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Seidel switching is a classical operation on graphs which plays a central role in the theory of two-graphs, signed graphs, and switching classes. In this paper we focus on those switches which leave a given graph invariant up to isomorphism. We call such subsets of the vertex set \emph{identity Seidel switches}. After recalling basic properties of Seidel switching and the associated abelian group structure, we introduce Seidel equivalence classes of graphs and then study the structure of the family of identity Seidel switches of a fixed graph. We show that this family forms a 14 pages; 2--group under composition, and we obtain structural constraints on graphs in which many vertices or edges give rise to identity switches. In particular, we derive necessary conditions in terms of degree parameters, and we characterize certain edge-identity switches via an automorphism of an induced subgraph. Several constructions and examples are presented, and some open problems are proposed.

We investigate the dynamical aspects of the quantum switch and find a particular form of quantum memory emerging out of the switch action. We first analyze the loss of information in a general quantum evolution subjected to a quantum switch and propose a measure to quantify the switch-induced memory. We then derive an uncertainty relation between information loss and switch-induced memory. We explicitly consider the example of depolarizing dynamics and show how it is affected by the action of a quantum switch. For a more detailed analysis, we consider both the control qubit and the final measurement on the control qubit as noisy and investigate the said uncertainty relation. Further, while deriving the Lindblad-type dynamics for the reduced operation of the switch action, we identify that the switch-induced memory actually leads to the emergence of non-Markovianity. Interestingly, we demonstrate that the emergent non-Markovianity can be explicitly attributed to the switch operation by comparing it with other standard measures of non-Markovianity. Our investigation thus paves the way forward to understanding the quantum switch as an emerging non-Markovian quantum memory.

We study switching current distributions in superconducting nanostrips using theoretical models and numerical simulations. Switching current distributions are commonly measured in experiments and may provide a window into the microscopic switching mechanisms. As the current through a superconducting strip is increased from zero it will at some point switch to the normal dissipative state. Due to thermal and/or quantum fluctuations the switching current will be random and follow a certain distribution depending on sweep rate, temperature, material properties and geometry. By analyzing the resulting distribution it is possible to infer the transition rate for a switch, which can be related to the free energy barrier separating the metastable superconducting state and the normal one. We study different switching scenarios and show using simulations how data taken for different sweep rates can be combined to obtain the switching rate over a wider interval of currents.

With a growing number of quantum networks in operation, there is a pressing need for performance analysis of quantum switching technologies. A quantum switch establishes, distributes, and maintains entanglements across a network. In contrast to a classical switching fabric, a quantum switch is a two sided queueing network. The switch generates Link Level Entanglements (LLEs), which are then fused to process the networks entanglement requests. Our proof techniques analyse a two time scale separation phenomenon at the fluid scale for a general switch topology. This allows us to demonstrate that the optimal fluid dynamics are given by a scheduling algorithm that solves a certain average reward Markov Decision Process.

Sub-threshold swing (S) defines the sharpness of ON-OFF switching of a Field Effect Transistor (FET) with S=0 corresponding to abrupt switching characteristics. While thermodynamics dictates S to be greater than or equal to 60mV/dec for classical FETs, "Landau switches" use inherently unstable gate insulators to achieve abrupt switching. Unfortunately, S=0 switching is always achieved at the expense of an intrinsic hysteresis, making these switches unsuitable for low-power applications. The fundamental question therefore remains: Under what condition, hysteresis-free abrupt switching can be achieved in a Landau switch? In this paper, we first provide an intuitive classification of all charge based switches in terms of their energy landscapes and identify two-well energy landscape as the characteristic feature of Landau switches. We then use nanoelectromechanical field effect transistor (NEMFET) as an illustrative example of a Landau switch and conclude that a flat energy landscape is essential for hysteresis-free abrupt switching. In contrast, a hysteresis-free smooth sub-60mV/dec switching is obtained by stabilizing the unstable gate insulator in its unstable regime. Our conclusio

