Miracle-dz/UniFAR-FTUs huggingface dataset:
DARC: Differentiable ARchitecture Compression

In many learning situations, resources at inference time are significantly more constrained than resources at training time. This paper studies a general paradigm, called Differentiable ARchitecture Compression (DARC), that combines model compression and architecture search to learn models that are resource-efficient at inference time. Given a resource-intensive base architecture, DARC utilizes the training data to learn which sub-components can be replaced by cheaper alternatives. The high-level technique can be applied to any neural architecture, and we report experiments on state-of-the-art convolutional neural networks for image classification. For a WideResNet with $97.2\%$ accuracy on CIFAR-10, we improve single-sample inference speed by $2.28\times$ and memory footprint by $5.64\times$, with no accuracy loss. For a ResNet with $79.15\%$ Top1 accuracy on ImageNet, we improve batch inference speed by $1.29\times$ and memory footprint by $3.57\times$ with $1\%$ accuracy loss. We also give theoretical Rademacher complexity bounds in simplified cases, showing how DARC avoids overfitting despite over-parameterization.
Nik1: a Nim1‐like protein kinase of S. cerevisiae interacts with the Cdc28 complex and regulates cell cycle progression

Background: The eukaryotic cell cycle is driven by CDK‐cyclin complexes. A number of proteins interact either with CDK or the CDK complex to regulate CDK activity. A search for novel cell cycle regulators in the budding yeast Saccharomyces cerevisiae yielded multicopy suppressors of the cdc2‐L7mutation of the fission yeast, Schizosaccharomyces pombe.
HellaSwag: Can a Machine Really Finish Your Sentence?

Recent work by Zellers et al. (2018) introduced a new task of commonsense natural language inference: given an event description such as “A woman sits at a piano,” a machine must select the most likely followup: “She sets her fingers on the keys. ” With the introduction of BERT, near human-level performance was reached. Does this mean that machines can perform human level commonsense inference? In this paper, we show that commonsense inference still proves difficult for even state-of-the-art models, by presenting HellaSwag, a new challenge dataset. Though its questions are trivial for humans (>95% accuracy), state-of-the-art models struggle (<48%). We achieve this via Adversarial Filtering (AF), a data collection paradigm wherein a series of discriminators iteratively select an adversarial set of machine-generated wrong answers. AF proves to be surprisingly robust. The key insight is to scale up the length and complexity of the dataset examples towards a critical ‘Goldilocks’ zone wherein generated text is ridiculous to humans, yet often misclassified by state-of-the-art models. Our construction of HellaSwag, and its resulting difficulty, sheds light on the inner workings of deep pretrained models. More broadly, it suggests a new path forward for NLP research, in which benchmarks co-evolve with the evolving state-of-the-art in an adversarial way, so as to present ever-harder challenges.
CERTIFAI: A Common Framework to Provide Explanations and Analyse the Fairness and Robustness of Black-box Models

Concerns within the machine learning community and external pressures from regulators over the vulnerabilities of machine learning algorithms have spurred on the fields of explainability, robustness, and fairness. Often, issues in explainability, robustness, and fairness are confined to their specific sub-fields and few tools exist for model developers to use to simultaneously build their modeling pipelines in a transparent, accountable, and fair way. This can lead to a bottleneck on the model developer's side as they must juggle multiple methods to evaluate their algorithms. In this paper, we present a single framework for analyzing the robustness, fairness, and explainability of a classifier. The framework, which is based on the generation of counterfactual explanations through a custom genetic algorithm, is flexible, model-agnostic, and does not require access to model internals. The framework allows the user to calculate robustness and fairness scores for individual models and generate explanations for individual predictions which provide a means for actionable recourse (changes to an input to help get a desired outcome). This is the first time that a unified tool has been developed to address three key issues pertaining towards building a responsible artificial intelligence system.
Invasive ductal carcinoma: correlation of apparent diffusion coefficient value with pathological prognostic factors

