The overall chain now supports up to a massive 57 simultaneous models across Stomps, Racks, Amps and Cabs. And setup is a breeze - just drag & drop any model into place, to build or re-position even the most complex rigs in seconds.
Analog circuits, especially tube and class-A discrete ones, are not dynamically linear, but have been modeled this way by most companies. So when IK debuted our revolutionary Dynamic Saturation Modeling technology over 10 years ago, it delivered a massive improvement in response and playability and cemented AmpliTube as the leader in realism and accuracy.
Online Reinforcement Learning (RL) is a fast-growing branch of machinelearning with increasingly important applications. Moreover, making RLalgorithms robust against perturbations is essential to their utility in thereal world. Adversarial RL, in which an attacker attempts to degrade anRL agent's performance by perturbing the environment, can be used tounderstand how to robustify RL systems. In this work, we connect an adversarialattack model to streaming algorithms: the victim samples pathsbased on its interactions with the environment, while the adversary corruptsthis stream of data. We construct an attack algorithm in MarkovDecision Processes (MDPs) for a random-sampling victim and prove itsoptimality, in addition to investigating an adversarial strategy against anepsilon-greedy victim with a warm start period. In the epsilon-greedy setting,we bound adversarial corruption and analyze how to exploit this highlyadaptive model to improve upon warm start budget. Experimentally, weshow that our algorithm outperforms baseline attacks, and we generaterandom MDPs to characterize how their general-case structure affects theadversary's ability to maintain its warm start corruption.
A dramatic increase in the number of outbreaks of Dengue has recently been reported, and climate change is likely to extend the geographical spread of the disease. In this context, this paper shows how a neural network approach can incorporate Dengue and COVID-19 data as well as external factors (such as social behaviour or climate variables), to develop predictive models that could improve our knowledge and provide useful tools for health policy makers. Through the use of neural networks with different social and natural parameters, in this paper we define a Correlation Model through which we show that the number of cases of COVID-19 and Dengue have very similar trends. We then illustrate the relevance of our model by extending it to a Long short-term memory model (LSTM) that incorporates both diseases, and using this to estimate Dengue infections via COVID-19 data in countries that lack sufficient Dengue data.
In this paper, we provide a fundamental analysis of the similarities and differences between synchronous and asynchronous distributed systems. Specifically, we define a special and normal adversary such that any protocol for a synchronous system that is resilient to the special adversary can be replicated by a protocol for an asynchronous system that is resilient to the normal adversary. Protocols for the synchronous model are less complex, as the guarantee that messages will be delivered within a bounded time makes it easy to determine the sequence of events in the system. But, this is unrealistic in the real world, as systems tend to be asynchronous where messages are not guaranteed to be delivered in a timely manner. Protocols for the asynchronous model, on the other hand, are more complex as there are many edge cases to account for. Our adversaries help to create intermediary models that allow us to replicate protocol outputs across both synchronous and asynchronous systems, allowing for simpler creation of protocols that remain functional under the asynchronous model.
Recently, transformer networks have enabled breakthroughs in the field of natural language processing. This is partially due to the fact that transformer models can be first trained on a large corpus of unlabeled data prior to fine-tuning on a downstream task. Unlike natural language, which is somewhat tolerant of minor differences in word choices or ordering, the structured nature of programming languages means that program meaning can be completely redefined or be invalid if even one token is altered. In comparison to high-level languages, low-level languages are less expressive and more repetitive with more details from the computer microarchitecture. Whereas recent literature has examined how to effectively use transformer models on high-level programming semantics, this project explores the effectiveness of applying transformer models on low-level representations of programs that can shed light on better optimizing compilers. In this paper, we show that transformer models can translate C to LLVM-IR with high accuracy, by training on a parallel corpus of functions extract from 1 million compilable, open-sourced C programs (AnghaBench) and its corresponding LLVM-IR after compiling with Clang. We also present another case study that analyzes x86_64 basic blocks for estimating their throughput. We discuss various changes in data selection, program representation, network architecture, and other modifications that influence the effectiveness of transformer models on low-level programs.
Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARK)s are used to convince a verifier that a server possesses certain information without revealing these private inputs. Thus, zk-SNARKs can be useful when outsourcing computations for cloud computing. The proofs returned by the server must be less computationally intensive than the given task, but the more complex the task, the more expensive the proof. We present a method that involves model pruning to decrease the complexity of the given task and thus the proof as well, to allow clients to outsource more complex programs. The proposed method harnesses the beneﬁts of producing accurate results using a lower number of constraints, while remaining secure.
In this paper, we introduce a partially synchronous model for distributed systems such that any protocol for our model can be transformed to a corresponding protocol for the asynchronous model. Given a distributed system with $n$ users, we define a normal adversary as one that allows up to $ f (f
A wide variety of digital signature schemes currently exist, from RSA to El-Gamal to Schnorr. More recently, multi-party signature schemes have been developed, including distributed signature schemes and threshold signature schemes. In particular, threshold signature schemes provide useful functionality, in that they require the number of participating parties to pass a threshold in order to generate a valid signature. However, they are limited in their complexity, as they can only model a threshold function. The proposed signature scheme (monotonic signature scheme) allows for the modeling of complex functions, so long as they are monotonic. This would allow for a much greater degree of access control, all while security and correctness are preserved.
During mitosis, DNA changes its physical structure from diffuse chromatin spread throughout the cell nucleus to discrete, compacted, cylindrical chromatids. This process is essential for cells to be able to transfer replicated chromosomes to the daughter nuclei. During interphase, chromatin is compartmentalized into heterochromatin and euchromatin, resulting in a visible signal in Hi-C contact maps. However, as the cell enters mitosis, this signal is disrupted, only to reappear after the cell divides. This paper explores the interphase and mitotic states by modeling DNA using polymer simulations. It is shown that loop extrusion, the mechanism underlying mitotic chromosome formation, can simultaneously be responsible for disrupting compartmentalization.
With the advance of blockchain and cryptocurrency, the need for efficient and practical consensus algorithms is growing. However, most existing works only consider protocols under the synchronous setting. It is usually assumed that there exist at least $h$ users who are always honest and online. This is impractical as honest users might alternate between online and offline states. In this paper, we adapt Byzantine Broadcast protocols to a dynamic synchronous model which features sleepy/offline users as well as information gaps. We do this by building off an approach centered around a Trust Graph, modifying key algorithms from previous works such as the post-processing algorithm to ensure correctness with the dynamic model. This allows the creation of a more fault-tolerant protocol.
The human body provides unique challenges to study from a dynamical perspective, due to its mechanical complexity and the difficulty of obtaining measurements of internal dynamic quantities. Thus, it is essential to create models that both simplify analysis and account for important anatomical details, the two of which must necessarily be balanced into a sufficiently accurate-yet-manageable framework. A number of critical applications require accurate inverse dynamic models of the