A quantum switch is a superoperator that, in general, creates a superposition of various causal orders of two or more quantum dynamics that are all divisible in the complete positivity (CP) sense. We introduce a process that we term as the universal quantum switch (UQS), which unlike conventional quantum switches, allows for the construction of a quantum switch that can superpose different causal orders of any set of quantum dynamics, regardless of their CP-divisibility. Our approach also enables the construction of a quantum switch while considering a single environment connected with the system, in contrast to the traditional one. Moreover, we show the UQS provides more advantages in performance for a certain state discrimination task compared to traditional quantum switches. The next question that we address is the following: What is the CP-divisibility characteristic of a dynamics built by acting a quantum switch on CP-divisible or -indivisible dynamics? In this regard, an example is presented where the dynamics created by the action of the UQS on two CP-indivisible dynamics is CP-indivisible. Additionally, we prove a necessary and sufficient condition for the channel created b

Compute Express Link (CXL) switch allows memory extension via PCIe physical layer to address increasing demand for larger memory capacities in data centers. However, CXL attached memory introduces 170ns to 400ns memory latency. This becomes a significant performance bottleneck for applications that host data in persistent memory as all updates, after traversing the CXL switch, must reach persistent domain to ensure crash consistent updates. We make a case for persistent CXL switch to persist updates as soon as they reach the switch and hence significantly reduce latency of persisting data. To enable this, we presented a system independent persistent buffer (PB) design that ensures data persistency at CXL switch. Our PB design provides 12\% speedup, on average, over volatile CXL switch. Our \textit{read forwarding} optimization improves speedup to 15\%.

Why do bilingual speakers code-switch (mix their two languages)? Among the several theories that attempt to explain this natural and ubiquitous phenomenon, the Triggering Hypothesis relates code-switching to the presence of lexical triggers, specifically cognates and proper names, adjacent to the switch point. We provide a fuller, more nuanced and refined exploration of the triggering hypothesis, based on five large datasets in three language pairs, reflecting both spoken and written bilingual interactions. Our results show that words that are assumed to reside in a mental lexicon shared by both languages indeed trigger code-switching; that the tendency to switch depends on the distance of the trigger from the switch point; and on whether the trigger precedes or succeeds the switch; but not on the etymology of the trigger words. We thus provide strong, robust, evidence-based confirmation to several hypotheses on the relationships between lexical triggers and code-switching.

Hybrid circuit and packet switching for data center networking (DCN) has received considerable research attention recently. A hybrid-switched DCN employs a much faster circuit switch that is reconfigurable with a nontrivial cost, and a much slower packet switch that is reconfigurable with no cost, to interconnect its racks of servers. The research problem is, given a traffic demand matrix (between the racks), how to compute a good circuit switch configuration schedule so that the vast majority of the traffic demand is removed by the circuit switch, leaving a remaining demand matrix that contains only small elements for the packet switch to handle. In this paper, we propose two new hybrid switch scheduling algorithms under two different scheduling constraints. Our first algorithm, called 2-hop Eclipse, strikes a much better tradeoff between the resulting performance (of the hybrid switch) and the computational complexity (of the algorithm) than the state of the art solution Eclipse/Eclipse++. Our second algorithm, called BFF (best first fit), is the first hybrid switching solution that exploits the potential partial reconfiguration capability of the circuit switch for performance ga

In a generic switching problem, a switching pattern consists of a one-to-one mapping from a set of inputs to a set of outputs (i.e., a permutation). We propose and investigate a wireless switching framework in which a multi-antenna relay is responsible for switching traffic among a set of $N$ stations. We refer to such a relay as a MIMO switch. With beamforming and linear detection, the MIMO switch controls which stations are connected to which stations. Each beamforming matrix realizes a permutation pattern among the stations. We refer to the corresponding permutation matrix as a switch matrix. By scheduling a set of different switch matrices, full connectivity among the stations can be established. In this paper, we focus on "fair switching" in which equal amounts of traffic are to be delivered for all $N(N-1)$ ordered pairs of stations. In particular, we investigate how the system throughput can be maximized. In general, for large $N$ the number of possible switch matrices (i.e., permutations) is huge, making the scheduling problem combinatorially challenging. We show that for N=4 and 5, only a subset of $N-1$ switch matrices need to be considered in the scheduling problem to ac

