Miracle-dz/UniFAR-FTUs huggingface dataset:
FTIR-spektroskopische Untersuchungen zum Aktivierungsmechanismus von bovinem und humanem Rhodopsin

Rhodopsin, which consists of the apoprotein opsin and its covalently bound ligand, is used as a model system to understand the activation mechanism of the large family of G protein coupled receptors (GPCRs). As a result of a photochemical reaction, rhodopsin undergoes activating structural changes, enabling it to bind the G protein and transmitting the stimulus to intracellular signaling pathways. In the first part of this work, site-directed mutants of bovine rhodopsin were produced, incorporated into an artificial lipid environment, and studied mainly by FTIR spectroscopy. The translocation of the transient Schiff base counterion (Glu181) during the activation process was determined using the Y191Fand Y192F-mutants. The interactions of Tyr206 contributed to the coupled movement of EL2 and TM5, which was shown by Y206F-mutant. A striking accumulation of methionines on the cytoplasmic side of the receptor was observed to be a key-player for the activating outward motion of TM6. In the second and primary part of this work, human rhodopsin, which has been rarely studied, was compared with the bovine receptor. Starting from various dark states, it was shown that the activation mechanisms of both receptors diverge from each other and yet ultimately converge in the formation of the active species. By analyzing the amino acid sequences of mammalian rhodopsins, two regions of high variability were identified, which provide the molecular basis for these discrepancies. This finding was verified by the investigation of human/bovine rhodopsin chimeras. In addition to this study, ovine rhodopsin was compared with both the bovine and human forms. It showed, as another naturally occurring variant of the light receptor, an independent pathway of activation.
Mean Waiting Time in Large-Scale and Critically Loaded Power of d Load Balancing Systems

Mean field models are a popular tool used to analyse load balancing policies. In some exceptional cases the waiting time distribution of the mean field limit has an explicit form. In other cases it can be computed as the solution of a differential equation or a recursive relation. In our paper [6] we study the limit of the mean waiting time E[W&955;] as the arrival rate &955; approaches 1 for a number of load balancing policies when job sizes are exponential with mean 1 (i.e. when the system gets close to instability). As E[W_&955;] diverges to infinity, we scale with -log(1-&955;) and present a method to compute the limit lim&955;-> 1- -E[W&955;]/log(1-&955;). We show that this limit has a surprisingly simple form for the load balancing algorithms considered. In addition, we propose an alternate scaling -log(p&955;) instead of -log(1-&955;), where p&955; is adapted to the policy at hand. This scaling allows us to obtain good approximations of the mean waiting time when &955; is close to one or zero.
Functional Differences in the Neural Substrates of Auditory Cognition as a Consequence of Music Training

Previous studies have demonstrated that musical deviants (syntactically irregular chords) elicit event related potentials/fields with negative polarity; specifically, the early right anterior negativity and the right anterior temporal negativity responses with peak latencies at ~200 ms and ~350 ms, respectively, post stimulus onset. Here, we investigated differences in the neural dynamics of the auditory perceptual system of individuals with music training compared to those with no music training. Magnetoencephalography was used to examine the neural response to a deviant sound when the auditory system was primed using stimulus entrainment to evoke an auditory gamma-band response between 31 Hz and 39 Hz, in 2 Hz steps. Participants responded to the harmonic relationship between the entrainment stimulus and the subsequent target stimulus. Gamma frequencies carry stimulus information; thus, the paradigm primed the auditory system with a known gamma frequency and evaluated any improvement in the brains response to a deviant stimulus. The entrainment stimuli did not elicit an early right anterior negativity response. Furthermore, the source location of the event-related field difference during the later right anterior temporal negativity response time-window varied depending on group and entrainment condition. In support of previous findings from research using this, and a functionally similar visual-priming paradigm, a 7 Hz phase modulation of gamma amplitude was found for non-musicians following 33 Hz stimulus entrainment. Overall, significant effects of gamma entrainment were found more frequently in the non-music brain. By contrast, musicians demonstrated a greater range of interactions with slower brain rhythms, indicative of increased top-down control. Graphical Abstract Conclusion Musicians’ auditory cognition relies more on top-down processes, while non-musicians rely on bottom-up processing, and therefore, their auditory cognition is facilitated by the entrained gamma-band response. First neural evidence in support of faster reaction-time responses due to an interaction in phase of an entrained gamma-band response of 33 Hz and a slower endogenous theta rhythm (~7 Hz) - consistently reported in previous research.
Dual control of MAPK activities by AP2C1 and MKP1 MAPK phosphatases regulates defence responses in Arabidopsis

