This paper presents the first hardware implementation of bittide, a decentralized clock synchronization mechanism for achieving logical synchrony in distributed systems. We de-tail the design and implementation of an 8-node bittide network using off-the-shelf FPGA boards and adjustable clock sources. Through experiments with various network topologies, including fully connected, hourglass, and cube, we demonstrate the ef-fectiveness of bittide in aligning node frequencies and bounding buffer excursions. We collect and analyze frequency, buffer oc-cupancy, and logical latency data, validating the hardware's per-formance against theoretical predictions and simulations. Our results show that bittide achieves tight frequency alignment, ro-bustly handles varying physical latencies, and establishes a con-sistent notion of logical time across the network, enabling pre-dictable distributed computation at scale with zero in-band over-head.

The bittide system is a recent approach to distributed computing, designed to achieve synchronous execution at a large scale without the need for a global clock or traditional wall-clock synchronization. It aims to overcome the complexity and expense of maintaining precise wall-clock time in distributed systems, especially at datacenter scale.

Visual encoders have become fundamental components in modern computer vision pipelines. However, ensuring robustness against adversarial perturbations remains a critical challenge. Recent efforts have explored both supervised and unsupervised adversarial fine-tuning strategies. We identify two key limitations in these approaches: (i) they often suffer from instability, especially during the early stages of fine-tuning, resulting in suboptimal convergence and degraded performance on clean data, and (ii) they exhibit a suboptimal trade-off between robustness and clean data accuracy, hindering the simultaneous optimization of both objectives. To overcome these challenges, we propose **L**agrangian-**O**ptimized **R**obust **E**mbeddings (LORE), a novel unsupervised adversarial fine-tuning framework. LORE utilizes constrained optimization, which offers a principled approach to balancing competing goals, such as improving robustness while preserving nominal performance. By enforcing embedding-space proximity constraints, LORE effectively maintains clean data performance throughout adversarial fine-tuning. Extensive experiments show that LORE stabilizes training and significantly improves zero-shot adversarial robustness with minimal degradation in clean data accuracy. Furthermore, we demonstrate the effectiveness of the adversarially fine-tuned image encoder in out-of-distribution generalization and enhancing the interpretability of image embeddings. The code is available on [GitHub](https://github.com/Theborna/LORE-Lagrangian-Optimized-Robust-Embeddings).

We introduce logical synchrony, a framework that allows distributed computing to be coordinated as tightly as in synchronous systems without the distribution of a global clock or any reference to universal time. We develop a model of events called a logical synchrony network, in which nodes correspond to processors and every node has an associated local clock which generates the events. We construct a measure of logical latency and develop its properties. A further model, called a multiclock network, is then analyzed and shown to be a refinement of the logical synchrony network. We present the bittide mechanism as an instantiation of multiclock networks, and discuss the clock control mechanism that ensures that buffers do not overflow or underflow. Finally we give conditions under which a logical synchrony network has an equivalent synchronous realization.

We discuss distributed reframing control of bittide systems. In a bittide system, multiple processors synchronize by monitoring communication over the network. Processors remain in logical synchrony by controlling the timing of frame transmissions. The protocol for doing this relies upon an underlying dynamic control system where each node makes only local observations and performs no direct coordination with other nodes. In this paper we develop a control algorithm based on the idea of buffer centering, which allows all nodes to maintain small buffer offsets while also requiring very little state information. We demonstrate that with buffer centering we can achieve separate control of frequency and phase, allowing frequencies to be syntonized and also buffers to maintain desired offsets rather than combining their control via a proportional-integral controller. The minimalism of this approach offers the potential to simplify both boot processes and failure handling.

We consider a scheduled orthogonal frequency division multiplexed (OFDM) wireless cellular network where the channels from the base-station to the n mobile users undergo flat fading. Spectral resources are to be divided among the users in order to maximize total user utility. We show that this problem can be cast as a nonlinear convex optimization problem, and describe an O(n) algorithm to solve it. Computational experiments show that the algorithm typically converges in around 25 iterations, where each iteration has a cost that is O(n), with a modest constant. When the algorithm starts from an initial resource allocation that is close to optimal, convergence typically takes even fewer iterations. Thus, the algorithm can efficiently track the optimal resource allocation as the channel conditions change due to fading. We also show how our techniques can be extended to solve resource allocation problems that arise in wideband networks with frequency selective fading and when the utility of a user is also a function of the resource allocations in the past.

