Miracle-dz/UniFAR-FTUs huggingface dataset:
Permutations Unlabeled Beyond Sampling Unknown

A recent unlabeled sampling result by Unnikrishnan, Haghighatshoar, and Vetterli states that with probability one over Gaussian random matrices A with iid entries, any <inline-formula><tex-math notation="LaTeX">$x$</tex-math></inline-formula> can be uniquely recovered from an unknown permutation of <inline-formula><tex-math notation="LaTeX">$y = A x$</tex-math></inline-formula> as soon as <inline-formula><tex-math notation="LaTeX">$A$</tex-math></inline-formula> has at least twice as many rows as columns. We show that this condition on <inline-formula><tex-math notation="LaTeX">$A$</tex-math></inline-formula> implies something much stronger: that an unknown vector <inline-formula><tex-math notation="LaTeX">$x$</tex-math></inline-formula> can be recovered from measurements <inline-formula><tex-math notation="LaTeX">$y = T A x$</tex-math></inline-formula>, when the unknown <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> belongs to an arbitrary set of invertible, diagonalizable linear transformations <inline-formula><tex-math notation="LaTeX">$\mathcal {T}$</tex-math></inline-formula>. The set <inline-formula><tex-math notation="LaTeX">$\mathcal {T}$</tex-math></inline-formula> can be finite or countably infinite. When it is the set of <inline-formula><tex-math notation="LaTeX">$m \times m$</tex-math></inline-formula> permutation matrices, we have the classical unlabeled sampling problem. We show that for almost all <inline-formula><tex-math notation="LaTeX">$A$</tex-math></inline-formula> with at least twice as many rows as columns, all <inline-formula><tex-math notation="LaTeX">$x$</tex-math></inline-formula> can be recovered either uniquely, or up to a scale depending on <inline-formula><tex-math notation="LaTeX">$\mathcal {T}$</tex-math></inline-formula>, and that the condition on the size of <inline-formula><tex-math notation="LaTeX">$A$</tex-math></inline-formula> is necessary. Our proof is based on vector space geometry. Specializing to permutations, we obtain a simplified proof of the uniqueness result of Unnikrishnan, Haghighatshoar, and Vetterli. In this letter, we are only concerned with uniqueness; stability and algorithms are left for future work.
A Convolutional Neural Network Approach for Estimating Tropical Cyclone Intensity Using Satellite-based Infrared Images

Existing techniques for satellite-based tropical cyclone (TC) intensity estimation involve an explicit feature extraction step to model TC intensity on a set of relevant TC features or patterns such as eye formation and cloud organization. However, crafting such a feature set is often time-consuming and requires expert knowledge. In this paper, a convolutional neural network (CNN) approach, which eliminates explicit feature extraction, for estimating the intensity of tropical cyclones is proposed. Utilizing a Visual Geometry Group 19-1ayer CNN (VGG19) model pre-trained on ImageNet, transfer learning experiments were performed using grayscale IR images of TCs obtained from various geostationary satellites in the Western North Pacific region (1996 - 2016) to estimate TC intensity. The model re-trained on TC images achieved a root-mean-square error (RMSE) of 13.23 knots - a performance comparable to existing feature-based approaches (RMSE ranging from 12 to 20 knots). Moreover, the model was able to learn generic TC features that were previously identified in feature-based approaches as important indicators of TC intensity.
Dynamic Hand Gesture Recognition Using FMCW Radar Sensor for Driving Assistance

Dynamic hand gesture recognition is very important for human-computer interaction. In vehicles, hand gesture recognition can be used as the driver’s auxiliary system to achieve remote control of the instrument. To a certain extent, this system can avoid physical buttons and touch screens causing interference to the driver. In this paper, we describe a driver assisted dynamic gesture recognition system to classify nine hand gestures based on micro-Doppler signatures obtained by 77GHz FMCW radar using a convolutional neural network (CNN). We further explore the changes in the accuracy of same gestures in a variety of experimental scenarios to help optimize the robustness of the system.
A Co-occurrence Background Model with Hypothesis on Degradation Modification for Object Detection in Strong Background Changes

