified based on inclusion and exclusion criteria. The selected apps were reviewed by three raters using our devised app rating scale. To validate the rater agreements on the ratings, inter-rater reliability is computed alongside their intra-rater reliability, ensuring their rating consistency. Also, the internal consistency of our rating scale was evaluated against all selected apps. User comments from the app stores are collected and analyzed to understand their expectations and views. Following the rating procedure, most apps earned acceptable ratings in software quality characteristics such as aesthetics, usability, and performance but gained poor ratings in AI-based advanced functionality, which is the key aspect of this study. However, most of the apps cannot be used as a complete solution to plant disease detection, diagnosis, and treatment. Only one app, Plantix–your crop doctor, could successfully identify plants from images, detect diseases, maintain a rich plant database, and suggest potential treatments for the disease presented. It also provides a community where plant lovers can communicate with each other to gain additional benefits. In general, all existing apps need to improve functionalities, user experience, and software quality. Therefore, a set of design considerations has been proposed for future app improvements.
On the Learnability of Physical Concepts: Can a Neural Network Understand What's Real?

We revisit the classic signal-to-symbol barrier in light of the remarkable ability of deep neural networks to generate realistic synthetic data. DeepFakes and spoofing highlight the feebleness of the link between physical reality and its abstract representation, whether learned by a digital computer or a biological agent. Starting from a widely applicable definition of abstract concept, we show that standard feed-forward architectures cannot capture but trivial concepts, regardless of the number of weights and the amount of training data, despite being extremely effective classifiers. On the other hand, architectures that incorporate recursion can represent a significantly larger class of concepts, but may still be unable to learn them from a finite dataset. We qualitatively describe the class of concepts that can be"understood"by modern architectures trained with variants of stochastic gradient descent, using a (free energy) Lagrangian to measure information complexity. Even if a concept has been understood, however, a network has no means of communicating its understanding to an external agent, except through continuous interaction and validation. We then characterize physical objects as abstract concepts and use the previous analysis to show that physical objects can be encoded by finite architectures. However, to understand physical concepts, sensors must provide persistently exciting observations, for which the ability to control the data acquisition process is essential (active perception). The importance of control depends on the modality, benefiting visual more than acoustic or chemical perception. Finally, we conclude that binding physical entities to digital identities is possible in finite time with finite resources, solving in principle the signal-to-symbol barrier problem, but we highlight the need for continuous validation.
Globally Consistent Video Depth and Pose Estimation with Efficient Test-Time Training

Dense depth and pose estimation is a vital prerequisite for various video applications. Traditional solutions suffer from the robustness of sparse feature tracking and insufficient camera baselines in videos. Therefore, recent methods utilize learning-based optical flow and depth prior to estimate dense depth. However, previous works require heavy computation time or yield sub-optimal depth results. We present GCVD, a globally consistent method for learning-based video structure from motion (SfM) in this paper. GCVD integrates a compact pose graph into the CNN-based optimization to achieve globally consistent estimation from an effective keyframe selection mechanism. It can improve the robustness of learning-based methods with flow-guided keyframes and well-established depth prior. Experimental results show that GCVD outperforms the state-of-the-art methods on both depth and pose estimation. Besides, the runtime experiments reveal that it provides strong efficiency in both short- and long-term videos with global consistency provided.
Non-destructive one-shot device testing under step-stress model with Weibull lifetime distributions

One-shot devices are product or equipment that can be used only once, so they get destroyed when tested. However, the destructiveness assumption may not be necessary in many practical applications such as assessing the effect of temperature on some electronic components, yielding to the so called non-destructive one-shot devices. Further, one-shot devices generally have large mean lifetime to failure, and so accelerated life tests (ALTs) must be performed for inference. The step-stress ALT shorten the lifetime of the products by increasing the stress level at which units are subjected to progressively at pre-specified times. Then, the non-destructive devices are tested at certain inspection times and surviving units can continue within the experiment providing extra information. Classical estimation methods based on the maximum likelihood estimator (MLE) enjoy suitable asymptotic properties but they lack of robustness. In this paper, we develop robust inferential methods for non-destructive one-shot devices based on the popular density power divergence (DPD) for estimating and testing under the step-stress model with Weibull lifetime distributions. We theoretically and empirically examine the asymptotic and robustness properties of the minimum DPD estimators and Wald-type test statistics based on them. Moreover, we develop robust estimators and confidence intervals for some important lifetime characteristics, namely reliability at certain mission times, distribution quantiles and mean lifetime of a device. Finally, we analyze the effect of temperature in three electronic components, solar lights, medium power silicon bipolar transistors and LED lights using real data arising from an step-stress ALT.
QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM

This article focuses on online kernel learning over a decentralized network. Each agent in the network receives online streaming data and collaboratively learns a globally optimal nonlinear prediction function in the reproducing kernel Hilbert space (RKHS). To overcome the curse of dimensionality issue in traditional online kernel learning, we utilize random feature (RF) mapping to convert the nonparametric kernel learning problem into a fixed-length parametric one in the RF space. We then propose a novel learning framework, named online decentralized kernel learning via linearized ADMM (ODKLA), to efficiently solve the online decentralized kernel learning problem. To enhance communication efficiency, we introduce quantization and censoring strategies in the communication stage, resulting in the quantized and communication-censored ODKLA (QC-ODKLA) algorithm. We theoretically prove that both ODKLA and QC-ODKLA can achieve the optimal sublinear regret O(√T) over T time slots. Through numerical experiments, we evaluate the learning effectiveness, communication efficiency, and computation efficiency of the proposed methods.
ZeroMesh: Zero-shot Single-view 3D Mesh Reconstruction

—Single-view 3D object reconstruction is a fundamental and challenging computer vision task that aims at recovering 3D shapes from single-view RGB images. Most existing deep learning based reconstruction methods are trained and evaluated on the same categories, and they cannot work well when handling objects from novel categories that are not seen during training. Focusing on this issue, this paper tackles Zero-shot Single-view 3D Mesh Reconstruction, to study the model generalization on unseen categories and encourage models to reconstruct objects literally. Speciﬁcally, we propose an end-to-end two-stage network, ZeroMesh, to break the category boundaries in reconstruction. Firstly, we factorize the complicated image-to-mesh mapping into two simpler mappings, i . e . , image-to-point mapping and point-to-mesh mapping, while the latter is mainly a geometric problem and less dependent on object categories. Secondly, we devise a local feature sampling strategy in 2D and 3D feature spaces to capture the local geometry shared across objects to enhance model generalization. Thirdly, apart from the traditional point-to-point supervision, we introduce a multi-view silhouette loss to supervise the surface generation process, which provides additional regularization and further relieves the overﬁtting problem. The experimental results show that our method signiﬁcantly outperforms the existing works on the ShapeNet and Pix3D under different scenarios and various metrics, especially for novel objects.
Open-world Contrastive Learning

Recent advance in contrastive learning has shown remarkable performance. However, the vast majority of approaches are limited to the closed-world setting. In this paper, we enrich the landscape of representation learning by tapping into an open-world setting, where unlabeled samples from novel classes can naturally emerge in the wild. To bridge the gap, we introduce a new learning framework, open-world contrastive learning (OpenCon). OpenCon tackles the challenges of learning compact representations for both known and novel classes , and facilitates novelty discovery along the way. We demonstrate the effectiveness of OpenCon on challenging benchmark datasets and establish competitive performance. On the ImageNet dataset, OpenCon signiﬁcantly outperforms the current best method by 11.9% and 7.4% on novel and overall classiﬁcation accuracy, respectively. We hope that our work will open up new doors for future work to tackle this important problem.
Benchmark Static API Call Datasets for Malware Family Classification

Nowadays, malware and malware incidents are increasing daily, even with various antivirus systems and malware detection or classification methodologies. Machine learning techniques have been the main focus of the security experts to detect malware and determine their families. Many static, dynamic, and hybrid techniques have been presented for that purpose. In this study, the static analysis technique has been applied to malware samples to extract API calls, which is one of the most used features in machine/deep learning models as it represents the behavior of malware samples. Since the rapid increase and continuous evolution of malware affect the detection capacity of antivirus scanners, recent and updated datasets of malicious software became necessary to overcome this drawback. This paper introduces two new datasets: One with 14,616 samples obtained and compiled from VirusShare and one with 9,795 samples from VirusSample. In addition, benchmark results based on static API calls of malware samples are presented using several machine and deep learning models on these datasets. We believe that these two datasets and benchmark results enable researchers to test and validate their methods and approaches in this field.
Fine-Grained Semantically Aligned Vision-Language Pre-Training

