Miracle-dz/UniFAR-FTUs huggingface dataset:
Central Thalamic-Medial Prefrontal Control of Adaptive Responding in the Rat: Many Players in the Chamber

The medial prefrontal cortex (mPFC) has robust afferent and efferent connections with multiple nuclei clustered in the central thalamus. These nuclei are elements in large-scale networks linking mPFC with the hippocampus, basal ganglia, amygdala, other cortical areas, and visceral and arousal systems in the brainstem that give rise to adaptive goal-directed behavior. Lesions of the mediodorsal nucleus (MD), the main source of thalamic input to middle layers of PFC, have limited effects on delayed conditional discriminations, like DMTP and DNMTP, that depend on mPFC. Recent evidence suggests that MD sustains and amplifies neuronal responses in mPFC that represent salient task-related information and is important for detecting and encoding contingencies between actions and their consequences. Lesions of rostral intralaminar (rIL) and ventromedial (VM) nuclei produce delay-independent impairments of egocentric DMTP and DNMTP that resemble effects of mPFC lesions on response speed and accuracy: results consistent with projections of rIL to striatum and VM to motor cortices. The ventral midline and anterior thalamic nuclei affect allocentric spatial cognition and memory consistent with their connections to mPFC and hippocampus. The dorsal midline nuclei spare DMTP and DNMTP. They have been implicated in behavioral-state control and response to salient stimuli in associative learning. mPFC functions are served during DNMTP by discrete populations of neurons with responses related to motor preparation, movements, lever press responses, reinforcement anticipation, reinforcement delivery, and memory delay. Population analyses show that different responses are timed so that they effectively tile the temporal interval from when DNMTP trials are initiated until the end. Event-related responses of MD neurons during DNMTP are predominantly related to movement and reinforcement, information important for DNMTP choice. These responses closely mirror the activity of mPFC neurons with similar responses. Pharmacological inactivation of MD and adjacent rIL affects the expression of diverse action- and outcome-related responses of mPFC neurons. Lesions of MD before training are associated with a shift away from movement-related responses in mPFC important for DNMTP choice. These results suggest that MD has short-term effects on the expression of event-related activity in mPFC and long-term effects that tune mPFC neurons to respond to task-specific information.
Post-exercise Cold Water Immersion Effects on Physiological Adaptations to Resistance Training and the Underlying Mechanisms in Skeletal Muscle: A Narrative Review

Post-exercise cold-water immersion (CWI) is a popular recovery modality aimed at minimizing fatigue and hastening recovery following exercise. In this regard, CWI has been shown to be beneficial for accelerating post-exercise recovery of various parameters including muscle strength, muscle soreness, inflammation, muscle damage, and perceptions of fatigue. Improved recovery following an exercise session facilitated by CWI is thought to enhance the quality and training load of subsequent training sessions, thereby providing a greater training stimulus for long-term physiological adaptations. However, studies investigating the long-term effects of repeated post-exercise CWI instead suggest CWI may attenuate physiological adaptations to exercise training in a mode-specific manner. Specifically, there is evidence post-exercise CWI can attenuate improvements in physiological adaptations to resistance training, including aspects of maximal strength, power, and skeletal muscle hypertrophy, without negatively influencing endurance training adaptations. Several studies have investigated the effects of CWI on the molecular responses to resistance exercise in an attempt to identify the mechanisms by which CWI attenuates physiological adaptations to resistance training. Although evidence is limited, it appears that CWI attenuates the activation of anabolic signaling pathways and the increase in muscle protein synthesis following acute and chronic resistance exercise, which may mediate the negative effects of CWI on long-term resistance training adaptations. There are, however, a number of methodological factors that must be considered when interpreting evidence for the effects of post-exercise CWI on physiological adaptations to resistance training and the potential underlying mechanisms. This review outlines and critiques the available evidence on the effects of CWI on long-term resistance training adaptations and the underlying molecular mechanisms in skeletal muscle, and suggests potential directions for future research to further elucidate the effects of CWI on resistance training adaptations.
Veksel: Simple, Efficient, Anonymous Payments with Large Anonymity Sets from Well-Studied Assumptions

We propose Veksel, a simple generic paradigm for constructing efficient non-interactive coin mixes. The central component in our work is a concretely efficient proof π1-many that a homomorphic commitment c* is a rerandomization of a commitment c ∈ {c1, …,cℓ} without revealing c. We formalize anonymous account-based cryptocurrency as a universally composable functionality and show how to efficiently instantiate it using π1-many in a straightforward way. We instantiate and implement π1-many from Strong-RSA, DDH and random oracles targeting ≈ 112 bits of security. The resulting NIZK has constant size (|π1-many| = 5.3 KB) and constant proving/verification time (≈ 90 ms), on an already accumulated set. Compared to ZCash---which offers comparable marginal verification cost and an anonymity set consisting of every existing transaction---our transactions are larger (6.2 KB) and verification is slower. On the other hand, Veksel relies on better studied assumptions, has no expensive trusted setup for proofs and is arguably simpler to implement. Additionally we think that π1-many might be interesting in other applications, e.g. proving possession of some credential posted on-chain. The efficiency of our concrete NIZK relies on a new Ristretto-friendly elliptic curve, Jabberwock, that is of independent interest: it can be used to efficiently prove statements on "committments on commitments'' in Bulletproofs.
Bringing State-Separating Proofs to EasyCrypt A Security Proof for Cryptobox

