Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual Swap Disentangling

Learning interpretable disentangled representations is a crucial yet challenging task. In this paper, we propose a weakly semi-supervised method, termed as Dual Swap Disentangling (DSD), for disentangling using both labeled and unlabeled data. Unlike conventional weakly supervised methods that rely on full annotations on the group of samples, we require only limited annotations on paired samples that indicate their shared attribute like the color. Our model takes the form of a dual autoencoder structure. To achieve disentangling using the labeled pairs, we follow a "encoding-swap-decoding" process, where we first swap the parts of their encodings corresponding to the shared attribute and then decode the obtained hybrid codes to reconstruct the original input pairs. For unlabeled pairs, we follow the "encoding-swap-decoding" process twice on designated encoding parts and enforce the final outputs to approximate the input pairs. By isolating parts of the encoding and swapping them back and forth, we impose the dimension-wise modularity and portability of the encodings of the unlabeled samples, which implicitly encourages disentangling under the guidance of labeled pairs. This dual swap mechanism, tailored for semi-supervised setting, turns out to be very effective. Experiments on image datasets from a wide domain show that our model yields state-of-the-art disentangling performances.
Ab-initio study on the possible doping strategies for MoS$_2$ monolayers

Density functional theory is used to systematically study the electronic and magnetic properties of doped MoS$_2$ monolayers, where the dopants are incorporated both via S/Mo substitution or as adsorbates. Among the possible substitutional dopants at the Mo site, Nb is identified as suitable p-type dopant, while Re is the donor with the lowest activation energy. When dopants are simply adsorbed on a monolayer we find that alkali metals shift the Fermi energy into the MoS$_2$ conduction band, making the system n-type. Finally, the adsorption of charged molecules is considered, mimicking an ionic liquid environment. We find that molecules adsorption can lead to both n- and p-type conductivity, depending on the charge polarity of the adsorbed species.
The Field of Emergence in the Aspects of Communicative Situation

Communicative situation is a basis, which designates potential models of “constructed forms”. It is within the field of emergence models of text, that can be potentially prognosticated in a certain communicative situation, are designated. Every text can be assumed as conceptual system structured on the base of certain communicative situation. However in the process of “structuring” of a certain model of “conceptual system” consciousness of a recipient is able act only within the border of the field of emergence for going out of this border indicates misunderstanding of the communicative situation. Citation: Albekov NN (2016) The Field of Emergence in the Aspects of Communicative Situation. J Neurol Neurol Disord 3(1): 102. doi: 10.15744/2454-4981.3. 102
Understanding Psycholinguistic Behavior of Predominant Drunk Texters in Social Media

In the last decade, social media has evolved as one of the leading platform to create, share, or exchange information; it is commonly used as a way for individuals to maintain social connections. In this online digital world, people use to post texts or pictures to express their views socially and create user-user engagement through discussions and conversations. Thus, social media has established itself to bear signals relating to human behavior. One can easily design user characteristic network by scraping through someone’s social media profiles. In this paper, we investigate the potential of social media in characterizing and understanding predominant drunk texters from the perspective of their social, psychological and linguistic behavior as evident from the content generated by them. Our research aims to analyze the behavior of drunk texters on social media and to contrast this with non-drunk texters. We use Twitter social media to obtain the set of drunk texters and non-drunk texters and show that we can classify users into these two respective sets using various psycholinguistic features with an overall average accuracy of 96.78% with very high precision and recall. Note that such an automatic classification can have far-reaching impact – (i) on health research related to addiction prevention and control, and (ii) in eliminating abusive and vulgar contents from Twitter, borne by the tweets of drunk texters.
Deep Neural Networks for Modeling Visual Perceptual Learning