Object detection has become an indispensable part of video processing and current background models are sensitive to background changes. In this paper, we propose a novel background model using an algorithm called Co-occurrence Pixel-block Pairs (CPB) against background changes, such as illumination changes and background motion. We utilize the co-occurrence “pixel to block” structure to extract the spatial-temporal information of each pixel to build background model, and then employ an efficient evaluation strategy to identify the current state of each pixel, which is named as correlation dependent decision function. Furthermore, we also introduce a Hypothesis on Degradation Modification (HoD) into CPB structure to reinforce the robustness of CPB. Experimental results obtained from the dataset of the PETS 2001, AIST-Indoor, SBMnet and CDW-2012 databases show that our models can detect objects robustly in strong background changes.
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations

Multi-agent reinforcement learning systems aim to provide interacting agents with the ability to collaboratively learn and adapt to the behaviour of other agents. In many real-world applications, the agents can only acquire a partial view of the world. Here we consider a setting whereby most agents' observations are also extremely noisy, hence only weakly correlated to the true state of the environment. Under these circumstances, learning an optimal policy becomes particularly challenging, even in the unrealistic case that an agent's policy can be made conditional upon all other agents' observations. To overcome these difficulties, we propose a multi-agent deep deterministic policy gradient algorithm enhanced by a communication medium (MADDPG-M), which implements a two-level, concurrent learning mechanism. An agent's policy depends on its own private observations as well as those explicitly shared by others through a communication medium. At any given point in time, an agent must decide whether its private observations are sufficiently informative to be shared with others. However, our environments provide no explicit feedback informing an agent whether a communication action is beneficial, rather the communication policies must also be learned through experience concurrently to the main policies. Our experimental results demonstrate that the algorithm performs well in six highly non-stationary environments of progressively higher complexity, and offers substantial performance gains compared to the baselines.
A fast Cascade Shape Regression Method based on CNN-based Initialization

Cascade shape regression (CSR) methods predict facial landmarks by iteratively updating an initial shape and are state-of-the-art. The initial shape always limits the result and causes local optimum, which is usually obtained from the average face or by randomly picking a face from the training set. In this paper, we propose a CNN-based initial method for CSR. Convolution neural network provides a highly robust initial shape estimation, while the following CSR algorithm fine-tunes the initialization rapidly to achieve higher accuracy. Furthermore, CNN-based initial approach is proposed to get 68-point initial shape, which is calculated from convolutional network 5-point result by the radial basis function interpolation with thin-plate splines (RBF-TPS). Extensive experiments demonstrate that CSR methods are sensitive to the initialization and proposed approach gets favorable results compared to state-of-the-art algorithms and achieves real-time performance.
Tuning Natural Killer Cell Anti-multiple Myeloma Reactivity by Targeting Inhibitory Signaling via KIR and NKG2A

Natural killer (NK) cells are attractive candidates for allogeneic cell-based immunotherapy due to their potent antitumor effector function and good safety profile. NK cells express killer immunoglobulin-like receptors (KIRs) and the NKG2A receptor important for NK cells education as well as providing inhibitory signals upon encountering HLA-expressing target cells. Multiple myeloma (MM) is an example of a tumor expressing relatively high levels of HLA molecules. In this review, we discuss the functional relevance of inhibitory KIRs and NKG2A for NK cells anti-MM response and strategies to lower these inhibitory signaling to enhance clinical efficacy of allogeneic NK cells in MM.
Dopaminergic Neurotransmission in Patients With Parkinson's Disease and Impulse Control Disorders: A Systematic Review and Meta-Analysis of PET and SPECT Studies

