Miracle-dz/UniFAR-FTUs huggingface dataset:
Hypocretin as a Hub for Arousal and Motivation

The lateral hypothalamus is comprised of a heterogeneous mix of neurons that serve to integrate and regulate sleep, feeding, stress, energy balance, reward, and motivated behavior. Within these populations, the hypocretin/orexin neurons are among the most well studied. Here, we provide an overview on how these neurons act as a central hub integrating sensory and physiological information to tune arousal and motivated behavior accordingly. We give special attention to their role in sleep-wake states and conditions of hyper-arousal, as is the case with stress-induced anxiety. We further discuss their roles in feeding, drug-seeking, and sexual behavior, which are all dependent on the motivational state of the animal. We further emphasize the application of powerful techniques, such as optogenetics, chemogenetics, and fiber photometry, to delineate the role these neurons play in lateral hypothalamic functions.
Mix&Match - Agent Curricula for Reinforcement Learning

We introduce MixM using our method to progress through an action-space curriculum we achieve both faster training and better final performance than one obtains using traditional methods. (2) We further show that M&M can be used successfully to progress through a curriculum of architectural variants defining an agents internal state. (3) Finally, we illustrate how a variant of our method can be used to improve agent performance in a multitask setting.
Validation of PiezoRx Pedometer Derived Sedentary Time

Although pedometers are valid tools for measuring physical activity, to date they have not been used to assess sedentary time. The primary purpose of this study was to determine if the PiezoRx pedometer is a valid and reliable measure of sedentary time compared to the hip-worn Actical accelerometer. A secondary purpose was to compare sedentary time derived via the Fitbit Flex with that of the Actical. Finally, a third purpose was to compare sedentary time derived from the above devices, with that of the ActivPAL inclinometer. Thirty-five participants ages 11–69 years (Mage= 23.3; 21 Female) wore five devices for up to one week: two PiezoRx pedometers, an Actical, an ActivPAL and a Fitbit Flex. Participants recorded daily wear-time of each device using a log sheet. The average sedentary time calculated from the PiezoRx (716±137.68 min/day) was not different from the Actical (694 ±136.11 min/day, p>0.05), although it was higher than the ActivPAL (475±171.52 min/day) and Fitbit Flex (530±149.94 min/day, all p<0.001). Sedentary time from all devices were significantly correlated with each other, with the strongest relationship seen between the Actical and PiezoRx (R2=0.93, p<0.001). In comparison to the ActivPAL, error in PiezoRx- (R2=0.96), Actical- (R2=0.96) and Fitbit Flex- (R2=0.34) determined sedentary time was strongly associated with standing time (all p<0.001). Sedentary time derived using the PiezoRx pedometer may be statistically equivalent to the Actical accelerometer, but not the ActivPAL inclinometer or Fitbit Flex.
X-FDR: A Cross-Layer Routing Protocol for Multihop Full-Duplex Wireless Networks

The recent developments in self-interference (SI) cancellation techniques have led to the practical realization of FD radios that can perform simultaneous transmission and reception. FD technology is attractive for various legacy communications standards. In this article, after discussing the opportunities of FD technology at the network layer, we present a cross-layer aided routing protocol, called X-FDR, for multihop FD wireless networks. X-FDR exploits a physical (PHY) layer model capturing the imperfection of SI cancellation. At the medium access control (MAC) layer, X-FDR adopts an optimized MAC protocol that implements a power control mechanism without creating the hidden terminal problem. X-FDR exploits the unique characteristics of FD technology at the network layer to construct energy-efficient and low end-to-end latency routes in the network. Performance evaluation demonstrates the effectiveness of X-FDR in achieving the gains of FD at higher layers of the protocol stack.
Perturbations in a Signed Graph and its Index

Abstract In this paper we consider the behaviour of the largest eigenvalue (also called the index) of signed graphs under small perturbations like adding a vertex, adding an edge or changing the sign of an edge. We also give a partial ordering of signed cacti with common underlying graph by their indices and demonstrate a general method for obtaining lower and upper bounds for the index. Finally, we provide our computational results related to the generation of small signed graphs.
An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network †