Understanding visual perceptual learning (VPL) has become increasingly more challenging as new phenomena are discovered with novel stimuli and training paradigms. Although existing models aid our knowledge of critical aspects of VPL, the connections shown by these models between behavioral learning and plasticity across different brain areas are typically superficial. Most models explain VPL as readout from simple perceptual representations to decision areas and are not easily adaptable to explain new findings. Here, we show that a well -known instance of deep neural network (DNN), whereas not designed specifically for VPL, provides a computational model of VPL with enough complexity to be studied at many levels of analyses. After learning a Gabor orientation discrimination task, the DNN model reproduced key behavioral results, including increasing specificity with higher task precision, and also suggested that learning precise discriminations could transfer asymmetrically to coarse discriminations when the stimulus conditions varied. Consistent with the behavioral findings, the distribution of plasticity moved toward lower layers when task precision increased and this distribution was also modulated by tasks with different stimulus types. Furthermore, learning in the network units demonstrated close resemblance to extant electrophysiological recordings in monkey visual areas. Altogether, the DNN fulfilled predictions of existing theories regarding specificity and plasticity and reproduced findings of tuning changes in neurons of the primate visual areas. Although the comparisons were mostly qualitative, the DNN provides a new method of studying VPL, can serve as a test bed for theories, and assists in generating predictions for physiological investigations. SIGNIFICANCE STATEMENT Visual perceptual learning (VPL) has been found to cause changes at multiple stages of the visual hierarchy. We found that training a deep neural network (DNN) on an orientation discrimination task produced behavioral and physiological patterns similar to those found in human and monkey experiments. Unlike existing VPL models, the DNN was pre-trained on natural images to reach high performance in object recognition, but was not designed specifically for VPL; however, it fulfilled predictions of existing theories regarding specificity and plasticity and reproduced findings of tuning changes in neurons of the primate visual areas. When used with care, this unbiased and deep-hierarchical model can provide new ways of studying VPL from behavior to physiology.
Task-specific comparison of 3D image registration methods

We present a new class of approaches for rigid-body registration and their evaluation in studying Multiple Sclerosis via multi protocol MRI. Two pairs of rigid-body registration algorithms were implemented, using cross- correlation and mutual information, operating on original gray-level images and on the intermediate images resulting from our new scale-based method. In the scale image, every voxel has the local scale value assigned to it, defined as the radius of the largest sphere centered at the voxel with homogeneous intensities. 3D data of the head were acquired from 10 MS patients using 6 MRI protocols. Images in some of the protocols have been acquired in registration. The co-registered pairs were used as ground truth. Accuracy and consistency of the 4 registration methods were measured within and between protocols for known amounts of misregistrations. Our analysis indicates that there is no best method. For medium and large misregistration, methods using mutual information, for small misregistration, and for the consistency tests, correlation methods using the original gray-level images give the best results. We have previously demonstrated the use of local scale information in fuzzy connectedness segmentation and image filtering. Scale may also have considerable potential for image registration as suggested by this work.
Acetylcholinesterase from Puntius javanicus for the detection of carbamate and organophosphate

A new alternative local source of acetylcholinesterase (AChE) has been found to be sensitive towards several insecticides. AChE was purified from the brain tissue of Puntius javanicususing affinity chromatography (procainamide hydrochloride coupled with sephacryl 6B) with the total recovery of 40% at 8.48 purification fold. All carbamate (CB) insecticides tested at the concentration of 1 mg/L were capable of lowering AChE activity to less than 30%; Bendiocarp (18.80%), carbaryl (5.96%,), carbofuran (6.12%), methomyl (13.91%) and propoxur (15.851%). The P. javanicus AChE was found to be unaffected by several activated organophosphates (OP) such as acephate and dimethoate, while trichlorfon slightly inhibited the enzyme activity (p<0.05). Chlorpyrifos, diazinon, malathion and parathion lowered AChE activity to 43.02, 40.97, 37.11 and 46.72%, respectively. Pesticides that inhibit AChE activities more than 50% were further tested in different concentrations to determine the half maximal inhibitory concentration (IC50). Carbofuran and carbaryl showed the lowest IC50 value at 0.035 and 0.031 mg/L, respectively, as both showed no significant differences (p <0.05), followed by bendiocarp, propoxur, methomyl, malathion, diazinon, parathion and chlorpyrifos at 0.045, 0.076, 0.090, 0.063, 0.103, 0.151 and 0.202 mg/L, respectively. Based on these results, the sensitivity of AChE from brain P. javanicus brain tissue shows promise as an alternative biosensor for the detection of insecticides contamination.
Locally adaptive reconstruction of lost low-frequency coefficients in wavelet coded images

In packet switched networks such as the Internet, packets may get lost during transmission due to, e.g., network congestion. This leads to a quality degradation of the original signal. As video communication is a bandwidth consuming application, the original data are first compressed. This compression step increases the impact of information loss even more. In wavelet based image and video coding, the low frequency data is the most important. Loss of low frequency coefficients results in annoying black holes in the received images and video. This effect can be countered by post processing error concealment: a lost coefficient is estimated from its neighboring coefficients. In this paper we present a locally adaptive interpolation method for the lost low frequency coefficients. For each lost low frequency coefficient, we estimate the optimal interpolation direction (horizontal or vertical) using novel error measures. In this way, we preserve the edges in the reconstructed image much better. Compared to older techniques of similar complexity, our scheme reconstructs images with the same or better quality. This is reflected in the visual as well as in the numerical results: there is an increase of up to 4.4 dB compared to bilinear concealment. The proposed scheme is fast and simple, which makes it suitable for real-time applications.
Subcutaneous Lobular Capillary Hemangioma Presenting as a Facial Mass