The aim of this study was to correlate the apparent diffusion coefficient (ADC) value of invasive ductal carcinoma with pathological prognostic factors. A prospective study was conducted on 59 untreated female patients (mean age 46 years) with invasive ductal carcinoma. All patients were examined at 1.5 Tesla using dedicated bilateral breast coil. They underwent diffusion weighted MR imaging of the breast using a single shot echo planar imaging with a b‐factor of 200 and 400 sec/mm2. Apparent diffusion coefficient (ADC) maps were reconstructed. The ADC value of the breast cancer was calculated and correlated with the pathologic prognostic factors (tumor size, grade and lymph nodes). The mean ADC values of invasive ductal carcinoma were significantly lower in patients with high grade, large breast cancer as well as those with axillary lymph nodes metastasis in a statistically significant way (p = 0.001 for the three factors). The mean ADC value of invasive ductal carcinoma was correlated with histologic grade (r = −0.675, p = 0.001), tumor size (r = 0.504, p = 0.001) and showed lower ADC values with positive lymph node metastasis. Apparent diffusion coefficient value is correlated with pathological parameters of invasive ductal carcinoma. The lower ADC values are associated with higher histological grade, larger tumor size and presence of axillary lymph nodes. So, the ADC value can be considered as a promising prognostic parameter that may identify highly aggressive breast cancer. Copyright © 2010 John Wiley & Sons, Ltd.
Analysis and Design of Slitted Waveguides With Suppressed Slot-Mode Using Periodic FDTD

The characteristics of the slot- and perturbed TE10 modes in a slitted waveguide are studied for the purpose of suppressing the undesired slot-mode. The use of the finite difference time domain (FDTD) with periodic boundary conditions allows the extraction of the individual complex wavenumbers of the two modes, and thus the tracking of the modes as modifications are made to the structure. Two structures are presented, the first suppressing the propagation of the slot-mode, and the second suppressing the radiation of the slot-mode. Experimental validation is presented supporting the numerical results. It will be shown that a wideband suppression of the slot-mode propagation is achievable. However, there is a significant impact on the propagation characteristics of the desired perturbed TE10 mode. Conversely, the suppression of the slot-mode radiation is achievable with minimal impact on the perturbed TE10 mode.
Autologous Hematopoietic Stem Cells Are a Preferred Source to Generate Dendritic Cells for Immunotherapy in Multiple Myeloma Patients

In multiple myeloma (MM), dendritic cells (DCs), and their precursors are prone to malignant cell-mediated regulation of function leading to low efficacy of DC vaccine. DCs taken directly from MM patient's body or derived from monocytes are fewer in numbers and are also dysfunctional. Here, we investigated the functionality of Hematopoietic stem cell-derived DCs (SC-DCs) from MM patients. Mature-MM-SC-DCs showed all essential functions like antigen uptake, allogenic T cells simulation and migration comparable to those derived from healthy donor (HD) samples. A comparison of Mo-DCs and SC-DCs obtained from the same MM patients' samples revealed that the expression of IL-6 was higher in the precursors of Mo-DCs leading to their impaired migration. In addition, expression of CCR7 which is responsible for DCs migration was found to be lower in MM-Mo-DCs. The chromatin permissiveness as observed by H3K4me3 histone modification at the Ccr7 promoter in MM-Mo-DCs was significantly lower than those in MM-SC-DCs. Levels of Zbtb46- a hall mark DC transcription factor mRNA was also found to be reduced in MM-Mo-DCs. Cytotoxic T cells generated from MM-SC-DCs from autologous naïve T cells exhibited reduced antitumor activity because the T cells were exhausted. Blocking of CTLA-4 on autologous T cells could partially restore T cell proliferation and activation. Thus, a combination of MM-SC-DC vaccine and anti-CTLA-4 antibody may serve as a better candidate for immunotherapy of MM. This study has implications in increasing the efficacy of cancer immunotherapy in MM.
Relative Contribution of Estrogen Withdrawal and Gonadotropins Increase Secondary to Ovariectomy on Prostaglandin Generation in Mesenteric Microvessels

