People Used to Control Machines. In a world regulated by devices, humanity has become disconnected from the physical world--from stick-shift cars to postcards. If gratification is so easy, why don't you feel more gratified already? It's still easy to experience individual feats of gratification when you find them (or they find you). But the ordinary circumstances that once produced so much gratification have gradually receded. Unseen choices in design, business, and social life have made it harder for you to engage directly with the sensory world.

PCWorld tested numerous USB-C cables using a Treedix tester and found half performed poorly, with many offering only basic USB 2.0 data or limited power delivery despite appearing high-quality. Testing revealed significant performance variations between cables, helping identify which ones support fast charging speeds like 100W-240W and high-speed data transfer capabilities. The audit process helped declutter cable collections, improve charging efficiency, and promote eco-friendly disposal of unusable cables through proper recycling. USB-C has been one of the best innovations for convenience and compatibility, but it also means that my bag of cables and plugs is now a tangled mess of confusion. I don't know which ones are for data, which are for charging, or how fast each one is.

Neural Information Processing Systems http://nips.cc/

Motivated by neuroscientific and clinical applications, we empirically examine whether observational measures of information flow can suggest interventions. We do so by performing experiments on artificial neural networks in the context of fairness in machine learning, where the goal is to induce fairness in the system through interventions. Using our recently developed M-information flow framework, we measure the flow of information about the true label (responsible for accuracy, and hence desirable), and separately, the flow of information about a protected attribute (responsible for bias, and hence undesirable) on the edges of a trained neural network. We then compare the flow magnitudes against the effect of intervening on those edges by pruning. We show that pruning edges that carry larger information flows about the protected attribute reduces bias at the output to a greater extent. This demonstrates that M-information flow can meaningfully suggest targets for interventions, answering the title's question in the affirmative. We also evaluate bias-accuracy tradeoffs for different intervention strategies, to analyze how one might use estimates of desirable and undesirable information flows (here, accuracy and bias flows) to inform interventions that preserve the former while reducing the latter.

We study downlink beam and rate adaptation in a multi-user mmWave MISO system where multiple base stations (BSs), each using analog beamforming from finite codebooks, serve multiple single-antenna user equipments (UEs) with a unique beam per UE and discrete data transmission rates. BSs learn about transmission success based on ACK/NACK feedback. To encode service goals, we introduce a satisficing throughput threshold $τ_r$ and cast joint beam and rate adaptation as a combinatorial semi-bandit over beam-rate tuples. Within this framework, we propose SAT-CTS, a lightweight, threshold-aware policy that blends conservative confidence estimates with posterior sampling, steering learning toward meeting $τ_r$ rather than merely maximizing. Our main theoretical contribution provides the first finite-time regret bounds for combinatorial semi-bandits with satisficing objective: when $τ_r$ is realizable, we upper bound the cumulative satisficing regret to the target with a time-independent constant, and when $τ_r$ is non-realizable, we show that SAT-CTS incurs only a finite expected transient outside committed CTS rounds, after which its regret is governed by the sum of the regret contributions of restarted CTS rounds, yielding an $O((\log T)^2)$ standard regret bound. On the practical side, we evaluate the performance via cumulative satisficing regret to $τ_r$ alongside standard regret and fairness. Experiments with time-varying sparse multipath channels show that SAT-CTS consistently reduces satisficing regret and maintains competitive standard regret, while achieving favorable average throughput and fairness across users, indicating that feedback-efficient learning can equitably allocate beams and rates to meet QoS targets without channel state knowledge.

We study the Combinatorial Group Testing (CGT) problems in a novel game-theoretic framework, with a solution concept of Adversarial Equilibrium (AE).

The experimental data is used to train complex-weighted models with a range of regularisation approaches.

Neural Information Processing Systems http://nips.cc/