Mitogen-activated protein kinase (MAPK) cascades transmit environmental signals and induce stress and defence responses in plants. These signalling cascades are negatively controlled by specific phosphatases of the type 2C Ser/Thr protein phosphatase (PP2C) and dual-specificity phosphatase (DSP) families that inactivate stress-induced MAPKs; however, the interplay between phosphatases of these different types has remained unknown. Our work reveals that different Arabidopsis MAPK phosphatases, the PP2C-type AP2C1 and the DSP-type MKP1, exhibit both specific and overlapping functions in plant stress responses. Each single mutant and ap2c1 mkp1 double mutant displayed enhanced wound-induced activation of MAPKs MPK3, MPK4, and MPK6, as well as induction of a set of transcription factors. Moreover, ap2c1 mkp1 double mutants show an autoimmune-like response, associated with elevated levels the stress hormones salicylic acid and ethylene, and of the phytoalexin camalexin. Interestingly, this phenotype is reduced in ap2c1 mkp1 mpk6 triple mutants, suggesting that the autoimmune-like response is due to MPK6 misregulation. We conclude that the evolutionarily distant MAPK phosphatases AP2C1 and MKP1 contribute crucially to the tight control of MPK6 activity, ensuring appropriately balanced stress signalling and suppression of autoimmune-like responses during plant growth and development. Highlight Double MAPK phosphatase mutant plants ap2c1 mkp1 exhibit constitutive, autoimmune-like stress responses, dependent on their substrate MAPK MPK6.
Manta: a Plug and Play Private DeFi Stack

We propose Manta , a plug and play private DeFi stack that consists of Manta DAP , a multi-asset decentralized anonymous payment scheme and Manta DAX , an automated market maker (AMM) based decentralized anonymous exchange scheme. Compared with existing privacy preserving cryptocurrencies such as Zcash and Monero, Manta supports multiple base assets and allows the priva-tized assets to be exchanged anonymously via Manta DAX . We think this is a major step forward towards building a privacy preserving DeFi stack. Thanks to the eﬃciency of modern NIZKs (non-interactive zero-knowledge proof systems) and our carefully crafted design, Manta is eﬃcient: our benchmarks reports a 15 second, oﬀ-line zero-knowledge proof (ZKP) generation time, and a 6 millisecond, on-line proof veriﬁcation time.
Multidimentional ModDiv public key exchange protocol

This paper presents Multidimentional ModDiv public key exchange protocol which security is based on the hardness of an LWR problem instance consisting on ﬁnding a secret vector X in Z nq knowing vectors A and B respectively in Z mp and Z m − n p − q , where elements of vector B are deﬁned as follows : B ( i ) = ( (cid:80) j = n j =1 A ( i + n − j ) × X ( j )) Mod ( P ) Div ( Q ). Mod is integer modulo, Div is integer division, P and Q are known positive integers which sizes in bits are respectively p and q which satisfy p > 2 × q . m and n satisfy m > 2 × n .
Anytime Heuristic and Monte Carlo Methods for Large-Scale Simultaneous Coalition Structure Generation and Assignment