Background: Around 30% Parkinson's disease (PD) patients develop impulse control disorders (ICDs) to D2/3 dopamine agonists and, to a lesser extent, levodopa. We aim to investigate striatal dopaminergic function in PD patients with and without ICD. Methods: PubMed, Science Direct, EBSCO, and ISI Web of Science databases were searched (from inception to March 7, 2018) to identify PET or SPECT studies reporting striatal dopaminergic function in PD patients with ICD (ICD+) compared to those without ICD (ICD–). Studies which included drug naïve patients, explored non-pharmacological procedures (e.g., deep brain stimulation), and those using brain blood perfusion or non-dopaminergic markers were excluded. Standardized mean difference (SDM) was used and random-effect models were applied. Separate meta-analyses were performed for dopamine transporter level, dopamine release, and dopamine receptors availability in the putamen, caudate, dorsal, and ventral striatum. Results: A total of 238 studies were title and abstract screened, of which 19 full-texts were assessed. Nine studies (ICD+: N = 117; ICD–: N = 175 patients) were included in the analysis. ICD+ showed a significant reduction of dopamine transporter binding in the putamen (SDM = −0.46; 95% CI: −0.80, −0.11; Z = 2.61; p = 0.009), caudate (SDM = −0.38; 95% CI: −0.73, −0.04; Z = 2.18; p = 0.03) and dorsal striatum (SDM = −0.45; 95% CI: −0.77, −0.13; Z = 2.76; p = 0.006), and increased dopamine release to reward-related stimuli/gambling tasks in the ventral striatum (SDM = −1.04; 95% CI: −1.73, −0.35; Z = 2.95; p = 0.003). Dopamine receptors availability did not differ between groups. Heterogeneity was low for dopamine transporter in the dorsal striatum (I2 = 0%), putamen (I2 = 0%) and caudate (I2 = 0%), and pre-synaptic dopamine release in the dorsal (I2 = 0%) and ventral striatum (I2 = 0%); heterogeneity was high for dopamine transporter levels in the ventral striatum (I2 = 80%), and for dopamine receptors availability in the ventral (I2 = 89%) and dorsal (I2 = 86%) striatum, putamen (I2 = 93%), and caudate (I2 = 71%). Conclusions: ICD+ patients show lower dopaminergic transporter levels in the dorsal striatum and increased dopamine release in the ventral striatum when engaged in reward-related stimuli/gambling tasks. This dopaminergic imbalance might represent a biological substrate for ICD in PD. Adequately powered longitudinal studies with drug naïve patients are needed to understand whether these changes may represent biomarkers of premorbid vulnerability to ICD.
Do You Trust Me? Facial Width-to-Height Ratio of Website Avatars and Intention to Purchase from Online Store

This research investigates whether the facial width-to-height ratio (fWHR) of avatars can serve as a first cue to website impression formation. Research has demonstrated that fWHR triggers people’s inference making regarding the behavioral intentions of others and therefore behavioral responses in the form of approach vs. avoidance behavior. Our research model examines the effect of an online store avatar’s fWHR on intention to purchase from the online store and whether this effect is mediated by perceptions of avatar’s warmth and online store’s trustworthiness. Mediation analysis revealed that a higher fWHR causes a lower intention to purchase and that this effect is mediated by perceptions of avatar’s warmth and resulting online store’s trustworthiness.
MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment

This research strives for natural language moment retrieval in long, untrimmed video streams. The problem is not trivial especially when a video contains multiple moments of interests and the language describes complex temporal dependencies, which often happens in real scenarios. We identify two crucial challenges: semantic misalignment and structural misalignment. However, existing approaches treat different moments separately and do not explicitly model complex moment-wise temporal relations. In this paper, we present Moment Alignment Network (MAN), a novel framework that unifies the candidate moment encoding and temporal structural reasoning in a single-shot feed-forward network. MAN naturally assigns candidate moment representations aligned with language semantics over different temporal locations and scales. Most importantly, we propose to explicitly model moment-wise temporal relations as a structured graph and devise an iterative graph adjustment network to jointly learn the best structure in an end-to-end manner. We evaluate the proposed approach on two challenging public benchmarks DiDeMo and Charades-STA, where our MAN significantly outperforms the state-of-the-art by a large margin.
Efficient Lifelong Learning with A-GEM

