peer reviewed

The notion of duality – that a given physical system can have two different mathematical descriptions – is a key idea in modern theoretical physics. Establishing a duality in lattice statistical mechanics models requires the construction of a dual Hamiltonian and a map from the original to the dual observables. By using neural networks to parameterize these maps and introducing a loss function that penalises the difference between correlation functions in original and dual models, we formulate the process of duality discovery as an optimization problem. We numerically solve this problem and show that our framework can rediscover the celebrated Kramers-Wannier duality for the 2d Ising model, numerically reconstructing the known mapping of temperatures. We further investigate the 2d Ising model deformed by a plaquette coupling and find families of “approximate duals”. We discuss future directions and prospects for discovering new dualities within this framework.

Global cooperation on climate change mitigation is essential to limit temperature increases while supporting long-term, equitable economic growth and sustainable development. Achieving such cooperation among diverse regions, each with different incentives, in a dynamic environment shaped by complex geopolitical and economic factors, without a central authority, is a profoundly challenging game-theoretic problem. This article introduces RICE-N, a multi-region integrated assessment model that simulates the global climate, economy, and climate negotiations and agreements. RICE-N uses multi-agent reinforcement learning (MARL) to encourage agents to develop strategic behaviors based on the environmental dynamics and the actions of the others. We present two negotiation protocols: (1) Bilateral Negotiation, an exemplary protocol and (2) Basic Club, inspired from Climate Clubs and the carbon border adjustment mechanism (Nordhaus, 2015; Comissions, 2022). We compare their impact against a no-negotiation baseline with various mitigation strategies, showing that both protocols significantly reduce temperature growth at the cost of a minor drop in production while ensuring a more equitable distribution of the emission reduction costs.

peer reviewed

Gateway courses play a key role in student success.These courses often control access to advanced coursework and can delay graduation if students struggle to pass them.Identifying such courses is important for curriculum design and student support.However, existing methods rely on heuristics or past grades and often overlook complex patterns in student enrollment data.In this paper, we propose a tensor decomposition-based method to identify gateway courses in a systematic way.We model studentcourse-semester interactions as a three-dimensional tensor.This captures relationships between students, courses, and time.Using PARAFAC tensor decomposition, we extract latent factors that represent course importance based on their structure in the data.Our analysis shows that this method highlights courses that are central, or distinctive in the curriculum.Courses with high latent factor magnitudes are likely to represent gateway courses or critical prerequisites.As future work, we plan to combine tensor decomposition with learning techniques.This could enable predictive models and provide insights for curriculum design.Our goal is to develop a complete framework to analyze educational data, and support institutions in improving student outcomes.

This paper defines a reusable framework called Imperative Code Execution Machine (ICEM) which enables developers to implement application logic using code stored in the form of plain text and evade the necessity of its compilation, even at runtime. ICEM aims to reduce amount of apex code, facilitate rapid development while adopting evolving business requirements, maintenance, testing and eventually shrink Time to Market (TTM) for a given application or functionality over Salesforce platform. The framework has potential to mitigate the challenges that arise with the practice of agile for development of applications with convoluted business logic by shifting the focus from “developing” a solution to “designing” a solution.

Deep Metric Learning (DML) is arguably one of the most influential lines of research for learning visual similarities with many proposed approaches every year. Although the field benefits from the rapid progress, the divergence in training protocols, architectures, and parameter choices make an unbiased comparison difficult. To provide a consistent reference point, we revisit the most widely used DML objective functions and conduct a study of the crucial parameter choices as well as the commonly neglected mini-batch sampling process. Under consistent comparison, DML objectives show much higher saturation than indicated by literature. Further based on our analysis, we uncover a correlation between the embedding space density and compression to the generalization performance of DML models. Exploiting these insights, we propose a simple, yet effective, training regularization to reliably boost the performance of ranking-based DML models on various standard benchmark datasets. Code and a publicly accessible WandB-repo are available at https://github.com/Confusezius/Revisiting_Deep_Metric_Learning_PyTorch.

A recent Graph Neural Network (GNN) approach for learning to branch has been shown to successfully reduce the running time of branch-and-bound algorithms for Mixed Integer Linear Programming (MILP). While the GNN relies on a GPU for inference, MILP solvers are purely CPU-based. This severely limits its application as many practitioners may not have access to high-end GPUs. In this work, we ask two key questions. First, in a more realistic setting where only a CPU is available, is the GNN model still competitive? Second, can we devise an alternate computationally inexpensive model that retains the predictive power of the GNN architecture? We answer the first question in the negative, and address the second question by proposing a new hybrid architecture for efficient branching on CPU machines. The proposed architecture combines the expressive power of GNNs with computationally inexpensive multi-layer perceptrons (MLP) for branching. We evaluate our methods on four classes of MILP problems, and show that they lead to up to 26% reduction in solver running time compared to state-of-the-art methods without a GPU, while extrapolating to harder problems than it was trained on. The code for this project is publicly available at https://github.com/pg2455/Hybrid-learn2branch.

