Miracle-dz/UniFAR-FTUs huggingface dataset:
Ranked List Loss for Deep Metric Learning

The objective of deep metric learning (DML) is to learn embeddings that can capture semantic similarity information among data points. Existing pairwise or tripletwise loss functions used in DML are known to suffer from slow convergence due to a large proportion of trivial pairs or triplets as the model improves. To improve this, rankingmotivated structured losses are proposed recently to incorporate multiple examples and exploit the structured information among them. They converge faster and achieve state-of-the-art performance. In this work, we present two limitations of existing ranking-motivated structured losses and propose a novel ranked list loss to solve both of them. First, given a query, only a fraction of data points is incorporated to build the similarity structure. Consequently, some useful examples are ignored and the structure is less informative. To address this, we propose to build a setbased similarity structure by exploiting all instances in the gallery. The samples are split into a positive set and a negative set. Our objective is to make the query closer to the positive set than to the negative set by a margin. Second, previous methods aim to pull positive pairs as close as possible in the embedding space. As a result, the intraclass data distribution might be dropped. In contrast, we propose to learn a hypersphere for each class in order to preserve the similarity structure inside it. Our extensive experiments show that the proposed method achieves state-of-the-art performance on three widely used benchmarks.
Diversification of Transcriptional Regulation Determines Subfunctionalization of Paralogous Branched Chain Aminotransferases in the Yeast Saccharomyces cerevisiae

Saccharomyces cerevisiae harbors BAT1 and BAT2 paralogous genes that encode branched chain aminotransferases and have opposed expression profiles and physiological roles . Accordingly, in primary nitrogen sources such as glutamine, BAT1 expression is induced, supporting Bat1-dependent valine–isoleucine–leucine (VIL) biosynthesis, while BAT2 expression is repressed. Conversely, in the presence of VIL as the sole nitrogen source, BAT1 expression is hindered while that of BAT2 is activated, resulting in Bat2-dependent VIL catabolism. The presented results confirm that BAT1 expression is determined by transcriptional activation through the action of the Leu3–α-isopropylmalate (α-IPM) active isoform, and uncovers the existence of a novel α-IPM biosynthetic pathway operating in a put3Δ mutant grown on VIL, through Bat2-Leu2-Leu1 consecutive action. The classic α-IPM biosynthetic route operates in glutamine through the action of the leucine-sensitive α-IPM synthases. The presented results also show that BAT2 repression in glutamine can be alleviated in a ure2Δ mutant or through Gcn4-dependent transcriptional activation. Thus, when S. cerevisiae is grown on glutamine, VIL biosynthesis is predominant and is preferentially achieved through BAT1; while on VIL as the sole nitrogen source, catabolism prevails and is mainly afforded by BAT2.
Complement Random Forest

Random Forest is a popular decision forest building algorithm which focuses on generating diverse decision trees as the base classiﬁers. For high dimensional data sets, Random Forest generally excels in generating diverse decision trees at the cost of less accurate individual decision trees. To achieve higher prediction accuracy, a decision forest needs both accurate and diverse decision trees as the base classiﬁers. In this paper we propose a novel decision forest algorithm called Complement Random Forest that aims to generate accurate yet diverse decision trees when applied on high dimensional data sets. We conduct an elaborate experimental analysis on seven publicly available data sets from UCI Machine Learning Repository. The experimental results indicate the eﬀective-ness of our proposed technique.
Is Deeper Better only when Shallow is Good?

Understanding the power of depth in feed-forward neural networks is an ongoing challenge in the field of deep learning theory. While current works account for the importance of depth for the expressive power of neural-networks, it remains an open question whether these benefits are exploited during a gradient-based optimization process. In this work we explore the relation between expressivity properties of deep networks and the ability to train them efficiently using gradient-based algorithms. We give a depth separation argument for distributions with fractal structure, showing that they can be expressed efficiently by deep networks, but not with shallow ones. These distributions have a natural coarse-to-fine structure, and we show that the balance between the coarse and fine details has a crucial effect on whether the optimization process is likely to succeed. We prove that when the distribution is concentrated on the fine details, gradient-based algorithms are likely to fail. Using this result we prove that, at least in some distributions, the success of learning deep networks depends on whether the distribution can be well approximated by shallower networks, and we conjecture that this property holds in general.
ShiftsReduce

Racetrack memories (RMs) have significantly evolved since their conception in 2008, making them a serious contender in the field of emerging memory technologies. Despite key technological advancements, the access latency and energy consumption of an RM-based system are still highly influenced by the number of shift operations. These operations are required to move bits to the right positions in the racetracks. This article presents data-placement techniques for RMs that maximize the likelihood that consecutive references access nearby memory locations at runtime, thereby minimizing the number of shifts. We present an integer linear programming (ILP) formulation for optimal data placement in RMs, and we revisit existing offset assignment heuristics, originally proposed for random-access memories. We introduce a novel heuristic tailored to a realistic RM and combine it with a genetic search to further improve the solution. We show a reduction in the number of shifts of up to 52.5%, outperforming the state of the art by up to 16.1%.
A CRISPR Tagging-Based Screen Reveals Localized Players in Wnt-Directed Asymmetric Cell Division