Lobular capillary hemangioma is a benign lesion of the skin and mucous membranes. Subcutaneous lobular capillary hemangioma presents as a deeper nodule. Lack of the characteristic surface changes of this subtype of lobular capillary hemangioma makes the clinical diagnosis challenging. We describe clinical, radiologic, and histological features of a subcutaneous lobular capillary hemangioma tissue presenting as a facial mass in a 12-year-old male. The mass was a firm, nontender, immobile, subcutaneous nodule, with no color change of the overlying skin. CT imaging documented a hyperdense and nonlipomatous mass involving soft tissue of the left lateral nasal wall. An excisional biopsy was performed. Histologic evaluation showed subcutaneous lobular capillary hemangioma. Subcutaneous lobular capillary hemangioma, although uncommon, should be considered in the differential diagnosis of lateral nasal wall mass in children.
Scaling to Billion-plus Word Corpora

Most phenomena in natural languages are distributed in accordance with Zipf's law, so many words, phrases and other items occur rarely and we need very large corpora to provide evidence about them. Previous work shows that it is possible to create very large (multi-billion word) corpora from the web. The usability of such corpora is often limited by duplicate contents and a lack of efficient query tools. This paper describes BiWeC, a Big Web Corpus of English texts currently comprising 5.5b words fully processed, and with a target size of 20b. We present a method for detecting near-duplicate text documents in multi-billion-word text collections and describe how one corpus query tool, the Sketch Engine, has been re-engineered to efficiently encode, process and query such corpora on low-cost hardware.
Community Finding of Malware and Exploit Vendors on Darkweb Marketplaces

Many people involved in malicious cyber activity rely on online environments to improve their hacking skills and capabilities, among which, darkweb marketplaces are one of the most prevalent. Vendors advertise and sell their wares worldwide on those markets, generating communities of like-minded individuals focused on sub fields of hacking. As there is no direct communication between vendors in these environments, identifying the communities formed by them becomes challenging; especially with the absence of ground truth knowledge to validate the results. In this paper, we develop a method based on Machine Learning and Social Network Analysis (SNA) to identify and validate communities of malware and exploit vendors, using product offerings in 20 different marketplaces on the darkweb. To validate the viability of our approach, we cross-validate the community assignments of common individuals selling their products on two mutually exclusive sets of marketplaces, demonstrating how the multiplexity of social ties can be used to detect and validate communities of malware and exploit vendors.
Unsupervised Learning with Stein's Unbiased Risk Estimator

Learning from unlabeled and noisy data is one of the grand challenges of machine learning. As such, it has seen a flurry of research with new ideas proposed continuously. In this work, we revisit a classical idea: Stein's Unbiased Risk Estimator (SURE). We show that, in the context of image recovery, SURE and its generalizations can be used to train convolutional neural networks (CNNs) for a range of image denoising and recovery problems without any ground truth data. Specifically, our goal is to reconstruct an image $x$ from a noisy linear transformation (measurement) of the image. We consider two scenarios: one where no additional data is available and one where we have measurements of other images that are drawn from the same noisy distribution as $x$, but have no access to the clean images. Such is the case, for instance, in the context of medical imaging, microscopy, and astronomy, where noise-less ground truth data is rarely available. We show that in this situation, SURE can be used to estimate the mean-squared-error loss associated with an estimate of $x$. Using this estimate of the loss, we train networks to perform denoising and compressed sensing recovery. In addition, we also use the SURE framework to partially explain and improve upon an intriguing results presented by Ulyanov et al. in "Deep Image Prior": that a network initialized with random weights and fit to a single noisy image can effectively denoise that image. Public implementations of the networks and methods described in this paper can be found at this https URL.
GIST at SemEval-2018 Task 12: A network transferring inference knowledge to Argument Reasoning Comprehension task

This paper describes our GIST team system that participated in SemEval-2018 Argument Reasoning Comprehension task (Task 12). Here, we address two challenging factors: unstated common senses and two lexically close warrants that lead to contradicting claims. A key idea for our system is full use of transfer learning from the Natural Language Inference (NLI) task to this task. We used Enhanced Sequential Inference Model (ESIM) to learn the NLI dataset. We describe how to use ESIM for transfer learning to choose correct warrant through a proposed system. We show comparable results through ablation experiments. Our system ranked 1st among 22 systems, outperforming all the systems more than 10%.
Assessment of sequence dependent geometric distortion in contrast-enhanced MR images employed in stereotactic radiosurgery treatment planning

