Statistical Evidence, Normalcy, and the Gatecrasher Paradox

Martin Smith has recently proposed, in this journal, a novel and intriguing approach to puzzles and paradoxes in evidence law arising from the evidential standard of the Preponderance of the Evidence. According to Smith, the relation of normic support provides us with an elegant solution to those puzzles. In this paper I develop a counterexample to Smith’s approach and argue that normic support can neither account for our reluctance to base affirmative verdicts on bare statistical evidence nor resolve the pertinent paradoxes. Normic support is, as a consequence, not a successful epistemic anti-luck condition

A Simple and Efficient Method to Mitigate the Hot Spot Problem in Wireless Sensor Networks

Much work on wireless sensor networks deals with or considers the hot spot problem, i.e., the problem that the sensor nodes closest to the base station are critical for the lifetime of the sensor network because these nodes need to relay more packet than nodes further away from the base station. Since it is often assumed that sensor nodes will become inexpensive, a simple solution to the hot spot problem is to place additional sensor nodes around the base stations. Using a simple mathematical model we discuss the possible performance gains of adding these supplementary nodes. Our results show that for certain networks only a limited number of additional nodes are required to fourfold network lifetime. We also show that the possible gain depends heavily on the fraction of nodes already present in the vicinity of the base station

Counterexample-Guided Learning of Monotonic Neural Networks

The widespread adoption of deep learning is often attributed to its automatic feature construction with minimal inductive bias. However, in many real-world tasks, the learned function is intended to satisfy domain-specific constraints. We focus on monotonicity constraints, which are common and require that the function's output increases with increasing values of specific input features. We develop a counterexample-guided technique to provably enforce monotonicity constraints at prediction time. Additionally, we propose a technique to use monotonicity as an inductive bias for deep learning. It works by iteratively incorporating monotonicity counterexamples in the learning process. Contrary to prior work in monotonic learning, we target general ReLU neural networks and do not further restrict the hypothesis space. We have implemented these techniques in a tool called COMET. Experiments on real-world datasets demonstrate that our approach achieves state-of-the-art results compared to existing monotonic learners, and can improve the model quality compared to those that were trained without taking monotonicity constraints into account

Modular Timing Constraints for Delay-Insensitive Systems

This paper introduces ARCtimer, a framework for modeling, generating, verifying, and enforcing timing constraints for individual self-timed handshake components. The constraints guarantee that the component’s gate-level circuit implementation obeys the component’s handshake protocol specification. Because the handshake protocols are delayinsensitive, self-timed systems built using ARCtimer-verified components are also delay-insensitive. By carefully considering time locally, we can ignore time globally. ARCtimer comes early in the design process as part of building a library of verified components for later system use. The library also stores static timing analysis (STA) code to validate and enforce the component’s constraints in any self-timed system built using the library. The library descriptions of a handshake component’s circuit, protocol, timing constraints, and STA code are robust to circuit modifications applied later in the design process by technology mapping or layout tools. In addition to presenting new work and discussing related work, this paper identifies critical choices and explains what modular timing verification entails and how it works

Active Queue Management for Fair Resource Allocation in Wireless Networks

This paper investigates the interaction between end-to-end flow control and MAC-layer scheduling on wireless links. We consider a wireless network with multiple users receiving information from a common access point; each user suffers fading, and a scheduler allocates the channel based on channel quality,but subject to fairness and latency considerations. We show that the fairness property of the scheduler is compromised by the transport layer flow control of TCP New Reno. We provide a receiver-side control algorithm, CLAMP, that remedies this situation. CLAMP works at a receiver to control a TCP sender by setting the TCP receiver's advertised window limit, and this allows the scheduler to allocate bandwidth fairly between the users

Fairness in Power Flow Network Congestion Management with Outer Matching and Principal Notions of Fair Division

The problem of network flow congestion occurring in power networks is increasing in severity. Especially in low-voltage networks this is a novel development. The congestion is caused for a large part by distributed and renewable energy sources introducing a complex blend of prosumers to the network. Since congestion management solutions may require individual prosumers to alter their prosumption, the concept of fairness has become a crucial topic of attention. This paper presents a concept of fairness for low-voltage networks that prioritizes local, outer matching and allocates grid access through fair division of available capacity. Specifically, this paper discusses three distinct principal notions of fair division; proportional, egalitarian, and nondiscriminatory division. In addition, this paper devises an efficient algorithmic mechanism that computes such fair allocations in limited computational time, and proves that only egalitarian division results in incentive compatibility of the mechanism

Simple Open-Source Formal Verification of Industrial Programs

Industrial programs written on Programmable Logic Controllers (PLCs) have become an essential component of many modern industries, including automotive, aerospace, manufacturing, infrastructure, and even amusement parks. As these safety-critical systems become larger and more complex, ensuring their continuous error-free operation has become a significant and important challenge. Formal methods are a potential solution to this issue but have traditionally required substantial time and expertise to deploy. This usability issue is compounded by the fact that PLCs are highly proprietary and have substantial licensing costs, making it difficult to learn about or deploy formal methods on them. This thesis presents the OPPP (Open-source Proving of PLC Programs) system as a solution to this usability issue. The OPPP system allows the end-to-end creation and verification of PLC programs from within the development environment. The system is created with an emphasis on being easy to use, with formal constraints presented in English phrases that require no special knowledge to understand. The system uses entirely open-source components, including modified versions of both the OpenPLC development environment and the PLCverif verification platform. The OPPP system is then demonstrated to formalize the requirements of two college-level introductory PLC programming problems. It is further demonstrated to correctly find errors in and verify the correctness of a known good and known bad solution to each problem

Human Capital, Resource Constraints and Intergenerational Fairness

This paper studies an endogenous growth model with human capital, exhaustible resources, and overlapping generations. Under laissez-faire, higher study time reduces depletion rates by increasing the share of re- sources that present generations are willing to sell to successors. However, selfish behavior may prevent competitive sustained growth, and implement- ing utilitarian allocations generally induces optimal-and-sustainable paths. It is shown that: (i) raising study time and decreasing resource depletion are always complementary targets in optimal policies; (ii) growth effects are stronger the lower the optimal share of exploited resources; (iii) gener- ational welfare gains from optimal policies are delayed by faster depletion and, contrary to intuition, anticipated by lower social discount rates.Endogenous Growth, Exhaustible Resources, Human Capital, Over- lapping Generations, Intergenerational Fairness, Sustainability