Oriented cell divisions are critical to establish and maintain cell fates and tissue organization. Diverse extracellular and intracellular cues have been shown to provide spatial information for mitotic spindle positioning; however, the molecular mechanisms by which extracellular signals communicate with cells to direct mitotic spindle positioning are largely unknown. In animal cells, oriented cell divisions are often achieved by the localization of force-generating motor protein complexes to discrete cortical domains. Disrupting either these force-generating complexes or proteins that globally affect microtubule stability results in defects in mitotic positioning, irrespective of whether these proteins function as spatial cues for spindle orientation. This poses a challenge to traditional genetic dissection of this process. Therefore, as an alternative strategy to identify key proteins that act downstream of intercellular signaling, we screened the localization of many candidate proteins by inserting fluorescent tags directly into endogenous gene loci, without overexpressing the proteins. We tagged 23 candidate proteins in Caenorhabditis elegans and examined each protein’s localization in a well-characterized, oriented cell division in the four-cell-stage embryo. We used cell manipulations and genetic experiments to determine which cells harbor key localized proteins and which signals direct these localizations in vivo. We found that Dishevelled and adenomatous polyposis coli homologs are polarized during this oriented cell division in response to a Wnt signal, but two proteins typically associated with mitotic spindle positioning, homologs of NuMA and Dynein, were not detectably polarized. These results suggest an unexpected mechanism for mitotic spindle positioning in this system, they pinpoint key proteins of interest, and they highlight the utility of a screening approach based on analyzing the localization of endogenously tagged proteins.
Neural language models as psycholinguistic subjects: Representations of syntactic state

We investigate the extent to which the behavior of neural network language models reflects incremental representations of syntactic state. To do so, we employ experimental methodologies which were originally developed in the field of psycholinguistics to study syntactic representation in the human mind. We examine neural network model behavior on sets of artificial sentences containing a variety of syntactically complex structures. These sentences not only test whether the networks have a representation of syntactic state, they also reveal the specific lexical cues that networks use to update these states. We test four models: two publicly available LSTM sequence models of English (Jozefowicz et al., 2016; Gulordava et al., 2018) trained on large datasets; an RNN Grammar (Dyer et al., 2016) trained on a small, parsed dataset; and an LSTM trained on the same small corpus as the RNNG. We find evidence for basic syntactic state representations in all models, but only the models trained on large datasets are sensitive to subtle lexical cues signaling changes in syntactic state.
Automatic cough detection for portable spirometry system trained on large database

In this work, we give a short introduction on cough detection efforts that were undertaken during the last decade and we describe the solution for automatic cough detection developed for the AioCare portable spirometry system. As the system is intended to be used in a large variety of environments and different patients, we train the algorithm using the large database of spirometry curves which is the NHANES database by the American National Center for Health Statistics. Using such a massive dataset is a novelty in the field. We apply few data preprocessing steps, derive specific features and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance. The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1÷91.2%, 81.8÷83.8% and 95.0÷95.9% for the test set, respectively. The ANN solution was selected as the final classifier. Classification methodology developed in this study is robust for detecting cough events during spirometry measurements. We also show that it is universal and transferable between different systems as the performance on the NHANES and the AioCare test sets is similar. As far as we know, the solution presented in this work is the first description of the automatic cough detection algorithm based totally on the air flow signals and the first cough detection implemented in the commercial spirometry system that is to be published.
Cervical Cancer Screening: Assessment of Perception and Utilization of Services among Health Workers in Low Resource Setting

Background Cervical cancer is a preventable disease and the most common cancer among women in Nigeria. Objectives To appraise the perception and utilization of cervical cancer screening services among female nurses in Federal Teaching hospital, Abakaliki. Materials and Methods A cross-sectional study was done using semi-structured questionnaires on 408 consenting female nurses. Data was represented using simple percentages, charts, and Chi-square. Results Of the 408 questionnaires shared, 388 were correctly and completely filled and analyzed. The respondents in this study showed good knowledge of cervical cancer as all (388) were aware that cervical cancer is a preventable disease of public health concern. Majority of them, 179 (46.1%) were between 21 and 30 years of age. The most common symptom of cervical cancer identified was postcoital bleeding (57%). Nursing training was the most common (73.5%) source of cervical cancer information. Utilization of cervical cancer screening was poor in this study as only 20.6% of the respondents had ever undergone screening. The most common reason for nonscreening was that they have not thought of it (28.4%). Conclusion Despite the high level of awareness of cervical cancer screening, utilization remains low. There is, therefore, the need for cervical cancer education for the nurses to help improve utilization.
Glycosylation Profile of the Transferrin Receptor in Gestational Iron Deficiency and Early-Onset Severe Preeclampsia