In lifelong learning, the learner is presented with a sequence of tasks, incrementally building a data-driven prior which may be leveraged to speed up learning of a new task. In this work, we investigate the efficiency of current lifelong approaches, in terms of sample complexity, computational and memory cost. Towards this end, we first introduce a new and a more realistic evaluation protocol, whereby learners observe each example only once and hyper-parameter selection is done on a small and disjoint set of tasks, which is not used for the actual learning experience and evaluation. Second, we introduce a new metric measuring how quickly a learner acquires a new skill. Third, we propose an improved version of GEM (Lopez-Paz & Ranzato, 2017), dubbed Averaged GEM (A-GEM), which enjoys the same or even better performance as GEM, while being almost as computationally and memory efficient as EWC (Kirkpatrick et al., 2016) and other regularization-based methods. Finally, we show that all algorithms including A-GEM can learn even more quickly if they are provided with task descriptors specifying the classification tasks under consideration. Our experiments on several standard lifelong learning benchmarks demonstrate that A-GEM has the best trade-off between accuracy and efficiency.
Color Noise-Based Feature for Splicing Detection and Localization

Images that have been altered and more specifically spliced together have invaded the digital domain due to the ease with which we are able to copy and paste them. To detect such forgeries the digital image processing community is proposing new automatic algorithms designed to help human operators reveal manipulated images. In this paper, we focus on a local detection system, which considers which tampered areas produce local statistical effects that do not impact neighboring areas or the image as a whole. We propose to study how the definition of local blocks, considering their size and overlap, impacts final pixel detection. We also propose new features which are an original way to consider the noise of an image as a colored signal. Indeed, in a non-forged image, there is a high correlation of noise between the three color channels R, G and B. We show that an optimal configuration can be defined and in this case the proposed approach outperforms several previously proposed methods using the same tested dataset, in uncompressed and JPEG modes. Note, in this paper we only focus on feature extraction without using machine learning.
Influence of cervical cerclage interventions upon the incidence of neonatal death: a retrospective study comparing prophylactic versus rescue cerclages

Objective The purpose of this study was to compare the efficacy of prophylactic and rescue cervical cerclages for pregnant patients with an incompetent cervix, and to assess the neonatal outcomes of both clinical conditions. Methods This was a retrospective observational study of all women who had an elective or rescue cerclage between January 2008 and December 2016 in our institution. Prophylactic cerclage was defined as a cerclage before 16 weeks of gestation, while rescue cerclages were performed between 16 and 23 weeks of gestation. Results In total, we analyzed the outcomes of 212 cervical interventions; 71% of the recruited patients experienced prophylactic cerclage, while 29% underwent rescue cerclage. Most of the patients delivered vaginally (70%) and were able to leave the hospital with a healthy newborn (78%). The mean pregnancy prolongation time after cerclage in the prophylactic and rescue groups were 21 weeks and 10 weeks, respectively. Conclusion Prophylactic cerclage interventions are most likely to be associated with a reduction of fetal demise because of the correlation between fetal prognosis and the gestational age at which cerclage is performed. Once the diagnosis of cervical insufficiency is confirmed, cerclage should be recommended as this will help to prolong the pregnancy.
A Novel Pilot Allocation Scheme for TDD Massive MIMO Systems

Pilot contamination affects the system performance of time division duplex (TDD) massive multiple-input multiple-output (MIMO) systems, and many pilot allocation methods are proposed to reduce pilot contamination. However, most existing pilot allocation methods focus on the scenes that cells are regular shape or the number of users in each cell is equal, so these methods can not be used in the actual scenes with irregular shaped cells and randomly distributed users. In order to solve this problem, the idea of coalition game is introduced into the pilot allocation method in this paper, and a novel pilot allocation scheme based on coalition game, which can be used in any actual scene, is proposed. Simulation results show that the proposed scheme can effectively reduce pilot contamination and improve average spectrum efficiency of the uplink.
Critical usage factors of ICT in higher education. Case study on Eastern Macedonia and Thrace Institute of Technology

Information and Communication Technologies (ICT) are widely applied in many sectors including education offering new ways of interaction, knowledge dissemination and mobility. Higher education students are familiar with the technology from very young ages. This study suggests and examines potential factors that may influence ICT usage of higher education students. A case study of a Greek higher educational institute is presented. Results reveal the extend that each of the presented factors influence students perceptions regarding ICT use.
Modified AES Cipher Round and Key Schedule