Consider a legged robot at fixed foot placements. Where can the robot move its center of mass (CM) while remaining in static equilibrium? If the terrain is flat, the CM must lie above the convex hull of the robot's feet. If the terrain is not flat, this often-used approximation can be arbitrarily bad. Instead, the CM must lie above the projection of a nonlinear convex set that is defined by the properties of each foot placement. This paper presents an algorithm to compute the shape of this projection and gives a tight bound on the algorithm's running time. It also presents a method of amortizing the cost of this computation when it is only necessary to test static equilibrium at particular CM positions--that is, when it is only necessary to test the membership of points in the projection of a convex set rather than find its shape.

The general joint design of the physical, MAC, and routing layers to minimize network energy consumption is complex and hard to solve. Heuristics to compute approximate solutions and high-complexity algorithms to compute exact solutions have been previously proposed. In this paper, we focus on synchronous small-scale networks with interference-free link scheduling and practical MQAM link transmission schemes. We show that the cross-layer optimization problems can be closely approximated by convex optimization problems that can be efficiently solved. There are two main contributions of this paper. First of all, we minimize the total network energy that includes both transmission and circuit energy consumptions, where we explore the tradeoff between the two energy elements. Specifically, we use interference-free TDMA as the medium access control scheme. We optimize the routing flow, TDMA slot assignment, and MQAM modulation rate and power on each link. The results demonstrate that the minimum energy transmission scheme is a combination of multihop and single-hop transmissions for general networks; including circuit energy favors transmission schemes with fewer hops. Secondly, based on the solved optimal transmission scheme, we quantify the best trade-off curve between delay and energy consumption, where we derive a scheduling algorithm to minimize the worst-case packet delay.

A legged robot walking on uneven terrain can avoid falling only by applying contact forces with its feet on the ground that compensate for gravity without causing slip. To plan safe motions, it is necessary to test this constraint at every posture explored at each set of foot placements. Since a huge number of postures may be explored, this test must be as fast as possible. Existing approaches either search explicitly for contact forces at each posture, or precompute the support polygon and check that the robot's center of mass lies above it. This paper presents a new algorithm that is faster than either existing approach. This algorithm is an incremental method of projection, that computes only enough of the support polygon to decide whether static equilibrium is possible at each posture. It takes advantage of information gained testing previous postures in order to test subsequent postures more quickly

We consider a sensor network in which each sensor takes measurements, at various times, of some unknown parameters, corrupted by independent Gaussian noises. Each node can take a finite or infinite number of measurements, at arbitrary times (ie, asynchronously). We propose a space-time diffusion scheme, that relies only on peer-to-peer communication, and allows every node to asymptotically compute the global maximum-likelihood estimate of the unknown parameters. At each iteration, information is diffused across the network by a temporal update step and a spatial update step. Both steps update each node's state by a weighted average of its current value and locally available data: new measurements for the time update, and neighbors' data for the spatial update. At any time, any node can compute a local weighted least-squares estimate of the unknown parameters, which converges to the global maximum-likelihood solution. With an infinite number of measurements, these estimates converge to the true parameter values in the sense of mean-square convergence. We show that this scheme is robust to unreliable communication links, and works in a network with dynamically changing topology.

We consider the joint optimal design of the physical, medium access control (MAC), and routing layers to maximize the lifetime of energy-constrained wireless sensor networks. The problem of computing lifetime-optimal routing flow, link schedule, and link transmission powers for all active time slots is formulated as a non-linear optimization problem. We first restrict the link schedules to the class of interference-free time division multiple access (TDMA) schedules. In this special case, we formulate the optimization problem as a mixed integerconvex program, which can be solved using standard techniques. Moreover, when the slots lengths are variable, the optimization problem is convex and can be solved efficiently and exactly using interior point methods. For general non-orthogonal link schedules, we propose an iterative algorithm that alternates between adaptive link scheduling and computation of optimal link rates and transmission powers for a fixed link schedule. The performance of this algorithm is compared to other design approaches for several network topologies. The results illustrate the advantages of load balancing, multihop routing, frequency reuse, and interference mitigation in increasing the lifetime of energy-constrained networks. We also briefly discuss computational approaches to extend this algorithm to large networks