Objective To examine the expression of hypoxia-inducible factor-1α (HIF-1α), TfR1, and TfR1-attached terminal monosaccharides in placentas of women with IDAP and severe preeclampsia. Methods TfR1 and HIF-1α were detected by western blot. Immunoadsorption of TfR1 was performed to characterize the terminal monosaccharides by specific lectin binding. Results There was no difference in the expression of TfR1 and HIF-1α between groups. Lectin blot analysis pointed out an overexpression of galactose β1-4 N-acetylglucosamine (Gal-GlcNAc) and mannose in severe preeclampsia. Conclusion The increase in Gal-GlcNAc may be due to the increased presence of antennary structures and the mannose glycans of TfR1 may indicate the presence of misfolded or incomplete proteins. These findings may be associated with the low expression of placental TfR1 in women with preeclampsia.
Learning Heuristics over Large Graphs via Deep Reinforcement Learning

There has been an increased interest in discovering heuristics for combinatorial problems on graphs through machine learning. While existing techniques have primarily focused on obtaining high-quality solutions, scalability to billion-sized graphs has not been adequately addressed. In addition, the impact of budget-constraint, which is necessary for many practical scenarios, remains to be studied. In this paper, we propose a framework called GCOMB to bridge these gaps. GCOMB trains a Graph Convolutional Network (GCN) using a novel probabilistic greedy mechanism to predict the quality of a node. To further facilitate the combinatorial nature of the problem, GCOMB utilizes a Q-learning framework, which is made efficient through importance sampling. We perform extensive experiments on real graphs to benchmark the efficiency and efficacy of GCOMB. Our results establish that GCOMB is 100 times faster and marginally better in quality than state-of-the-art algorithms for learning combinatorial algorithms. Additionally, a case-study on the practical combinatorial problem of Influence Maximization (IM) shows GCOMB is 150 times faster than the specialized IM algorithm IMM with similar quality.
Fate map of Medicago truncatula root nodules

Legume root nodules are induced by N-fixing rhizobium bacteria that are hosted in an intracellular manner. These nodules are formed by reprogramming differentiated root cells. The model legume Medicago truncatula forms indeterminate nodules with a meristem at their apex. This organ grows by the activity of the meristem that adds cells to the different nodule tissues. In Medicago sativa it has been shown that the nodule meristem is derived from the root middle cortex. During nodule initiation, inner cortical cells and pericycle cells are also mitotically activated. However, whether and how these cells contribute to the mature nodule has not been studied. Here, we produce a nodule fate map that precisely describes the origin of the different nodule tissues based on sequential longitudinal sections and on the use of marker genes that allow the distinction of cells originating from different root tissues. We show that nodule meristem originates from the third cortical layer, while several cell layers of the base of the nodule are directly formed from cells of the inner cortical layers, root endodermis and pericycle. The latter two differentiate into the uninfected tissues that are located at the base of the mature nodule, whereas the cells derived from the inner cortical cell layers form about eight cell layers of infected cells. This nodule fate map has then been used to re-analyse several mutant nodule phenotypes. This showed, among other things, that intracellular release of rhizobia in primordium cells and meristem daughter cells are regulated in a different manner.
How Machine (Deep) Learning Helps Us Understand Human Learning: the Value of Big Ideas

I use simulation of two multilayer neural networks to gain intuition into the determinants of human learning. The first network, the teacher, is trained to achieve a high accuracy in handwritten digit recognition. The second network, the student, learns to reproduce the output of the first network. I show that learning from the teacher is more effective than learning from the data under the appropriate degree of regularization. Regularization allows the teacher to distinguish the trends and to deliver "big ideas" to the student. I also model other learning situations such as expert and novice teachers, high- and low-ability students and biased learning experience due to, e.g., poverty and trauma. The results from computer simulation accord remarkably well with finding of the modern psychological literature. The code is written in MATLAB and will be publicly available from the author's web page.
Inductive Transfer for Neural Architecture Optimization

The recent advent of automated neural network architecture search led to several methods that outperform state-of-the-art human-designed architectures. However, these approaches are computationally expensive, in extreme cases consuming GPU years. We propose two novel methods which aim to expedite this optimization problem by transferring knowledge acquired from previous tasks to new ones. First, we propose a novel neural architecture selection method which employs this knowledge to identify strong and weak characteristics of neural architectures across datasets. Thus, these characteristics do not need to be rediscovered in every search, a strong weakness of current state-of-the-art searches. Second, we propose a method for learning curve extrapolation to determine if a training process can be terminated early. In contrast to existing work, we propose to learn from learning curves of architectures trained on other datasets to improve the prediction accuracy for novel datasets. On five different image classification benchmarks, we empirically demonstrate that both of our orthogonal contributions independently lead to an acceleration, without any significant loss in accuracy.
Learning From Synthetic Data for Crowd Counting in the Wild