The ubiquity of Bluetooth-enabled smartphones and peripherals has brought tremendous convenience to our daily life. In recent years, Bluetooth beacons have also been gaining popularity in implementing a variety of innovative location-based services such as self-guided systems in exhibition centers. However, the broadcast-based beacon technology can only provide unidirectional communication. In case smartphone users would like to respond to the beacon messages, they have to rely on their own mobile Internet connections to send the information back to the backend system. Nevertheless, mobile Internet services may not be always available or too costly. In this work, we develop a real-time locating system based only on the Bluetooth low energy (BLE) technology to support interactive communications by combining the broadcast and mesh topology options to extend the applicability of beacon solutions. Specifically, we turn the smartphone into a beacon device and augment the beacon devices with the capability of forming a mesh network. The implementation result shows that our beacon devices can detect the presence of specific users at specific locations, and then the presence state can be sent to the application server via the relay of beacon devices. Moreover, the application server can send personalized location-based messages to the users, again via the relay of beacon devices. With the capability of relaying messages between the beacon devices, it would be convenient for developers to implement a variety of interactive applications such as tracking VIP customers at the airport, or tracking an elder with Alzheimer’s disease in the neighborhood.
Explorations of Ransomware

Ransomware is a form of malicious software that prevents users from accessing their system until they pay a ransom. The outline for a typical attack is as follows: (1) Infection, (2) Encryption, (3) Extortion, (4) Decryption [1], where encryption is executed using a key that is inaccessible to the user and must be obtained from the attacker. Ransomware attacks have gained a lot of popularity over the last year, affecting systems that contain critical data such as health care providers, government entities and multinationals, and also bringing great profits for attackers [1]. For example, in 2018 the city of Atlanta was hit by a costly ransomware attack [2]. As ransomware victims invest large amounts of resources to prevent and mitigate these attacks and recover data, we believe it is important to explore variations of this malware to understand the possible risks that systems are exposed to. This paper proposes two novel variants of ransomware attacks, where each provide an alternative to ransom payments in order to regain access to their system. The first, RansomShare, enables a user to decrypt their files once they have successfully infected a minimum number of other users. The second, Detention, enables a user to decrypt their files by computing a solution to a puzzle that is guaranteed to take a predetermined bounded amount of time, with the user having the option to pay chosen amounts to reduce this time proportionately. The remainder of this paper is divided into four sections. Section 2 reviews the relevant schemes used to implement a proof of concept for the two proposed versions of ransomware. Section 3 describes the methodologies of construction for both variants of ransomware. Section 4 describes
Toward Automatically Measuring Learner Ability from Human-Machine Dialog Interactions using Novel Psychometric Models

While dialog systems have been widely deployed for computer-assisted language learning (CALL) and formative assessment systems in recent years, relatively limited work has been done with respect to the psychometrics and validity of these technologies in evaluating and providing feedback regarding student learning and conversational ability. This paper formulates a Markov decision process based measurement model, and applies it to text chat data collected from crowdsourced native and non-native English language speakers interacting with an automated dialog agent. We investigate how well the model measures speaker conversational ability, and find that it effectively captures the differences in how native and non-native speakers of English accomplish the dialog task. Such models could have important implications for CALL systems of the future that effectively combine dialog management with measurement of learner conversational ability in real-time.
Melatonin: A Multifunctional Factor in Plants

Melatonin (N-acetyl-5-methoxy-tryptamine) is a universal molecule that is present in animals and plants. It has been detected in different kinds of plants and organs in different levels. Melatonin in plants shares the same initial biosynthesis compound with auxin, and therefore functions as indole-3-acetic acid like hormones. Moreover, melatonin is involved in regulating plant growth and development, protecting plants against biotic and abiotic stresses, such as salt, drought, cold, heat and heavy metal stresses. Melatonin improves the stress tolerance of plants via a direct pathway, which scavenges reactive oxygen species directly, and indirect pathways, such as increasing antioxidate enzymes activity, photosynthetic efficiency and metabolites content. In addition, melatonin plays a role in regulating gene expression, and hence affects performance of plants. In this review, the biosynthesis pathway, growth and development regulation, and the environment stress response of melatonin in plants are summarized and future research directions and priorities of melatonin in plants are speculated.
On Latent Distributions Without Finite Mean in Generative Models