Optimal simultaneous coalition structure generation and assignment is computationally hard. The state-of-the-art can only compute solutions to problems with severely limited input sizes, and no effective approximation algorithms that are guaranteed to yield high-quality solutions are expected to exist. Real-world optimization problems, however, are often characterized by large-scale inputs and the need for generating feasible solutions of high quality in limited time. In light of this, and to make it possible to generate better feasible solutions for difficult large-scale problems efficiently, we present and benchmark several different anytime algorithms that use general-purpose heuristics and Monte Carlo techniques to guide search. We evaluate our methods using synthetic problem sets of varying distribution and complexity. Our results show that the presented algorithms are superior to previous methods at quickly generating near-optimal solutions for small-scale problems, and greatly superior for efficiently finding high-quality solutions for large-scale problems. For example, for problems with a thousand agents and values generated with a uniform distribution, our best approach generates solutions 99.5% of the expected optimal within seconds. For these problems, the state-of-the-art solvers fail to find any feasible solutions at all.
SoK: Privacy-Preserving Computing in the Blockchain Era

Privacy is a huge concern for cryptocurrencies and blockchains as most of these systems log everything in the clear. This has resulted in several academic and industrial initiatives to address privacy. Starting with the UTXO model of Bitcoin, initial works brought confidentiality and anonymity to payments. Recent works have expanded to support more generalized forms of private computation. Such solutions tend to be highly involved as they rely on advanced cryptographic primitives and creative techniques to handle issues related to dealing with private records (e.g. concurrency and double spending). This situation makes it hard to comprehend the current state-of-the-art, much less build on top of it. To address these challenges, we develop a systematization of knowledge for privacy-preserving solutions in blockchain. To the best of our knowledge, our work is the first of its kind. After motivating design challenges, we devise two systematization frameworks-the first as a stepping stone to the second- and use them to study the state-of-the-art. For our first framework, we study the zero-knowledge proof systems used in surveyed solutions, based on their key features and limitations. Our second is for privacy-preserving solutions; we define several dimensions to categorize the surveyed schemes and, in doing so, identify two major paradigms employed to achieve private computation. We go on to provide insights to guide solutions' adoption and development. Finally, we touch upon challenges related to limited functionality and accommodating new developments.
Unified Tensor Framework for Incomplete Multi-view Clustering and Missing-view Inferring

In this paper, we propose a novel method, referred to as incomplete multi-view tensor spectral clustering with missing-view inferring (IMVTSC-MVI) to address the challenging multi-view clustering problem with missing views. Different from the existing methods which commonly focus on exploring the certain information of the available views while ignoring both of the hidden information of the missing views and the intra-view information of data, IMVTSC-MVI seeks to recover the missing views and explore the full information of such recovered views and available views for data clustering. In particular, IMVTSC-MVI incorporates the feature space based missing-view inferring and manifold space based similarity graph learning into a unified framework. In such a way, IMVTSC-MVI allows these two learning tasks to facilitate each other and can well explore the hidden information of the missing views. Moreover, IMVTSC-MVI introduces the low-rank tensor constraint to capture the high-order correlations of multiple views. Experimental results on several datasets demonstrate the effectiveness of IMVTSC-MVI for incomplete multi-view clustering.
High-Speed NTT-based Polynomial Multiplication Accelerator for CRYSTALS-Kyber Post-Quantum Cryptography

This paper demonstrates an architecture for accelerating the polynomial multiplication using number theoretic transform (NTT). Ky-ber is one of the ﬁnalists in the third round of the NIST post-quantum cryptography standardization process. Simultaneously, the performance of NTT execution is its main challenge, requiring large memory and complex memory access pattern. In this paper, an eﬃcient NTT architecture is presented to improve the respective computation time. We propose several optimization strategies for eﬃciency improvement targeting different performance requirements for various applications. Our NTT architecture, including four butterﬂy cores, occupies only 798 LUTs and 715 FFs on a small Artix-7 FPGA, showing more than 44% improvement compared to the best previous work. We also implement a coprocessor architecture for Kyber KEM beneﬁting from our high-speed NTT core to accomplish three phases of the key exchange in 9, 12, and 19 µ s, respectively, operating at 200 MHz.
Tri-op redactable blockchains with block modification, removal, and insertion