Machine-checked cryptography aims to reinforce confidence in the primitives and protocols that underpin all digital security. However, machine-checked proof techniques remain in practice difficult to apply to real-world constructions. A particular challenge is structured reasoning about complex constructions at different levels of abstraction. The State-Separating Proofs (SSP) methodology for guiding cryptographic proofs by Brzuska, Delignat-Lavaud, Fournet, Kohbrok and Kohlweiss (ASIACRYPT'18) is a promising contestant to support such reasoning. In this work, we explore how SSPs can guide EasyCrypt formalisations of proofs for modular constructions. Concretely, we propose a mapping from SSP to EasyCrypt concepts which enables us to enhance cryptographic proofs with SSP insights while maintaining compatibility with existing EasyCrypt proof support. To showcase our insights, we develop a formal security proof for the cryptobox family of public-key authenticated encryption schemes based on non-interactive key exchange and symmetric authenticated encryption. As a side effect, we obtain the first formal security proof for NaCl's instantiation of cryptobox. Finally we discuss changes to the practice of SSP on paper and potential implications for future tool designers.
Stacking Sigmas: A Framework to Compose Σ-Protocols for Disjunctions

Zero-Knowledge (ZK) Proofs for disjunctive statements have been a focus of a long line of research. Classical results such as Cramer et al. [CRYPTO’94] and Abe et al. [AC’02] design generic compilers that transform certain classes of ZK proofs into ZK proofs for disjunctive statements. However, communication complexity of the resulting protocols in these results ends up being proportional to the complexity of proving all clauses in the disjunction. More recently, Heath et al. [EC’20] exploited special properties of garbled circuits to construct efficient ZK proofs for disjunctions, where the proof size is only proportional to the length of the largest clause in the disjunction. However, these techniques do not appear to generalize beyond garbled circuits. In this work, we focus on achieving the best of both worlds. We design a general framework that compiles a large class of unmodified Σ-protocols, each for an individual statement, into a new Σ-protocol that proves a disjunction of these statements. Our framework can be used both when each clause is proved with the same Σ-protocol and when different Σ-protocols are used for different clauses. The resulting Σ-protocol is concretely efficient and has communication complexity proportional to the communication required by the largest clause, with additive terms that are only logarithmic in the number of clauses. We show that our compiler can be applied to many well-known Σ-protocols, including classical protocols (e.g. Schnorr [JC’91] and Guillou-Quisquater [CRYPTO’88]) and modern MPC-in-the-head protocols such as the recent work of Katz, Kolesnikov and Wang [CCS’18] and the Ligero protocol of Ames et al. [CCS’17]. Finally, since all of the protocols in our class can be made non-interactive in the random oracle model using the Fiat-Shamir transform, our result yields the first generic non-interactive zeroknowledge protocol for disjunctions where the communication only depends on the size of the largest clause.
A New Neural Distinguisher Model Considering Derived Features from Multiple Ciphertext Pairs

Gohr has proposed the only deep learning-based distinguisher model at Crypto 2019, which is used to distinguish reduced Speck32/64 and a pseudorandom permutation. This distinguisher model can be applied to many symmetric ciphers. Given a plaintext diﬀerential, Gohr’s distinguisher model can learn diﬀerences between two distributions from adequate single ciphertext pairs. In this paper, we propose a new neural distinguisher model which takes k (cid:62) 2 ciphertext pairs as the analysis object. A non-uniform distribution can produce many derived features that will not appear in a single ciphertext pair. Our neural distinguisher model can exploit these derived features from k ciphertext pairs. Taking Gohr’s distinguisher model as the baseline model, we ﬁrstly construct strong baseline distinguishers for ﬁve reduced ciphers. Then our neural distinguishers for ﬁve ciphers are also constructed using the new distinguisher model proposed in this paper. Experiments show our neural distinguishers can always obtain distinguishing accuracy promotions under various settings of k . When combining k samples incorrectly classiﬁed by baseline distinguishers into one group, our neural distinguishers can still distinguish correctly with a non-negligible probability. It indicates that derived features have been successfully captured by our neural distinguishers. The distinguishing accuracy promotion also comes from derived features. Our neural distin-guishers can also be used to improve the key recovery attack on 11-round Specck32/64. Besides,compared with the raw attack scheme provided by Gohr, we propose a new key recovery attack scheme which can further reduce the time complexity.
Federated Learning