We investigate the properties of multidimensional probability distributions in the context of latent space prior distributions of implicit generative models. Our work revolves around the phenomena arising while decoding linear interpolations between two random latent vectors -- regions of latent space in close proximity to the origin of the space are sampled causing distribution mismatch. We show that due to the Central Limit Theorem, this region is almost never sampled during the training process. As a result, linear interpolations may generate unrealistic data and their usage as a tool to check quality of the trained model is questionable. We propose to use multidimensional Cauchy distribution as the latent prior. Cauchy distribution does not satisfy the assumptions of the CLT and has a number of properties that allow it to work well in conjunction with linear interpolations. We also provide two general methods of creating non-linear interpolations that are easily applicable to a large family of common latent distributions. Finally we empirically analyze the quality of data generated from low-probability-mass regions for the DCGAN model on the CelebA dataset.
Playing Atari with Six Neurons

Deep reinforcement learning, applied to vision-based problems like Atari games, maps pixels directly to actions; internally, the deep neural network bears the responsibility of both extracting useful information and making decisions based on it. By separating the image processing from decision-making, one could better understand the complexity of each task, as well as potentially find smaller policy representations that are easier for humans to understand and may generalize better. To this end, we propose a new method for learning policies and compact state representations separately but simultaneously for policy approximation in reinforcement learning. State representations are generated by an encoder based on two novel algorithms: Increasing Dictionary Vector Quantization makes the encoder capable of growing its dictionary size over time, to address new observations as they appear in an open-ended online-learning context; Direct Residuals Sparse Coding encodes observations by disregarding reconstruction error minimization, and aiming instead for highest information inclusion. The encoder autonomously selects observations online to train on, in order to maximize code sparsity. As the dictionary size increases, the encoder produces increasingly larger inputs for the neural network: this is addressed by a variation of the Exponential Natural Evolution Strategies algorithm which adapts its probability distribution dimensionality along the run. We test our system on a selection of Atari games using tiny neural networks of only 6 to 18 neurons (depending on the game's controls). These are still capable of achieving results comparable---and occasionally superior---to state-of-the-art techniques which use two orders of magnitude more neurons.
Virt . SC : Combining Self-Checksumming with Virtualization Obfuscation

Man-At-The-End (MATE) attacks are a threat to software security and intellectual property. Tampering with the software code (also called code manipulation) is an attack that enables adversaries to achieve such goals. Attackers can tamper with the code statically (i.e. binary representation), or dynamically (i.e. in memory). One way to raise the bar against attacks of such is to employ code integrity guards, e.g., self-checksumming [1]. Self-checksumming guards check that program’s code hashes to expected values and respond, in a punishing way to users, upon detection of inconsistencies. Clearly, all the expected values have to be precomputed and fed into guards in advance. Bear in mind that checks take place at runtime, and hence hashes are computed over the machine code representation of the software. Therefore, adjusting guards (precomputing expected hashes and subsequently feeding them into software) requires an after compilation patching routine. The post-patching process is heavily architecture (OS, CPU) dependent and extremely error-prone [2]. In order to facilitate the usage of self-checksumming techniques, it is highly desirable to eliminate the necessity for the post-patching process. Applying guards at a higher level of abstraction instead of the machine code can aid us to achieve this goal. One possibility is to employ guards on top of virtualized instructions [3]. The virtualization obfuscation technique [4] can readily provide this abstraction of interest. Therefore, combining self-checksumming with virtualization obfuscation is particularly compelling.
A Novel Displacement and Tilt Detection Method Using Passive UHF RFID Technology