Recent studies have established that ovariectomy impairs endothelial function, partially by increasing vasoconstrictor prostaglandins generation. Because ovariectomy causes concomitant lack of estrogen and increase of gonadotropins (ie, LH and FSH), in this study we explored the relative role of estrogen and LH/FSH in modulating vasoconstrictor prostaglandins generation in mesenteric arteriolar bed of SHR. Endothelium-dependent relaxation to acetylcholine (ACh) and bradykinin (Bk) was markedly reduced in ovariectomized (OVX) compared with SHR in physiological estrus (OE). Estrogen replacement (OVX + E), but not the decrease in LH/FSH levels with leuprolide (OVX + Leu), corrected the altered vasorelaxation response in OVX. Treatment of mesenteries with diclofenac, prostaglandin-H synthase (PGHS) inhibitor, significantly enhanced the relaxing response in arteries from OVX and OVX + Leu, but not those from OE, indicating that a PGHS-derived vasoconstrictor has modified the endothelium-dependent response during estrogen but not LH/FSH deprivation. Confirming these data, in response to exogenous arachidonic acid, whereas arteries from OVX and OVX + Leu exhibited a marked and similar vasoconstrictor response, the arteries from OE and OVX + E rats exhibited a slight vasodilation. We also demonstrated by RT-PCR that ovariectomy significantly increased PGHS-2 but not PGHS-1 mRNA expression in comparison to OE. The PGHS-2 overexpression in OVX was corrected by estrogen replacement, but not by the reduction of LH/FSH levels. Altogether these data strongly support a role for hypoestrogenism rather than LH/FSH enhancement, associated with the removal of ovaries, in the increase of vasoconstrictor prostaglandins, possibly by a mechanism involving PGHS-2 overexpression.
Supernova Photometric Classification Pipelines Trained on Spectroscopically Classified Supernovae from the Pan-STARRS1 Medium-deep Survey

Photometric classification of supernovae (SNe) is imperative as recent and upcoming optical time-domain surveys, such as the Large Synoptic Survey Telescope (LSST), overwhelm the available resources for spectrosopic follow-up. Here we develop a range of light curve (LC) classification pipelines, trained on 513 spectroscopically classified SNe from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS): 357 Type Ia, 93 Type II, 25 Type IIn, 21 Type Ibc, and 17 Type I superluminous SNe (SLSNe). We present a new parametric analytical model that can accommodate a broad range of SN LC morphologies, including those with a plateau, and fit this model to data in four PS1 filters (gP1rP1iP1zP1). We test a number of feature extraction methods, data augmentation strategies, and machine-learning algorithms to predict the class of each SN. Our best pipelines result in ≈90% average accuracy, ≈70% average purity, and ≈80% average completeness for all SN classes, with the highest success rates for SNe Ia and SLSNe and the lowest for SNe Ibc. Despite the greater complexity of our classification scheme, the purity of our SN Ia classification, ≈95%, is on par with methods developed specifically for Type Ia versus non-Type Ia binary classification. As the first of its kind, this study serves as a guide to developing and training classification algorithms for a wide range of SN types with a purely empirical training set, particularly one that is similar in its characteristics to the expected LSST main survey strategy. Future work will implement this classification pipeline on ≈3000 PS1/MDS LCs that lack spectroscopic classification.
Global phylogenomics of multidrug-resistant Salmonella enterica serotype Kentucky ST198