Over the past few years, machine learning has revolutionized fields such as computer vision, natural language processing, and speech recognition. Much of this success is based on collecting vast amounts of data, often in privacy-invasive ways. Federated Learning is a new subfield of machine learning that allows training models without collecting the data itself. Instead of sharing data, users collaboratively train a model by only sending weight updates to a server. While this better respects privacy and is more flexible in some situations, it does come at a cost. Naively implementing the concept scales poorly when applied to models with millions of parameters. To make Federated Learning feasible, this thesis proposes changes to the optimization process and explains how dedicated compression methods can be employed. With the use of Differential Privacy techniques, it can be ensured that sending weight updates does not leak significant information about individuals. Furthermore, strategies for additionally personalizing models locally are proposed. To empirically evaluate Federated Learning, a large-scale system was implemented for Mozilla Firefox. 360,000 users helped to train and evaluate a model that aims to improve search results in the Firefox URL bar. Eidesstattliche Erklärung Ich versichere hiermit an Eides Statt, dass diese Arbeit von niemand anderem als meiner Person verfasst worden ist. Alle verwendeten Hilfsmittel wie Berichte, Bücher, Internetseiten oder ähnliches sind im Literaturverzeichnis angegeben, Zitate aus fremden Arbeiten sind als solche kenntlich gemacht. Die Arbeit wurde bisher in gleicher oder ähnlicher Form keiner anderen Prüfungskommission vorgelegt und auch nicht veröffentlicht. Berlin, 20. August 2018
Ukrainian Navigational Aids Network Configuration Estimation

Ukrainian navigational aids network has a long history of deployment and development. Currently, navigational aids network includes ground equipment of Distance Measuring Equipment (DME), Very high-frequency Omni-directional Ranging (VOR), and Non-Directional Beacons (NDB). In our research, we would like to provide an overall study of navigational aids network configuration to specify the impact of particular ground station on the service provided. Common models of navigational aids are considered to estimate navigational aids availability and efficient pair forming in case of positioning by DME/DME and VOR/VOR. An area of correspondence to RNAV requirements is used as a criterion to estimate the impact of each navigational aid on overall network performance. Also, the navigational aids network is represented in the form of a weighted graph to estimate characteristics of centrality and betweenness for ranging network nodes. Configuration of Ukrainian navigational aids network is studied as an example. Obtained results are useful for a better understanding of navigational aids network configuration.
Influence of Aging on Creepage Discharge Characteristics of Oil-Paper Insulation Under AC-DC Combined Voltage

There is a relative lack of research on the development of creepage discharge in aged oil-paper insulation subjected to AC-DC combined voltage in converter transformers. In this study, based on the physical and chemical properties of oil-paper, we propose a sphere-plate electrode model to study the creepage discharge parameters and characteristic spectra of aged oil-paper subjected to AC-DC combined voltage. The results show that the creepage discharge inception voltage of aged oil-paper is related to the AC voltage component of the AC-DC combined voltage. However, when the DC voltage component is 60 kV, the flashover phenomenon was found to occur in one of the severely aged samples before the AC voltage was applied. Despite being subjected to the same DC voltage, the flashover phenomenon did not occur in three other samples aged to different degrees. Large-amplitude discharges were found to appear earlier in the severely aged samples. At the same creepage discharge stage, as the aging time of the oil-paper increased, the number of large-amplitude discharges was found to rapidly increase, and the time interval between adjacent discharges was found to gradually decrease. At each stage of the creepage discharge, the small-amplitude discharge has a “hump” shaped distribution. The larger the DC voltage component, the shorter the endurance time of aged oil-paper, and the more severe the damage to the oil-paper. The DC voltage component was found to have an accelerating effect on the development of creepage discharge in aged oil-paper insulation.
LMI-Based Optimal Linear Quadratic Controller Design for Multiple Solar PV Units Connected to Distribution Networks

In this paper, a state feedback linear quadratic regulator (LQR) controller is designed for multiple PV units which are connected to distribution networks in a networked structure instead of the point of common coupling (PCC). The proposed control scheme is designed for voltage source converters (VSCs) used with different PV units to couple with existing distribution networks. The control inputs are derived using appropriate feedback gains where an linear matrix inequality (LMI)-based LQR scheme is applied to determine these gains in such a way that tracking errors for all states are minimized. The performance of the proposed scheme is evaluated through rigorous simulations under different operating conditions and compared with a conventional proportional integral (PI) controller in terms of the capability to deliver power and minimize total harmonic distortions (THDs) in the inverter output current.
Long-Term Max-Min Fairness Guarantee Mechanism for Integrated Multi-RAT and MEC Networks