Large-scale vision-language pre-training has shown impressive advances in a wide range of downstream tasks. Existing methods mainly model the cross-modal alignment by the similarity of the global representations of images and texts, or advanced cross-modal attention upon image and text features. However, they fail to explicitly learn the fine-grained semantic alignment between visual regions and textual phrases, as only global image-text alignment information is available. In this paper, we introduce LOUPE, a fine-grained semantically aLigned visiOn-langUage PrE-training framework, which learns fine-grained semantic alignment from the novel perspective of game-theoretic interactions. To efficiently compute the game-theoretic interactions, we further propose an uncertainty-aware neural Shapley interaction learning module. Experiments show that LOUPE achieves state-of-the-art performance on a variety of vision-language tasks. Furthermore, without any object-level human annotations and fine-tuning, LOUPE achieves competitive performance on object detection and visual grounding. More importantly, LOUPE opens a new promising direction of learning fine-grained semantics from large-scale raw image-text pairs. The repository of this work is at https://github.com/YYJMJC/LOUPE.
DropKey

In this paper, we focus on analyzing and improving the dropout technique for self-attention layers of Vision Transformer, which is important while surprisingly ignored by prior works. In particular, we conduct researches on three core questions: First, what to drop in self-attention layers? Different from dropping attention weights in literature, we propose to move dropout operations forward ahead of attention matrix calculation and set the Key as the dropout unit, yielding a novel dropout-before-softmax scheme. We theoretically verify that this scheme helps keep both regularization and probability features of attention weights, alleviating the overfittings problem to specific patterns and enhancing the model to globally capture vital information; Second, how to schedule the drop ratio in consecutive layers? In contrast to exploit a constant drop ratio for all layers, we present a new decreasing schedule that gradually decreases the drop ratio along the stack of self-attention layers. We experimentally validate the proposed schedule can avoid overfittings in low-level features and missing in high-level semantics, thus improving the robustness and stableness of model training; Third, whether need to perform structured dropout operation as CNN? We attempt patch-based block-version of dropout operation and find that this useful trick for CNN is not essential for ViT. Given exploration on the above three questions, we present the novel DropKey method that regards Key as the drop unit and exploits decreasing schedule for drop ratio, improving ViTs in a general way. Comprehensive experiments demonstrate the effectiveness of DropKey for various ViT architectures, e.g. T2T and VOLO, as well as for various vision tasks, e.g., image classification, object detection, human-object interaction detection and human body shape recovery.
Low-Redundancy Codes for Correcting Multiple Short-Duplication and Edit Errors

Due to its higher data density, longevity, energy efficiency, and ease of generating copies, DNA is considered a promising technology for satisfying future storage needs. However, a diverse set of errors including deletions, insertions, duplications, and substitutions may arise in DNA at different stages of data storage and retrieval. The current paper constructs error-correcting codes for simultaneously correcting short (tandem) duplications and at most <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> edits, where a short duplication generates a copy of a substring with length <inline-formula> <tex-math notation="LaTeX">$\leq 3$ </tex-math></inline-formula> and inserts the copy following the original substring, and an edit is a substitution, deletion, or insertion. Compared to the state-of-the-art codes for duplications only, the proposed codes correct up to <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> edits (in addition to duplications) at the additional cost of roughly <inline-formula> <tex-math notation="LaTeX">$8p(\log _{q} n) (1+o(1))$ </tex-math></inline-formula> symbols of redundancy, thus achieving the same asymptotic rate, where <inline-formula> <tex-math notation="LaTeX">$q\ge 4$ </tex-math></inline-formula> is the alphabet size and <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> is a constant. Furthermore, the time complexities of both the encoding and decoding processes are polynomial when <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> is a constant with respect to the code length.
An applied mathematical view of meteorological modeling

The earth’s atmosphere is of overwhelming complexity due to a rich interplay between a large number of phenomena interacting on very diverse length and time scales. There are mathematical equation systems which, in principle, provide a comprehensive description of this system. Yet, exact or accurate approximate solutions to these equations covering the full range of complexities they allow for are not available. As a consequence, one of the central themes of theoretical meteorology is the development of simplified model equations that are amenable to analysis and computational approximate solution, while still faithfully representing an important subset of the observed phenomena.