: In distributed computations and cryptography, it is desirable to record events on a public ledger, such that later alterations are computationally infeasible. An implementation of this idea is called blockchain , which is a distributed protocol that allows the creation of an immutable ledger. While such an idea is very appealing, the ledger may be contaminated with incorrect, illegal, or even dangerous data, and everyone running the blockchain protocol has no option but to store and propagate the unwanted data. The ledger is bloated over time, and it is not possible to remove redundant information. Finally, missing data cannot be inserted later. Redactable blockchains were invented to allow the ledger to be mutated in a controlled manner. To date, redactable blockchains support at most two types of redactions: block modification and removal. The next logical step is to support block insertions. However, we show that this seemingly innocuous enhancement renders all previous constructs insecure. We put forward a model for blockchains supporting all three redaction operations and construct a blockchain that is provably secure under this formal definition.
Generative Semi-supervised Learning for Multivariate Time Series Imputation

The missing values, widely existed in multivariate time series data, hinder the effective data analysis. Existing time series imputation methods do not make full use of the label information in real-life time series data. In this paper, we propose a novel semi-supervised generative adversarial network model, named SSGAN, for missing value imputation in multivariate time series data. It consists of three players, i.e., a generator, a discriminator, and a classifier. The classifier predicts labels of time series data, and thus it drives the generator to estimate the missing values (or components), conditioned on observed components and data labels at the same time. We introduce a temporal reminder matrix to help the discriminator better distinguish the observed components from the imputed ones. Moreover, we theoretically prove that, SSGAN using the temporal reminder matrix and the classifier does learn to estimate missing values converging to the true data distribution when the Nash equilibrium is achieved. Extensive experiments on three public real-world datasets demonstrate that, SSGAN yields a more than 15% gain in performance, compared with the state-of-the-art methods.
Breaking CAS-Lock and Its Variants by Exploiting Structural Traces

Logic locking is a prominent solution to protect against design intellectual property theft. However, there has been a decade-long cat-and-mouse game between defenses and attacks. A turning point in logic locking was the development of miterbased Boolean satisfiability (SAT) attack that steered the research in the direction of developing SAT-resilient schemes. These schemes, however achieved SAT resilience at the cost of low output corruption. Recently, cascaded locking (CAS-Lock) [SXTF20a] was proposed that provides non-trivial output corruption all-the-while maintaining resilience to the SAT attack. Regardless of the theoretical properties, we revisit some of the assumptions made about its implementation, especially about security-unaware synthesis tools, and subsequently expose a set of structural vulnerabilities that can be exploited to break these schemes. We propose our attacks on baseline CAS-Lock as well as mirrored CAS (M-CAS), an improved version of CAS-Lock. We furnish extensive simulation results of our attacks on ISCAS’85 and ITC’99 benchmarks, where we show that CAS-Lock/M-CAS can be broken with ∼94% success rate. Further, we open-source all implementation scripts, locked circuits, and attack scripts for the community. Finally, we discuss the pitfalls of point function-based locking techniques including Anti-SAT [XS18] and Stripped Functionality Logic Locking(SFLL-HD) [YSN+17], which suffer from similar implementation issues.
Training Spiking Neural Networks with Accumulated Spiking Flow

The fast development of neuromorphic hardwares promotes Spiking Neural Networks (SNNs) to a thrilling research avenue. Current SNNs, though much efficient, are less effective compared with leading Artificial Neural Networks (ANNs) especially in supervised learning tasks. Recent efforts further demonstrate the potential of SNNs in supervised learning by introducing approximated backpropagation (BP) methods. To deal with the non-differentiable spike function in SNNs, these BP methods utilize information from the spatio-temporal domain to adjust the model parameters. With the increasing of time window and network size, the computational complexity of spatio-temporal backpropagation augments dramatically. In this paper, we propose a new backpropagation method for SNNs based on the accumulated spiking flow (ASF), i.e. ASF-BP. In the proposed ASF-BP method, updating parameters does not rely on the spike train of spiking neurons but leverage accumulated inputs and outputs of spiking neurons over the time window, which reduces the BP complexity significantly. We further present an adaptive linear estimation model to approach the dynamic characteristics of spiking neurons statistically. Experimental results demonstrate that with our proposed ASF-BP method, light-weight convolutional SNNs achieve superior performances compared with other spike-based BP methods on both non-neuromorphic (MNIST, CIFAR10) and neuromorphic (CIFAR10-DVS) datasets. The code is available at https://github.com/neural-lab/ASF-BP.
Cloud Data Services: Workloads, Architectures and Multi-Tenancy