The displacement and tilt angle of an object are useful information for wireless monitoring applications. In this paper, a low-cost detection method based on passive radio frequency identification (RFID) technology is proposed. This method uses a standard ultrahigh-frequency (UHF) RFID reader to measure the phase variation of the tag response and detect the displacement and tilt angle of RFID tags attached to the targeted object. An accurate displacement result can be detected by the RFID system with a linearly polarized (LP) reader antenna. Based on the displacement results, an accurate tilt angle can also be detected by the RFID system with a circularly polarized (CP) reader antenna, which has been proved to have a linear relationship with the phase parameter of the tag’s backscattered wave. As far as accuracy is concerned, the mean absolute error (MAE) of displacement is less than 2 mm and the MAE of the tilt angle is less than 2.5° for an RFID system with 500 mm working range.
Encountered‐type haptic display for large VR environment using per‐plane reachability maps

We show a novel encountered‐type haptic system, H‐Wall, to enable haptic feedback using a manipulator of seven degrees of freedom suitable for simulating indoor virtual reality environments, which are characterized and confined by a set of vertical walls and revolving doors. At runtime, our system tracks hand motion using a red–green–blue depth sensor and locates its configuration. Then, the robotic manipulator plans a trajectory for the end effector, attached to a rectangular rigid board, to make contact with the hand to deliver a sense of touch as long as the perceived hand contact force is substantial. The force feedback is generated in a passive sense for static walls that the rigid board, corresponding to a vertical wall, holds its position as long as the perceived hand contact force is substantial. For a revolving door, the force feedback is generated in an active sense based on impedance control. In order to address the issue of limited workspace, we also propose a new reachability map, called per‐plane reachability map, that is optimized to answer whether passive haptic feedback can be generated by a manipulator when the user touches a vertical wall at a given orientation. We successfully demonstrate our system to provide an illusion to the user in a virtual environment with touch sensation to the surrounding environment.
Genome-wide significant risk factors on chromosome 19 and the APOE locus

The apolipoprotein E (APOE) gene on chromosome 19q13.32, was the first, and remains the strongest, genetic risk factor for Alzheimer’s disease (AD). Additional signals associated with AD have been located in chromosome 19, including ABCA7 (19p13.3) and CD33 (19q13.41). The ABCA7 gene has been replicated in most populations. However, the contribution to AD of other signals close to APOE gene remains controversial. Possible explanations for inconsistency between reports include long range linkage disequilibrium (LRLD). We analysed the contribution of ABCA7 and CD33 loci to AD risk and explore LRLD patterns across APOE region. To evaluate AD risk conferred by ABCA7 rs4147929:G>A and CD33 rs3865444:C>A, we used a large Spanish population (1796 AD cases, 2642 controls). The ABCA7 rs4147929:G>A SNP effect was nominally replicated in the Spanish cohort and reached genome-wide significance after meta-analysis (odds ratio (OR)=1.15, 95% confidence interval (95% CI)=1.12–1.19; P = 1.60 x 10-19). CD33 rs3865444:C>A was not associated with AD in the dataset. The meta-analysis was also negative (OR=0.98, 95% CI=0.93–1.04; P=0.48). After exploring LRLD patterns between APOE and CD33 in several datasets, we found significant LD (D’ >0.20; P <0.030) between APOE-Ɛ2 and CD33 rs3865444C>A in two of five datasets, suggesting the presence of a non-universal long range interaction between these loci affecting to some populations. In conclusion, we provide here evidence of genetic association of the ABCA7 locus in the Spanish population and also propose a plausible explanation for the controversy on the contribution of CD33 to AD susceptibility.
Neural Cryptography : From Symmetric Encryption to Adversarial Steganography