In a generic switching problem, a switching pattern consists of a one-to-one mapping from a set of inputs to a set of outputs (i.e., a permutation). We propose and investigate a wireless switching framework in which a multi-antenna relay is responsible for switching traffic among a set of $N$ stations. We refer to such a relay as a MIMO switch. With beamforming and linear detection, the MIMO switch controls which stations are connected to which other stations. Each beamforming matrix realizes a permutation pattern among the stations. We refer to the corresponding permutation matrix as a switch matrix. By scheduling a set of different switch matrices, full connectivity among the stations can be established. In this paper, we focus on "fair switching" in which equal amounts of traffic are to be delivered for all $N(N-1)$ ordered pairs of stations. In particular, we investigate how the system throughput can be maximized. In general, for large $N$ the number of possible switch matrices (i.e., permutations) is huge, making the scheduling problem combinatorially challenging. We show that for the cases of N=4 and 5, only a subset of $N-1$ switch matrices need to be considered in the sched

Tangible control interfaces (TCIs), such as appliance panels, remotes, elevators, and embedded GUIs, are a fundamental component of everyday human-built environments. Interacting with these interfaces requires agents not only to ground language in visual observations,but also to execute actions, track temporally evolving state changes, and verify whether intended outcomes have been achieved. However, existing benchmarks predominantly evaluate open-loop perception or single-step action execution, failing to capture this continuous cycle of interaction, feedback, and correction. We introduce SWITCH, a benchmark for closed-loop interactive reasoning with TCIs in realistic egocentric environments1. SWITCH comprises 1,170 temporally interactive videos across diverse functional categories, providing structured annotations of instructions, actions, state transitions, outcomes, and recovery behaviors over time. To probe generative world modeling, SWITCH also evaluates video generation models on interaction-centered tasks using both LLM-as-judge and human evaluation2.Experiments with frontier proprietary and opensource multimodal models reveal persistent weaknesses in fine-grained visual-te

We report an electrically actuated, low-loss non-volatile optical switch based on the phase-change material (PCM) Sb2Se3 integrated on a silicon nitride (Si3N4) platform. The device is fabricated using an 8-inch wafer-scale process flow, demonstrating the feasibility of scalable manufacturing for photonic integrated circuits (PICs). By employing transparent indium tin oxide (ITO) micro-heaters, reversible switching between the amorphous and crystalline states is achieved with an extinction ratio of 25~dB and an endurance exceeding 140 million switching cycles, establishing a new benchmark for non-volatile integrated photonic memory and reconfigurable architectures. Furthermore, multi-level operation beyond 6 bits can be repeatably demonstrated by tailoring the electrical pulse widths, enabling precise control of the optical phase. These results highlight a scalable and energy-efficient platform for high-density programmable and non-volatile photonic integrated systems.

The switch process alternates independently between 1 and -1, with the first switch to 1 occurring at the origin. The expected value function of this process is defined uniquely by the distribution of switching times. The relation between the two is implicitly described through the Laplace transform, which is difficult to use for determining if a given function is the expected value function of some switch process. We derive an explicit relation under the assumption of monotonicity of the expected value function. It is shown that geometric divisible switching time distributions correspond to a non-negative decreasing expected value function. Moreover, an explicit relation between the switching time distribution and the autocovariance function of the switch process stationary counterpart is obtained, which allows parallel results for the stationary switch process and its covariance function. These results are applicable to approximation methods in statistical physics, and an example is presented.