Enterprises are moving their business critical workloads to public clouds at an accelerating pace. Cloud data services for Online Transaction Processing (OLTP), Data Analytics and NoSQL are essential building blocks for enterprise applications. Multi-tenancy is a crucial tenet for cloud data service providers that allows sharing of data center resources across tenants, thereby reducing cost. In this article we review architectures of today’s cloud data services and identify trends and challenges that arise in multi-tenant cloud data services. We survey techniques that have been developed for enabling elasticity, providing SLAs, ensuring performance isolation and reducing cost. We review the emerging paradigm of serverless databases and point out opportunities and challenges. We identify open research problems in the fast-changing landscape of cloud data services.
Stochastic Graphical Bandits with Adversarial Corruptions

We study bandits with graph-structured feedback, where a learner repeatedly selects an arm and then observes rewards of the chosen arm as well as its neighbors in the feedback graph. Existing work on graphical bandits assumes either stochastic rewards or adversarial rewards, both of which are extremes and appear rarely in real-world scenarios. In this paper, we study graphical bandits with a reward model that interpolates between the two extremes, where the rewards are overall stochastically generated but a small fraction of them can be adversarially corrupted. For this problem, we propose an online algorithm that can utilize the stochastic pattern and also tolerate the adversarial corruptions. The main idea is to restrict exploration to carefully-designed independent sets of the feedback graph and perform exploitation by adopting a soft version of arm elimination. Theoretical analysis shows that our algorithm attains an $O(\alpha \ln{K} \ln{T} + \alpha C)$ regret, where $\alpha$ is the independence number of the feedback graph, $K$ is the number of arms, $T$ is the time horizon, and $C$ quantifies the total corruptions introduced by the adversary. The effectiveness of our algorithm is demonstrated by numerical experiments.
Question-Driven Span Labeling Model for Aspect-Opinion Pair Extraction

Aspect term extraction and opinion word extraction are two fundamental subtasks of aspect-based sentiment analysis. The internal relationship between aspect terms and opinion words is typically ignored, and information for the decision-making of buyers and sellers is insufficient. In this paper, we explore an aspect–opinion pair extraction (AOPE) task and propose a Question-Driven Span Labeling (QDSL) model to extract all the aspect–opinion pairs from user-generated reviews. Specifically, we divide the AOPE task into aspect term extraction (ATE) and aspect-specified opinion extraction (ASOE) subtasks; we first extract all the candidate aspect terms and then the corresponding opinion words given the aspect term. Unlike existing approaches that use the BIO-based tagging scheme for extraction, the QDSL model adopts a span-based tagging scheme and builds a question–answer-based machine-reading comprehension task for an effective aspect–opinion pair extraction. Extensive experiments conducted on three tasks (ATE, ASOE, and AOPE) on four benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches.
On the Design and Misuse of Microcoded (Embedded) Processors - A Cautionary Note

Today’s microprocessors often rely on microcode updates to address issues such as security or functional patches. Unfortunately, microcode update flexibility opens up new attack vectors through malicious microcode alterations. Such attacks share many features with hardware Trojans and have similar devastating consequences for system security. However, due to microcode’s opaque nature, little is known in the open literature about the capabilities and limitations of microcode Trojans. We introduce the design of a microcoded RISC-V processor architecture together with a microcode development and evaluation environment. Even though microcode typically has almost complete control of the processor hardware, the design of meaningful microcode Trojans is not straightforward. This somewhat counter-intuitive insight is due to the lack of information at the hardware level about the semantics of executed software. In three security case studies we demonstrate how to overcome these issues and give insights on how to design meaningful microcode Trojans that undermine system security. To foster future research and applications, we publicly release our implementation and evaluation platform1.