Neural Cryptography is an emergent field that aims to combine cryptography with Neural Networks for applications in cryptanalysis and encryption. In this paper, we (1) show Neural Networks are capable of performing symmetric encryption in an adversarial setting and improve on the known literature on this topic. We also (2) show that Neural Networks are capable of detecting known cryptographically insecure communication by having them play known cryptographic games based on Ciphertext Indistinguishability. Finally, we (3) present further research in Neural Steganography in the context of developing neural end-to-end stegonographic (imagein-image, text-in-image, video-in-video) algorithms in the presence of adversarial networks attempting to censor. I. SYMMETRIC ENCRYPTION Symmetric encryption is a form of encryption in which the sender and receiver use the same key to encrypt a plaintext and decrypt the corresponding cyphertext. Traditionally, symmetric encryption algorithms have used either block or stream ciphers. However, it has been demonstrated that in a system of neural networks, with end-to-end adversarial training, they can learn how to perform forms of ‘encryption’ and ‘decryption’ without the use of a specific cryptographic algorithm [1].
Combination of arbitrary gradient encoding fields using SPACE RIP for reconstruction ( COGNAC )

Introduction: For parallel imaging it has recently been suggested to use non-linear radially symmetric gradients in order to better complement to receiving coil arrays [1]. Furthermore it has been assumed that non-linear gradients can reduce peripheral nerve stimulations and therefore allow faster gradient switching [2]. For example, using a radially symmetric gradient for frequency encoding, the object is projected onto concentric rings. For the encoding of the angular direction, a method is presented that uses both conventional gradients and the spatial information of the underlying receiver coil array. As the conventional k-space formalism needs gradients with uniform directions and therefore limits the possibilities of sampling, SPACE RIP [3] is used for reconstruction. In SPACE RIP an encoding matrix describes both, coil sensitivity profiles and gradient encoding. Reconstruction is done by inverting the encoding matrix. Combination Of arbitrary Gradient eNcoding fields using SPACE RIP for reconstruction (COGNAC) makes use of this approach to allow the use of arbitrary phase encoding fields for each phase encoding step. Theory: The signal equation for one phase encoding step in COGNAC is the generalized form of the SENSE [4] equation, where all signals on a frequency isocontour folds onto one pixel. Furthermore the equation is supplemented with the phase induced by the phase encoding field and yields in matrixform S ρ ρ ⋅ = ⋅ = ⋅ C G C' r r r u
Carnosine Prevents Type 2 Diabetes-Induced Osteoarthritis Through the ROS/NF-κB Pathway

Background: The anti-inflammatory and antioxidant capacity of carnosine (CAR) has been investigated in autoimmune diseases. The aim of this study was to evaluate the potential protective effects of oral CAR supplements to ameliorate type 2 diabetes mellitus (T2DM)-induced osteoarthritis (OA) in rats and its mechanism. Methods: Seventy male Sprague–Dawley rats were randomly divided into the control group (CG, n = 10) and the T2DM group (n = 60). A rat model of T2DM was established using a high fat diet and streptozotocin (30 mg/kg, i.p.). The 41 rats that developed T2DM were chosen and randomly divided into four groups: T2DM-induced OA group (OAG, n = 11), and the T2DM-induced OA with low, moderate, and high-doses of CAR for 8 weeks group (CAR-L, CAR-M, and CAR-H, n = 10). After 13 weeks, all rats were evaluated by enzyme-linked immunosorbent assay (ELISA), histology, immunohistochemistry, and western blotting. Fibroblast-like synoviocytes (FLSs) were obtained from the knee joints of all rats. The effects of CAR on the inflammatory response in interleukin (IL)-1β-stimulated FLSs under a high glucose environment were evaluated by real-time quantitative polymerase chain reaction, western blotting, flow cytometry, and immunofluorescence. Results: The results of ELISA (IL-1β and tumor necrosis factor-α), the histological evaluation (Mankin and OARSI score), western blotting [COL2A1, matrix metalloproteinase (MMP)-3, MMP-13, IL-1β, and nuclear factor-kappaB (NF-κB) p65], and immunohistochemistry (COL2A1, MMP-3, and MMP-13) indicated that oral CAR attenuated the development of T2DM-induced OA and suppressed the inflammatory response. Moreover, CAR alleviated MMP-3 and MMP-13 expression levels by decreasing reactive oxygen species content and suppressing nuclear translocation of NF-κB p65 on IL-1β-induced FLSs in a high glucose environment. Conclusion: These findings indicate that oral CAR had chondroprotective effects on T2DM-induced OA through the reactive oxygen species (ROS)/NF-κB pathway.