Salmonella enterica serotype Kentucky can be a common causative agent of salmonellosis, usually associated with consumption of contaminated poultry. Antimicrobial resistance (AMR) to multiple drugs, including ciprofloxacin, is an emerging problem within this serotype. We used whole-genome sequencing (WGS) to investigate the phylogenetic structure and AMR content of 121 S. e nterica serotype Kentucky sequence type 198 isolates from five continents. Population structure was inferred using phylogenomic analysis and whole genomes were compared to investigate changes in gene content, with a focus on acquired AMR genes. Our analysis showed that multidrug-resistant (MDR) S. enterica serotype Kentucky isolates belonged to a single lineage, which we estimate emerged circa 1989 following the acquisition of the AMR-associated Salmonella genomic island (SGI) 1 (variant SGI1-K) conferring resistance to ampicillin, streptomycin, gentamicin, sulfamethoxazole and tetracycline. Phylogeographical analysis indicates this clone emerged in Egypt before disseminating into Northern, Southern and Western Africa, then to the Middle East, Asia and the European Union. The MDR clone has since accumulated various substitution mutations in the quinolone-resistance-determining regions (QRDRs) of DNA gyrase (gyrA) and DNA topoisomerase IV (parC), such that most strains carry three QRDR mutations which together confer resistance to ciprofloxacin. The majority of AMR genes in the S. e nterica serotype Kentucky genomes were carried either on plasmids or SGI structures. Remarkably, each genome of the MDR clone carried a different SGI1-K derivative structure; this variation could be attributed to IS26-mediated insertions and deletions, which appear to have hampered previous attempts to trace the clone’s evolution using sub-WGS resolution approaches. Several different AMR plasmids were also identified, encoding resistance to chloramphenicol, third-generation cephalosporins, carbapenems and/or azithromycin. These results indicate that most MDR S. e nterica serotype Kentucky circulating globally result from the clonal expansion of a single lineage that acquired chromosomal AMR genes 30 years ago, and has continued to diversify and accumulate additional resistances to last-line oral antimicrobials. This article contains data hosted by Microreact.
Innovative Programs for Improvement in Reading Through Cognitive Enhancement

Forty-five Grade 3 students from a reservation school in Western Canada were divided into two remedial groups and a no-risk control group. One remedial group was given a classroom-administered cognitive enhancement program (COGENT) throughout the school year. The second group received COGENT for the first half of the year followed by a pull-out cognitive-based reading enhancement program (PREP). Children were assessed across phonological awareness, rapid naming, reading, and cognitive ability at the beginning of the year, midterm, and at the end of the school year. MANOVA results showed a significant interaction for reading measures, with students receiving classroom intervention over the school year making the greatest gains. Results are discussed in terms of group, remediation program, and individual participant improvements.
MistForm: Adaptive Shape Changing Fog Screens

We present MistForm, a shape changing fog display that can support one or two users interacting with either 2D or 3D content. Mistform combines affordances from both shape changing interfaces and mid-air displays. For example, a concave display can maintain content in comfortable reach for a single user, while a convex shape can support several users engaged on individual tasks. MistForm also enables unique interaction possibilities by exploiting the synergies between shape changing interfaces and mid-air fog displays. For instance, moving the screen will affect the brightness and blurriness of the screen at specific locations around the display, creating spaces with similar (collaboration) or different visibility (personalized content). We describe the design of MistForm and analyse its inherent challenges, such as image distortion and uneven brightness on dynamic curved surfaces. We provide a machine learning approach to characterize the shape of the screen and a rendering algorithm to remove aberrations. We finally explore novel interactive possibilities and reflect on their potential and limitations.
An experimental study on the facilitative effects of tussock structure among wetland plants

Although most investigations of clonal plants have focused on negative aspects of interactions with neighbors, some studies have shown positive effects of clonal plants on other species, especially clonal plants with a compact, or phalanx, growth habit. For example, several plant species have been observed to grow directly upon tussocks of the freshwater rush Juncus effusus L. , and the phenology of those species appeared to correlate with seasonal dynamics of the Juncus canopy. The present experimental study was conducted to enable distinction among four aspects of the Juncus tussock microhabitat: 1) collapse of the Juncus canopy, typical of summer conditions, 2) springtime pre-collapse erect architecture of the Juncus canopy, 3) availability of a living tussock platform in the absence of a canopy, and 4) simple physical provision of an elevated, organic surface for colonization in the form of artificial rooting platforms. Several species responded to particular features of the Juncus effusus canopy; three of those species, Boehmeria cylindrica, Leersia oryzoides, and Lonicera japonica, were identified as facilitated species in previous research on facilitation by Juncus. Particular features of the Juncus tussocks that appeared to correlate with increased cover of colonizing species were Juncus canopy height, tussock basal diameter, and relative water depth atop the tussock. Thus, results of this experimental study support conclusions from prior work on facilitation by Juncus effusus and other tussock-forming species regarding the importance of tussock physical structure in providing sites for plant colonization in freshwater wetlands.
Predicting Model Failure using Saliency Maps in Autonomous Driving Systems