We study a quantum switch that distributes maximally entangled multipartite states to sets of users. The entanglement switching process requires two steps: first, each user attempts to generate bipartite entanglement between itself and the switch; and second, the switch performs local operations and a measurement to create multipartite entanglement for a set of users. In this work, we study a simple variant of this system, wherein the switch has infinite memory and the links that connect the users to the switch are identical. Further, we assume that all quantum states, if generated successfully, have perfect fidelity and that decoherence is negligible. This problem formulation is of interest to several distributed quantum applications, while the technical aspects of this work result in new contributions within queueing theory. Via extensive use of Lyapunov functions, we derive necessary and sufficient conditions for the stability of the system and closed-form expressions for the switch capacity and the expected number of qubits in memory.

We study a quantum entanglement distribution switch serving a set of users in a star topology with equal-length links. The quantum switch, much like a quantum repeater, can perform entanglement swapping to extend entanglement across longer distances. Additionally, the switch is equipped with entanglement switching logic, enabling it to implement switching policies to better serve the needs of the network. In this work, the function of the switch is to create bipartite or tripartite entangled states among users at the highest possible rates at a fixed ratio. Using Markov chains, we model a set of randomized switching policies. Discovering that some are better than others, we present analytical results for the case where the switch stores one qubit per user, and find that the best policies outperform a time division multiplexing (TDM) policy for sharing the switch between bipartite and tripartite state generation. This performance improvement decreases as the number of users grows. The model is easily augmented to study the capacity region in the presence of quantum state decoherence and associated cut-off times for qubit storage, obtaining similar results. Moreover, decoherence-asso

Instead of looking at the lengths of synchronizing words as in Černý's conjecture, we look at the switch count of such words, that is, we only count the switches from one letter to another. Where the synchronizing words of the Černý automata $\mathcal{C}_n$ have switch count linear in $n$, we wonder whether synchronizing automata exist for which every synchronizing word has quadratic switch count. The answer is positive: we prove that switch count has the same complexity as synchronizing word length. We give some series of synchronizing automata yielding quadratic switch count, the best one reaching $\frac{2}{3} n^2 + O(n)$ as switch count. We investigate all binary automata on at most 9 states and determine the maximal possible switch count. For all $3\leq n\leq 9$, a strictly higher switch count can be reached by allowing more symbols. This behaviour differs from length, where for every $n$, no automata are known with higher synchronization length than $\mathcal{C}_n$, which has only two symbols. It is not clear if this pattern extends to larger $n$. For $n\geq 12$, our best construction only has two symbols.

The asymmetric switch process is a binary stochastic process that alternates between the values one and minus one, where the distributions of the time in these states may differ. Two versions of the process are considered: a non-stationary version that starts with a switch at time zero and a stationary one constructed from the non-stationary one. Characteristics of these two processes, such as the expected values and covariance, are investigated. The main results show an equivalence between the monotonicity of the expected value functions and the distribution of the intervals having a stochastic representation in the form of a sum of random variables, where the number of terms follows a geometric distribution. This representation has a natural interpretation as a model in which switching attempts may fail at random. From these results, conditions are derived when these characteristics lead to valid interval distributions, which is vital in applications.

Dynamic algorithm selection aims to exploit the complementarity of multiple optimization algorithms by switching between them during the search. While these kinds of dynamic algorithms have been shown to have potential to outperform their component algorithms, it is still unclear how this potential can best be realized. One promising approach is to make use of landscape features to enable a per-run trajectory-based switch. Here, the samples seen by the first algorithm are used to create a set of features which describe the landscape from the perspective of the algorithm. These features are then used to predict what algorithm to switch to. In this work, we extend this per-run trajectory-based approach to consider a wide variety of potential points at which to perform the switch. We show that using a sliding window to capture the local landscape features contains information which can be used to predict whether a switch at that point would be beneficial to future performance. By analyzing the resulting models, we identify what features are most important to these predictions. Finally, by evaluating the importance of features and comparing these values between multiple algorithms, we