Technology AI Texas Instruments' newest calculator is intentionally dumb The $160 device is not powered by AI, won't send annoying notifications, and can't connect to Wi-Fi. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The new TI-84 keeps the good old-fashioned physical buttons. Breakthroughs, discoveries, and DIY tips sent six days a week. In a world drowning in notifications and devices that want to be everything all at once, calculator giant Texas Instruments (TI) is going back to basics.

Aleksandra Mendoza, aka Alorah Ziva, alleges that the 20-year-old influencer injected her with drugs on a livestream and had nonconsensual sex with her while she was underage. An 18-year-old woman who promotes herself as the "#1 female looksmaxxer" is suing the highly controversial streamer Braden Eric Peters, aka Clavicular, for fraud, battery, and alleged sexual assault. In the suit, which was filed in Miami-Dade County court and obtained by WIRED, Aleksandra Mendoza, who goes by the name @zahloria, or Alorah Ziva, on Instagram, alleges that she first encountered Peters in May 2025, when she was just 16 years old. According to the complaint, Peters promised Mendoza he could make her "the female face of looksmaxxing," the online trend of using surgery or drugs to enhance one's facial features. Eager to grow her social media following, Mendoza agreed to make four looksmaxxing videos for Peters in exchange for a $1,000 payment, court documents say.

Within a single sniff, the mammalian olfactory system can decode the identity and concentration of odorants wafted on turbulent plumes of air. Yet, it must do so given access only to the noisy, dimensionally-reduced representation of the odor world provided by olfactory receptor neurons. As a result, the olfactory system must solve a compressed sensing problem, relying on the fact that only a handful of the millions of possible odorants are present in a given scene. Inspired by this principle, past works have proposed normative compressed sensing models for olfactory decoding. However, these models have not captured the unique anatomy and physiology of the olfactory bulb, nor have they shown that sensing can be achieved within the 100-millisecond timescale of a single sniff. Here, we propose a rate-based Poisson compressed sensing circuit model for the olfactory bulb.

Domain adaptation aims to transfer the knowledge learned on (data-rich) source domains to (low-resource) target domains, and a popular method is invariant representation learning, which matches and aligns the data distributions on the feature space. Although this method is studied extensively and applied on classification and regression problems, its adoption on ranking problems is sporadic, and the few existing implementations lack theoretical justifications. This paper revisits invariant representation learning for ranking. Upon reviewing prior work, we found that they implement what we call item-level alignment, which aligns the distributions of the items being ranked from all lists in aggregate but ignores their list structure. However, the list structure should be leveraged, because it is intrinsic to ranking problems where the data and the metrics are defined and computed on lists, not the items by themselves. To close this discrepancy, we propose list-level alignment--learning domain-invariant representations at the higher level of lists. The benefits are twofold: it leads to the first domain adaptation generalization bound for ranking, in turn providing theoretical support for the proposed method, and it achieves better empirical transfer performance for unsupervised domain adaptation on ranking tasks, including passage reranking.

Standard conformal prediction methods provide a marginal coverage guarantee, which means that for a random test point, the conformal prediction set contains the true label with a user-specified probability. In many classification problems, we would like to obtain a stronger guarantee--that for test points of a specific class, the prediction set contains the true label with the same user-chosen probability. For the latter goal, existing conformal prediction methods do not work well when there is a limited amount of labeled data per class, as is often the case in real applications where the number of classes is large. We propose a method called clustered conformal prediction that clusters together classes having "similar" conformal scores and performs conformal prediction at the cluster level. Based on empirical evaluation across four image data sets with many (up to 1000) classes, we find that clustered conformal typically outperforms existing methods in terms of classconditional coverage and set size metrics.

We show the theoretical energy consumption estimation method of the proposed Spike-driven Transformer in Table 1 of the main text. Compared to the vanilla Transformer counterpart, the spiking version requires information on timesteps T and spike firing rates (R). Therefore, we only need to evaluate the FLOPs of the vanilla Transformer, and T and R are known, we can get the theoretical energy consumption of spike-driven Transformer. FLConv = (kn)2 hn wn cn 1 cn, (S1) where kn is the kernel size, (hn,wn) is the output feature map size, cn 1 and cn are the input and output channel numbers, respectively. The FLOPs of the m-th MLP layer in ANNs are: FLMLP = im om, (S2) where im and om are the input and output dimensions of the MLP layer, respectively.