MOTIVATION: Facilitated by technological advances and expeditious decrease in the sequencing costs, whole-genome sequencing is increasingly implemented to uncover variations in cultivars/accessions of many crop plants. In tomato (Solanum lycopersicum), the availability of the genome sequence, followed by the resequencing of tomato cultivars and its wild relatives, has provided a prodigious resource for the improvement of traits. A high-quality genome resequencing of 84 tomato accessions and wild relatives generated a dataset that can be used as a resource to identify agronomically important alleles across the genome. Converting this dataset into a searchable database, including information about the influence of single-nucleotide polymorphisms (SNPs) on protein function, provides valuable information about the genetic variations. The database will assist in searching for functional variants of a gene for introgression into tomato cultivars. RESULTS: A recent release of better-quality tomato genome reference assembly SL3.0, and new annotation ITAG3.2 of SL3.0, dropped 3857 genes, added 4900 novel genes and updated 20 766 genes. Using the above version, we remapped the data from the tomato lines resequenced under the '100 tomato genome resequencing project' on new tomato genome assembly SL3.0 and made an online searchable Tomato Genomic Variations (TGVs) database. The TGV contains information about SNPs and insertion/deletion events and expands it by functional annotation of variants with new ITAG3.2 using SIFT4G software. This database with search function assists in inferring the influence of SNPs on the function of a target gene. This database can be used for selecting SNPs, which can be potentially deployed for improving tomato traits. AVAILABILITY AND IMPLEMENTATION: TGV is freely available at http://psd.uohyd.ac.in/tgv.

Enormous increase in process variations (due to progressive CMOS technology scaling) along-with the temperature and supply voltage variations are severely degrading the fabrication outcome of digital circuits i.e. circuits are not accomplishing the specification bounds of the required performances. Therefore, process and operating variations aware optimization has become a very essential task in VLSI design. Moreover, many specifications in a circuit have challenging trade-offs, hence demand effective optimization skills. With this vision, this paper presents optimization algorithm based robust transistor sizing for various nanoscale CMOS digital circuits. The objective is to minimize the static i.e. leakage power without degrading the operating frequency (i.e. keeping the propagation delays in bound) and area. The reported results are shown for 32nm CMOS Metal gate High-k model parameters, however methodology is equally valid for further scaled technology nodes. The Overall reduction in leakage power obtained is up to 88% keeping bound on the critical path delay. The temperature range and supply voltage has been taken between -55°C to +125°C (for automotive applications) and 0.90V to 1.10V (±10% variations) respectively at 3-sigma design.

Workload and resource management are two essential functions provided at the service level of the distributed systems infrastructure. To improve the global throughput of these software environments, workloads have to be evenly scheduled among the available resources. To realize this goal, several load balancing strategies and algorithms have been proposed. Most o f t h e s e strategies were developed assuming homogeneous set of sites linked with homogeneous and fast networks. However, for computational grids, we must address some new issues, namely: heterogeneity, scalability and adaptability. In this paper, we propose a decentralized cluster-based algorithm which achieves dynamic load balancing in the cloud architecture. The proposed algorithm presents the following main features: (i) it supports heterogeneity, (ii) scalability, (iii) low network congestion and (iv) absence of any bottleneck node due to its decentralized nature. Simulation results using CloudSim show the performance analysis of the algorithm for patronizing our claims about the load balancing achieved in the system.

The transmission of voice communications as datagram packets over IP networks, commonly known as voice-over-IP (VoIP) telephony, is rapidly gaining wide acceptance. With private phone conversations being conducted on insecure public networks, security of VoIP communications is increasingly important. We present a structured security analysis of the VoIP protocol stack, which consists of signaling (SIP), session description (SDP), key establishment (SDES, MIKEY, and ZRTP) and secure media transport (SRTP) protocols. Using a combination of manual and tool-supported formal analysis, we uncover several design flaws and attacks, most of which are caused by subtle inconsistencies between the assumptions that protocols at different layers of the VoIP stack make about each other. The most serious attack is a replay attack on SDES, which causes SRTP to repeat the keystream used for media encryption, thus completely breaking transport-layer security. We also demonstrate a man-in-the-middle attack on ZRTP, which allows the attacker to convince the communicating parties that they have lost their shared secret. If they are using VoIP devices without displays and thus cannot execute the "human authentication" procedure, they are forced to communicate insecurely, or not communicate at all, i.e., this becomes a denial of service attack. Finally, we show that the key derivation process used in MIKEY cannot be used to prove security of the derived key in the standard cryptographic model for secure key exchange.

Buffer overflow vulnerabilities are one of the most commonly and widely exploited security vulnerabilities in programs. Most existing solutions for avoiding buffer overflows are either inadequate, inefficient or incompatible with existing code. In this paper, we present a novel approach for transparent and efficient runtime protection against buffer overflows. The approach is implemented by two tools: Type Information Extractor and Depositor (TIED) and LibsafePlus. TIED is first used on a binary executable or shared library file to extract type information from the debugging information inserted in the file by the compiler and reinsert it in the file as a data structure available at runtime. LibsafePlus is a shared library that is preloaded when the program is run. LibsafePlus intercepts unsafe C library calls such as strcpy and uses the type information made available by TIED at runtime to determine whether it would be ‘safe’ to carry out the operation. With our simple design we are able to protect most applications with a performance overhead of less than 10%. Copyright © 2006 John Wiley & Sons, Ltd.