Recently, mobile edge computing (MEC) has been recognized as an emerging paradigm to meet the ever-increasing computation demands. Besides, multiple radio access technology (multi-RAT) has been proposed to enhance the network throughput and service reliability. However, the two technologies have been evolving independently. In this paper, we will develop an integrated multi-RAT and MEC network framework, which enables smart devices (SDs) to offload computation tasks over multiple RAT links in parallel. Considering the resource-limited nature as well as the time-varying property of the proposed network, we then focus on investigating long-term fairness guarantee mechanisms to facilitate fair resource sharing/allocation between SDs. Specifically, we formulate a max-min stochastic optimization problem with the objective of maximizing the minimum long-term time-average offloading utility. An adaptive task splitting and resource allocation algorithm is proposed based on the Lyapunov optimization technology, which jointly optimizes the SD task splitting and uplink transmit power allocation, the RAT subcarrier allocation, and the MEC computation frequency allocation. The adaptive algorithm can accommodate to the time-varying network dynamics without requiring their distribution information. Moreover, the proposed algorithm is shown to have polynomial computation complexity and be asymptotically optimal by rigorous analysis. Simulation results verify the theoretical analysis and show that the Jain's fairness index of the proposed algorithm converges to 1 as the number of time-slots grows, which outperforms the benchmark algorithms.
Plagiarism among Thai Students: A Study of Attitudes and Subjective Norms

For Thailand's schools, colleges, and universities, academic misconduct continues to be one of the most significant challenges. Since the start of the COVID pandemic in March 2020 the use of the internet, digital platforms, and eLearning has dramatically increased. This sudden explosion in online learning presents huge challenges in curtailing the temptations for engaging in plagiarism. The internet provides ample opportunities for Thai students to access a wealth of information without visiting a school or university library. While easy access to the internet has led to a boom in searching for information, it has also made it much easier for students to plagiarize content they come across. Most Thai schools, colleges, and universities have created a policy to curb plagiarism to uphold academic integrity. Since Thai students are very weak in English language proficiency there is a tolerance for copying from the internet. This study is based on the Theory of Planned Behavior (TPB) underpinned as a predictive model of academic dishonesty which explains behavior as a final act of logical thinking. Attitudes are the pre-requisites in formulating convictions and normative beliefs which are referred to as subjective norms cultivated from the positive and negative attitudes about plagiarism which students get from society. This study also provides measures to combat plagiarism in Thai institutions.
Deep & Attention: A Self-Attention based Neural Network for Remaining Useful Lifetime Predictions

The remaining useful lifetime (RUL) of assets plays a critical role in machine prognostics and health management (PHM). Accurate RUL predictions can reduce losses caused by equipment faults. Most existing data-driven PHM methods rely on long short-term memory (LSTM) networks to model the relationship of time series data and RUL. However, because of the sequential nature of LSTM, it is not conducive to parallel computing. Herein, we propose the Deep & Attention Network, which uses a combination of convolutional neural networks and Attention methodologies instead of LSTM. In the proposed Deep & Attention Network, the Attention component models the temporal property, while the Deep component learns the effect of noise data. Experiments on NASA's Commercial Modular Aero- Propulsion System Simulation datasets demonstrate that the proposed network achieves a level of performance similar to that of other state-of-the-art RUL prediction models. Moreover, compared with LSTM-based methods, our Self-Attention-based method is conducive to parallel computing.
Identifying the Drivers in the Adoption of Technology in Online Teaching, Learning and Assessment of an Undergraduate Architectural Program

The study aims to examine the behavioral intention (BEI) of faculty members specializing in the field of architecture towards the adoption of online teaching and assessment for architectural courses in architecture schools in the wake of COVID 19. The population targeted for investigation consisted of qualified faculty members duly registered with the Council of Architecture (Statutory Body governing the architectural education system in India) based on their experience in teaching online courses of any kind. The study empirically analyzed and validated performance expectancy (PEX), effort expectancy (EFE), social influence (SOI), facilitating conditions (FCT) and their relationship with Behavioral Intention (BEI) to adopt online teaching and assessment. Data was collected on BEI variables from 102 respondents through online and offline surveys on a five-point Likert scale. The survey questionnaire was developed based on a detailed literature review to identify intentions that guided technology adoption to ensure construct validity. Regression was applied to substantiate multifaceted relationships among constructs. The findings of the study confirmed a significant positive association of PEX, SOI and FCT on BEI, while EFE was insignificant to propel teachers to adopt online teaching. This research will provide an insight into the factors leading to low diffusion and adoption of online teaching and assessment in architectural schools in India. These insights will facilitate universities and educational institutions to focus in the right direction by creating versatile applications along with novel strategies for an effective online teaching-learning outcome.