In this paper, Advanced Encryption Standard was modified to address the low diffusion rate at the early rounds by adding additional operations in both the cipher round and the key schedule. The cipher round modifications for rounds 1 to 9 of the encryption cycle were the addition of XOR operation between the SubBytes and the ShiftRow processes and the inclusion of modulo addition between the ShiftRow and MixColumn operations. In the final round of the encryption cycle, modulo addition is inserted between the SubBytes and the ShiftRow. In the decryption cycle of the cipher round, all functions were replaced by their inverses, e.g. SubBytes to InverseSubBytes, Modulo Addition to Modulo Subtraction and so on. Furthermore, the modification in the key schedule algorithm were byte substitution and round constant addition appended to the key schedule algorithm before the key expansion. The byte substitution was utilized by transforming the bytes of the 128-bit master cipher key using the AES S-box and then the result was divided into four 32-bit words. Each word was then XORed with a variable round constant dependent on a specific byte value of the word. The metrics used for evaluation were avalanche effect and frequency test to measure the diffusion and confusion characteristics respectively. Avalanche effect was measured by changing one bit of the input plaintext and determining the percentage of bits that have changed states in the cipher text. While the frequency test determines the randomness of the string by assessing the distribution of ones and zeros. The results of the avalanche effect and the frequency test of the modified AES cipher round and key schedule was compared to the standard AES. The results of the avalanche effect evaluation show that there was an average increase in diffusion of 61.98% in round 1, 14.79% in round 2 and 13.87% in round 3. Consequently, the results of the frequency test demonstrated an improvement in the randomness of the ciphertext since the average difference between the number of ones to zeros is reduced from 11.6 to 6.4 bits along with better-computed p-values. The results clearly show that the modified AES has improved diffusion and confusion properties over the standard AES.
Entrainment in harmonically forced continuous and impulsive Goodwin’s oscillators: a comparison study

The Goodwin oscillator is a simple yet illustrative model of a biochemical system with a stable limit cycle. Considered as a prototypical biological oscillator, Goodwin’s model is broadly used e.g. to describe circadian rhythms, hormonal cycles, self-oscillatory metabolic pathways.These periodic or non-periodic oscillations are self-sustained; at the same time, they are entrainable by external periodic signals, adjusting the characteristics of the autonomous oscillatory behavior. Mathematical analysis of entrainment phenomena, i.e. nonlinear phenomena imposed by periodic exogenous signals, remains an open problem. This paper presents a comparative analysis of forced dynamics arising in two versions of Goodwin’s oscillator: the classical continuous oscillator and a more recent impulsive one, e.g. capturing pulsatile secretion of hormones. The main finding of this study is that while the continuous oscillator is always forced to a periodic solution by a sufficiently large exogenous signal amplitude, the impulsive one commonly exhibits a quasiperiodic or chaotic behavior thus highlighting the role of non-smooth dynamics in entrainment.
An Ensemble Stock Predictor and Recommender System

Stock market analysis and prediction tools have been prevalent for several years now with various techniques and models to predict stock markets efficiently. This paper presents the design and implementation of a novel technique to predict stock market trends. The approach is an ensemble model which takes into account historical stock data, tweets and news affecting the stock prices of various companies and provides recommendations on which stocks to invest in for a particular duration. The model is built on real historical stock data set obtained from Indian National Stock Exchange (www.nseindia.com) over a 20-year period. The model uses Recurrent Neural Networks (RNN) and Long-Short Term Memory (LSTM) to learn and predict the future stock trend for a basket of 50 companies with RMSE of 1.43 (stock units with respect to Indian currency). Finally, the predicted stock price within a duration is converted into a graphical image, which is passed to a Convolutional Neural Network (CNN) classifier. The classifier is trained on finer features like peaks and troughs within the stock trend image, to provide recommendations on when to invest in a particular company stock. When tested with real stock prices over a week, it was found that the model was able to achieve extremely high accuracy in predicting the stock trends.