A sensor network of nodes with wireless transceiver capabilities and limited energy is considered. We propose distributed algorithms to compute an optimal routing scheme that maximizes the time at which the first node in the network drains out of energy. The problem is formulated as a linear programming problem and subgradient algorithms are used to solve it in a distributed manner. The resulting algorithms have low computational complexity and are guaranteed to converge to an optimal routing scheme that maximizes the network lifetime. The algorithms are illustrated by an example in which an optimal flow is computed for a network of randomly distributed nodes. We also show how our approach can be used to obtain distributed algorithms for many different extensions to the problem. Finally, we extend our problem formulation to more general definitions of network lifetime to model realistic scenarios in sensor networks

We consider sensor networks where energy is a limited resource so that energy consumption must be minimized while satisfying given throughput requirements. Moreover, energy consumption must take into account both the transmission energy and the circuit processing energy for short-range communications. We emphasize that the energy efficiency must be supported across all layers of the protocol stack through a cross-layer design. In this context, we analyze energy-efficient joint routing, scheduling, and link adaptation strategies that maximize the network lifetime. We propose variable-length TDMA schemes where the slot length is optimally assigned according to the routing requirement while minimizing the energy consumption across the network. We show that the optimization problems can be transformed into or approximated by convex problems that can be efficiently solved using known techniques. The results show that multihop routing schemes are more energy-efficient when only transmission energy is considered, but single-hop transmissions may be more efficient when the circuit processing energy is considered.

We consider a wireless sensor network where the nodes have limited energy. We first analyze the delay performance of a transmission scheme based on time division multiple access (TDMA). We propose a simple link scheduling algorithm to find the minimum-delay schedule given the slot lengths for all the links. We then combine these results with our previous work on energy-optimal cross-layer design to minimize the delay in transferring a fixed number of bits from the source nodes to the sink, in an energy-constrained manner. We also study the tradeoff between the total energy consumption and delay. Pareto optimal energy-delay curves are computed by solving a series of convex optimization problems where each objective function is a weighted sum of the delay and the total energy consumption. The computation is done for networks with and without link adaptation capabilities.

We consider the joint optimal design of physical, medium access control (MAC), and routing layers to maximize the lifetime of energy-constrained wireless sensor networks. The problem of computing a lifetime-optimal routing flow, link schedule, and link transmission powers is formulated as a non-linear optimization problem. We first restrict the link schedules to the class of interference-free time division multiple access (TDMA) schedules. In this special case we formulate the optimization problem as a mixed integer-convex program, which can be solved using standard techniques. For general non-orthogonal link schedules, we propose an iterative algorithm that alternates between adaptive link scheduling and computation of optimal link rates and transmission powers for a fixed link schedule. The performance of this algorithm is compared to other design approaches for several network topologies. The results illustrate the advantages of load balancing, multihop routing, frequency reuse, and interference mitigation in increasing the lifetime of energy-constrained networks. We also describe a partially distributed algorithm to compute optimal rates and transmission powers for a given link schedule.

We consider a network of distributed sensors, where where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximum-likelihood estimate of the parameters. This scheme doesn't involve explicit point-to-point message passing or routing; instead, it diffuses information across the network by updating each node's data with a weighted average of its neighbors' data (they maintain the same data structure). At each step, every node can compute a local weighted least-squares estimate, which converges to the global maximum-likelihood solution. This scheme is robust to unreliable communication links. We show that it works in a network with dynamically changing topology, provided that the infinitely occurring communication graphs are jointly connected.

A sensor network of nodes with wireless transceiver capabilities and limited energy is considered. We propose distributed algorithms to compute an optimal routing scheme that maximizes the time at which the first node in the network drains out of energy. The problem is formulated as a linear programming problem and subgradient algorithms are used to solve it in a distributed manner. The resulting algorithms have low computational complexity and are guaranteed to converge to an optimal routing scheme that maximizes the network lifetime. The algorithms are illustrated by an example in which an optimal flow is computed for a network of randomly distributed nodes.