While machine learning systems show high success rate in many complex tasks, research shows they can also fail in very unexpected situations. Rise of machine learning products in safety-critical industries cause an increase in attention in evaluating model robustness and estimating failure probability in machine learning systems. In this work, we propose a design to train a student model -- a failure predictor -- to predict the main model's error for input instances based on their saliency map. We implement and review the preliminary results of our failure predictor model on an autonomous vehicle steering control system as an example of safety-critical applications.
Independent Vector Analysis with More Microphones Than Sources

We extend frequency-domain blind source separation based on independent vector analysis to the case where there are more microphones than sources. The signal is modelled as non-Gaussian sources in a Gaussian background. The proposed algorithm is based on a parametrization of the demixing matrix decreasing the number of parameters to estimate. Furthermore, orthogonal constraints between the signal and background subspaces are imposed to regularize the separation. The problem can then be posed as a constrained likelihood maximization. We propose efficient alternating updates guaranteed to converge to a stationary point of the cost function. The performance of the algorithm is assessed on simulated signals. We find that the separation performance is on par with that of the conventional determined algorithm at a fraction of the computational cost.
The Pathophysiology and Treatment of Hypertension in Patients With Cushing's Syndrome

When hypertension, a pathology that is frequently found in the general population, presents in a young patient, secondary causes such as Cushing's syndrome (CS), a rare disease characterized by long-term elevated cortisol levels, should be considered. Present in ~80% of CS patients independently of their age and sex, hypertension is one of the pathology's most prevalent, alarming features. Its severity is principally associated with the duration and intensity of elevated cortisol levels. Prompt diagnosis and rapid initiation of treatment are important for reducing/delaying the consequences of hypercortisolism. Glucocorticoid excess leads to hypertension via a variety of mechanisms including mineralocorticoid mimetic activity, alterations in peripheral and renovascular resistance, and vascular remodeling. As hypertension in CS patients is caused by cortisol excess, treating the underlying pathology generally contributes to reducing blood pressure (BP) levels, although hypertension tends to persist in approximately 30% of cured patients. Surgical removal of the pituitary tumor remains the first-line treatment for both adrenocorticotropin hormone (ACTH) dependent and independent forms of the syndrome. In light of the fact that surgery is not always successful in curing the underlying disease, it is essential that other treatments be considered and prescribed as needed. This article discusses the mechanisms involved in the pathogenesis of CS and the pros and the cons of the various antihypertensive agents that are presently available to treat these patients.
Self-Supervised Similarity Learning for Digital Pathology

Using features extracted from networks pretrained on ImageNet is a common practice in applications of deep learning for digital pathology. However it presents the downside of missing domain specific image information. In digital pathology, supervised training data is expensive and difficult to collect. We propose a self-supervised method for feature extraction by similarity learning on whole slide images (WSI) that is simple to implement and allows creation of robust and compact image descriptors. We train a siamese network, exploiting image spatial continuity and assuming spatially adjacent tiles in the image are more similar to each other than distant tiles. Our network outputs feature vectors of length 128, which allows dramatically lower memory storage and faster processing than networks pretrained on ImageNet. We apply the method on digital pathology WSIs from the Camelyon16 train set and assess and compare our method by measuring image retrieval of tumor tiles and descriptor pair distance ratio for distant/near tiles in the Camelyon16 test set. We show that our method yields better retrieval task results than existing ImageNet based and generic self-supervised feature extraction methods. To the best of our knowledge, this is also the first published method for self-supervised learning tailored for digital pathology.