Search for: All records

Invariant stimulus recognition is a challenging pattern-recognition problem that must be dealt with by all sensory systems. Since neural responses evoked by a stimulus are perturbed in a multitude of ways, how can this computational capability be achieved? We examine this issue in the locust olfactory system. We find that locusts trained in an appetitive-conditioning assay robustly recognize the trained odorant independent of variations in stimulus durations, dynamics, or history, or changes in background and ambient conditions. However, individual- and population-level neural responses vary unpredictably with many of these variations. Our results indicate that linear statistical decoding schemes, which assign positive weights to ON neurons and negative weights to OFF neurons, resolve this apparent confound between neural variability and behavioral stability. Furthermore, simplification of the decoder using only ternary weights ({+1, 0, −1}) (i.e., an “ON-minus-OFF” approach) does not compromise performance, thereby striking a fine balance between simplicity and robustness. 

We consider the problem of synthesizing good-enough (GE)-strategies for linear temporal logic (LTL) over finite traces or LTLf for short.The problem of synthesizing GE-strategies for an LTL formula φ over infinite traces reduces to the problem of synthesizing winning strategies for the formula (∃Oφ)⇒φ where O is the set of propositions controlled by the system.We first prove that this reduction does not work for LTLf formulas.Then we show how to synthesize GE-strategies for LTLf formulas via the Good-Enough (GE)-synthesis of LTL formulas.Unfortunately, this requires to construct deterministic parity automata on infinite words, which is computationally expensive.We then show how to synthesize GE-strategies for LTLf formulas by a reduction to solving games played on deterministic Büchi automata, based on an easier construction of deterministic automata on finite words.We show empirically that our specialized synthesis algorithm for GE-strategies outperforms the algorithms going through GE-synthesis of LTL formulas by orders of magnitude.

 

Emtricitabine (FTC) and lamivudine (3TC), containing an oxathiolane ring with unnatural (−)-stereochemistry, are widely used nucleoside reverse transcriptase inhibitors (NRTIs) in anti-HIV therapy. Treatment with FTC or 3TC primarily selects for the HIV-1 RT M184V/I resistance mutations. Here we provide a comprehensive kinetic and structural basis for inhibiting HIV-1 RT by (−)-FTC-TP and (−)-3TC-TP and drug resistance by M184V. (−)-FTC-TP and (−)-3TC-TP have higher binding affinities (1/K_d) for wild-type RT but slower incorporation rates than dCTP. HIV-1 RT ternary crystal structures with (−)-FTC-TP and (−)-3TC-TP corroborate kinetic results demonstrating that their oxathiolane sulfur orients toward the DNA primer 3′-terminus and their triphosphate exists in two different binding conformations. M184V RT displays greater (>200-fold)K_dfor theL-nucleotides and moderately higher (>9-fold)K_dfor theD-isomers compared to dCTP. The M184V RT structure illustrates how the mutation repositions the oxathiolane of (−)-FTC-TP and shifts its triphosphate into a non-productive conformation.

 

Thermoelectric (TE) materials can interconvert waste heat into electricity, which will become alternative energy sources in the future. The high-entropy alloys (HEAs) as a new class of materials are well-known for some excellent properties, such as high friction toughness, excellent fatigue resistance, and corrosion resistance. Here, we present a series of HEAs to be potential candidates for the thermoelectric materials. The thermoelectric properties of YxCoCrFeNi, GdxCoCrFeNiCu, and annealed Al0.3CoCrFeNi were investigated. The effects of grain size and formation of the second phase on thermoelectric properties were revealed. In HEAs, we can reduce the thermal conductivity by controlling the phonon scattering due to the considerable complexity of the alloys. The Y, Gd-doped HEAs are competitive candidate thermoelectric materials for energy conversion in the future. 

Recommendation for e-commerce with a mix of durable and nondurable goods has characteristics that distinguish it from the well-studied media recommendation problem. The demand for items is a combined effect of form utility and time utility, i.e., a product must both be intrinsically appealing to a consumer and the time must be right for purchase. In particular for durable goods, time utility is a function of inter-purchase duration within product category because consumers are unlikely to purchase two items in the same category in close temporal succession. Moreover, purchase data, in contrast to rating data, is implicit with non-purchases not necessarily indicating dislike. Together, these issues give rise to the positive-unlabeled demand-aware recommendation problem that we pose via joint low-rank tensor completion and product category inter-purchase duration vector estimation. We further relax this problem and propose a highly scalable alternating minimization approach with which we can solve problems with millions of users and millions of items in a single thread. We also show superior prediction accuracies on multiple real-world datasets.