This work focuses on MR-related sequence dependent geometric distortions, which are associated with B0 inhomogeneity and patient-induced distortion (susceptibility differences and chemical shift effects), in MR images used in stereotactic radiosurgery (SRS) applications. Emphasis is put on characterizing distortion at target brain areas identified by gadolinium diethylenetriamine pentaacetic acid (Gd-DTPA) paramagnetic contrast agent uptake. A custom-made phantom for distortion detection was modified to accommodate two small cylindrical inserts, simulating small brain targets. The inserts were filled with Gd-DTPA solutions of various concentrations (0–20 mM). The phantom was scanned at 1.5 T unit using both the reversed read gradient polarity (to determine the overall distortion as reflected by the inserts centroid offset) and the field mapping (to determine B0 inhomogeneity related distortion in the vicinity of the inserts) techniques. Post-Gd patient images involving a total of 10 brain metastases/targets were also studied using a similar methodology. For the specific imaging conditions, contrast agent presence was found to evidently affect phantom insert position, with centroid offset extending up to 0.068 mm mM−1 (0.208 ppm mM−1). The Gd-DTPA induced distortion in patient images was of the order of 0.5 mm for the MRI protocol used, in agreement with the phantom results. Total localization uncertainty of metastases-targets in patient images ranged from 0.35 mm to 0.87 mm, depending on target location, with an average value of 0.54 mm (2.24 ppm). This relative wide range of target localization uncertainty results from the fact that the B0 inhomogeneity distortion vector in a specific location may add to or partly counterbalance Gd-DTPA induced distortion, thus increasing or decreasing, respectively, the total sequence dependent distortion. Although relatively small, the sequence dependent distortion in Gd-DTPA enhanced brain images can be easily taken into account for SRS treatment planning and target definition purposes by carefully inspecting both the forward and reversed polarity series.
A neural dynamic architecture that autonomously builds mental models

Reasoning and other mental operations are believed to rely on mental models. Arguments have been made that mental models share representational substrate with perception. Here, we demonstrate that a neural dynamic architecture that perceptually grounds language may also support the building of mental models. Supplied with a sequence of simple premises that specify the colors of object pairs as well as their spatial relation, the architecture builds a mental model of the described scene. We show how the neural processes of the architecture evolve in response to both determinate and indeterminate premises. For indeterminate premises, we demonstrate that the preferred mental models observed in human participants emerge from the underlying neural dynamics.
Privatization of the Commons and Access to Land on The Frontier : Evidence from The Colonial Legacy in The Democratic Republic of Congo .

What is the importance of colonial policies in shaping today’s land tenure institutions and inequalities in access to land ? This paper sheds light on this question by analyzing ”paysannat”, a colonial intervention in the Belgian Congo attempting to push the evolution of the tenure system from communal toward private property rights. In the context of forced cultivation of cash crops, the Colony imposed the privatization of communal land (forests or fallows) to individual families in some villages. Despite ambitious plans, paysannat was only partially implemented due to administrative capacity and local resistence. Using spatial discontinuities of its implementation and a unique combination of contemporary household survey data, geographic data, as well as historic data from both colonial records and contemporary oral history surveys, this paper shows that paysannat had a persistent impact on local land institutions through its impact on the privatization of collective land. We find that paysannat was successful in pushing toward the indivualization of the commons, and that it had important distribution consequences between the clanic groups.
Assessment of errors in the measurement chain of distribution grids for feasibility study of a PMU application

This paper presents the results of tests done to review and discuss required levels of accuracy in the entire instrumentation and measurement chain of PMU-applications for distribution grids. Specific grid monitoring and estimation applications could have different requirements in terms of accuracy and resolution of the PMU estimates. Apart from the PMU's own accuracy, the overall accuracy of PMU estimates are dependent on accuracy level of the entire instrumentation channel and the grid's physical parameters. The results and discussions presented in this paper are in the context of a PMU-based line parameter estimation and Dynamic Line Rating (DLR) algorithm for medium voltage lines. These tests could be used to check feasibility of the DLR application on existing instrumentation infrastructure as well as to select the instrumentation equipment for futuristic applications in grids. The same methodology could be utilized to investigate the feasibility of other PMU-based applications for distribution grids.