Recently, counting the number of people for crowd scenes is a hot topic because of its widespread applications (e.g. video surveillance, public security). It is a difficult task in the wild: changeable environment, large-range number of people cause the current methods can not work well. In addition, due to the scarce data, many methods suffer from over-fitting to a different extent. To remedy the above two problems, firstly, we develop a data collector and labeler, which can generate the synthetic crowd scenes and simultaneously annotate them without any manpower. Based on it, we build a large-scale, diverse synthetic dataset. Secondly, we propose two schemes that exploit the synthetic data to boost the performance of crowd counting in the wild: 1) pretrain a crowd counter on the synthetic data, then finetune it using the real data, which significantly prompts the model's performance on real data; 2) propose a crowd counting method via domain adaptation, which can free humans from heavy data annotations. Extensive experiments show that the first method achieves the state-of-the-art performance on four real datasets, and the second outperforms our baselines. The dataset and source code are available at https://gjy3035.github. io/GCC-CL/.
Hepatitis B and C Viral Infection: Prevalence, Knowledge, Attitude, Practice, and Occupational Exposure among Healthcare Workers of Jimma University Medical Center, Southwest Ethiopia

Background Blood-borne infections have been recognized as an occupational hazard for nearly 50 years. Current treatment for hepatitis B virus (HBV) is very expensive for individuals in developing countries and cannot clear infection after it progresses to the chronic stage. Thus, early screenings of people who are at higher risk like healthcare workers and vaccination and awareness creation on standard precautions (SP) to prevent transmission are mandatory. This study determined seroprevalence of HBV and hepatitis C virus (HCV) among healthcare workers of Jimma University Medical Center (JUMC). Methods An institution based cross-sectional study was conducted from Nov 2015 to Jan 2016. The lottery method was used to select 240 healthcare workers. Data were collected by a self-administered questionnaire. Five to ten milliliters of whole venous blood was collected from each participant. The blood samples were analyzed (tested) for hepatitis B surface antigen (HBsAg) and anti-HCV antibody using automated Enzyme-Linked Immunosorbent Assay (ELISA). Data were entered into EpiData 3.1 and analyzed by SPSS 23. Results The positivity of HBsAg was 2.5% (6/240; 95% CI: 0.52-4.48%) and that of anti-HCV antibody was 0.42% (1/240; 95% CI: 0.0-1.23%). Most participants had good knowledge of HBV (73.9%), HCV (60.9%), and SP (82.2%) and positive attitude towards SP (88.7%), but only 42.6% had a good practice of SP. More than half (60%) and nearly half (43%) had a history of ever exposure and exposure in the last one year before the survey, respectively. Females were at lower risk of both having ever exposure (95% CI: (0.241, 0.777)) and exposure in the last one year before the survey (95% CI: (0.297, 0.933)) compared to males. Conclusion The prevalence of HBV was intermediate according to the endemicity classification by WHO. The practice of SP was poor in most participants and, thus, occupational exposure was high. Therefore, regular screening and vaccination of healthcare workers, regular provision of basic or refresher training and availing logistics, and regular motivation of healthcare workers on the practice of standard precautions are recommended.
Understanding Sparse JL for Feature Hashing

Feature hashing and other random projection schemes are commonly used to reduce the dimensionality of feature vectors. The goal is to efficiently project a high-dimensional feature vector living in $\mathbb{R}^n$ into a much lower-dimensional space $\mathbb{R}^m$, while approximately preserving Euclidean norm. These schemes can be constructed using sparse random projections, for example using a sparse Johnson-Lindenstrauss (JL) transform. A line of work introduced by Weinberger et. al (ICML '09) analyzes the accuracy of sparse JL with sparsity 1 on feature vectors with small $\ell_\infty$-to-$\ell_2$ norm ratio. Recently, Freksen, Kamma, and Larsen (NeurIPS '18) closed this line of work by proving a tight tradeoff between $\ell_\infty$-to-$\ell_2$ norm ratio and accuracy for sparse JL with sparsity $1$. In this paper, we demonstrate the benefits of using sparsity $s$ greater than $1$ in sparse JL on feature vectors. Our main result is a tight tradeoff between $\ell_\infty$-to-$\ell_2$ norm ratio and accuracy for a general sparsity $s$, that significantly generalizes the result of Freksen et. al. Our result theoretically demonstrates that sparse JL with $s > 1$ can have significantly better norm-preservation properties on feature vectors than sparse JL with $s = 1$; we also empirically demonstrate this finding.